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Hospital Harm – Falls with Injury

CBE ID
4120e
1.4 Project
Management of Acute Events, Chronic Disease, Surgery, and Behavioral Health
Endorsement Status
Endorsed
1.1 New or Maintenance
New
Previous Endorsement Cycle
Fall 2023
Is Under Review
No
Next Maintenance Cycle
Fall 2028
1.3 Measure Description

This ratio measure assesses the number of inpatient hospitalizations where at least one fall with a major or moderate injury occurs among the total qualifying inpatient hospital days for patients aged 18 years and older.

    • 1.5 Measure Type
      Outcome
      1.6 Composite Measure
      No
      1.7 Electronic Clinical Quality Measure (eCQM)
      Yes
      1.8 Level Of Analysis
      Facility
      1.9 Care Setting
      Hospital: Inpatient
      1.10 Measure Rationale

      N/A this is not a paired measure. 

      A website URL not available; Final measure specifications for implementation will be made publicly available on CMS’ appropriate quality website, once finalized through the CBE endorsement and CMS rulemaking processes. 

      1.11 Measure Webpage
      http://notavailable.com/seerationaleabove
      1.20 Testing Data Sources
      Electronic Health Records
      1.25 Data Sources

      Hospitals collect EHR data using certified electronic health record technology (CEHRT). The MAT output, which includes the human readable and XML artifacts of the clinical quality language (CQL) for the measure are contained in the eCQM specifications attached. No additional tools are used for data collection for eCQMs.

    • 1.14 Numerator

      Inpatient hospitalizations where the patient has a fall that results in moderate or major injury.

      The diagnosis of a fall and of a moderate or major injury must not be present on admission.

       

      Measure observation associated with the Numerator: The total number of inpatient hospitalizations where a fall with moderate or major injury occurred, across all eligible encounters.

      1.14a Numerator Details

      The numerator is inpatient hospitalizations where the patient has a fall that results in moderate or major injury. The diagnosis of a fall and of a moderate or major injury must not be present on admission.

      Examples of moderate injuries include lacerations, open wounds, dislocations, sprains, and muscle strains.

      Examples of major injuries include fractures, closed head injuries, and internal bleeding.

       

      The time period for data collection is during an inpatient hospitalization, which are defined as beginning at hospital arrival including time in the emergency department or observation when the transition between these encounters (if they exist) and the inpatient encounter are within an hour or less of each other.

       

      All data elements necessary to calculate this numerator are defined within value sets available in the Value Set Authority Center (VSAC) and listed below:

      • Fall diagnoses are represented by the value set Inpatient Falls (2.16.840.1.113762.1.4.1147.171)
      • Moderate injury diagnoses are represented by the value set Moderate Injuries (2.16.840.1.113762.1.4.1248.205)
      • Major injury diagnoses are represented by the value set Major Injuries (2.16.840.1.113762.1.4.1147.120)
      • The not present on admission indicators are represented by the value set Not Present On Admission or Documentation Insufficient to Determine (2.16.840.1.113762.1.4.1147.198)

       

      To access the value sets for the measure, please visit the Value Set Authority Center (VSAC), sponsored by the National Library of Medicine, at https://vsac.nlm.nih.gov/.

       

      The measure observation associated with the numerator is the total number of inpatient hospitalizations where a fall with moderate or major injury occurred, across all eligible encounters.

    • 1.15 Denominator

      Inpatient hospitalizations for patients aged 18 and older with a length of stay less than or equal to 120 days that ends during the measurement period. 

       

      Inpatient hospitalizations where the patient has a fall diagnosis present on admission are excluded from the denominator.

       

      Measure observation associated with the Denominator: The total number of eligible days across all encounters which match the initial population/denominator criteria.

      1.15a Denominator Details

      This measure includes all inpatient hospitalizations with a length of stay less than or equal to 120 days ending during the measurement period for patients aged 18 years and older at the time of admission, and all payers.  The time period for data collection is inpatient hospitalizations, which are defined as beginning at hospital arrival and including time in the emergency department and observation when the transition between these encounters (if they exist) and the inpatient encounter are within an hour or less of each other.

       

      Measurement period is one year. This measure is at the hospital-by-admission encounter level.

       

      All data elements necessary to calculate this denominator are defined within value sets available in the Value Set Authority Center (VSAC) and listed below:

      • Inpatient encounters are represented using the value set of Encounter Inpatient (2.16.840.1.113883.3.666.5.307)
      • Emergency department visits are represented using the value set of Emergency Department Visit (2.16.840.1.113883.3.117.1.7.1.292)
      • Observation encounters are represented using the value set of Observation Services (2.16.840.1.113762.1.4.1111.143)

       

      To access the value sets for the measure, please visit the Value Set Authority Center (VSAC), sponsored by the National Library of Medicine, at https://vsac.nlm.nih.gov/.

       

      The measure observation associated with the denominator is the total number of eligible days across all encounters which match the initial population/denominator criteria.

    • 1.15b Denominator Exclusions

      Inpatient hospitalizations where the patient has a fall diagnosis present on admission. 

      1.15c Denominator Exclusions Details

      The denominator exclusion is inpatient hospitalizations where the patient has a fall diagnosis present on admission. 

      The time period for data collection is during an inpatient hospitalization, which is defined as beginning at hospital arrival including time in the emergency department or observation when the transition between these encounters (if they exist) and the inpatient encounter are within an hour or less of each other.

       

      All data elements necessary to calculate this numerator are defined within value sets available in the Value Set Authority Center (VSAC) and listed below:

      • Fall diagnoses are represented by the value set Inpatient Falls (2.16.840.1.113762.1.4.1147.171)
      • The present on admission indicators are represented by the value set Present on Admission or Clinically Undetermined (2.16.840.1.113762.1.4.1147.197)

       

      To access the value sets for the measure, please visit the Value Set Authority Center (VSAC), sponsored by the National Library of Medicine, at https://vsac.nlm.nih.gov/. 

    • Most Recent Endorsement Activity
      Management of Acute Events, Chronic Disease, Surgery, and Behavioral Health Fall 2023
      Last Updated
    • Steward
      Centers for Medicare & Medicaid Services
      Steward Organization POC Email
      [email protected]
      Steward Organization URL
      https://www.cms.gov/
      Steward Organization Copyright

      N/A

      Measure Developer Secondary Point Of Contact

      Anna Michie
      American Institutes for Research (AIR)
      1400 Crystal Drive
      10th Floor
      Arlington, VA 22202
      United States

    • 2.1 Attach Logic Model
      Hospital Harm – Falls with Injury_Logic Model and Tables_11 01 2023.pdf
      2.2 Evidence of Measure Importance

      This eCQM captures the number of patients who experience harm in the form of major and moderate injuries during their inpatient hospitalization. Inpatient falls are among the most common incidents reported in hospitals and can increase length of stay and patient costs. Falls (including unplanned or unintended descents to the floor) can result in patient injury ranging from minor abrasion or bruising to death.

       

      Despite reductions in rates of inpatient falls with injury in recent years, these events remain common in the United States. It has been estimated that there are 700,000-1,000,000 inpatient falls in the U.S. annually, with more than one-third resulting in injury and up to 11,000 resulting in patient death (AHRQ, 2019; Currie, 2008). Moreover, there are medical units with persistently low and persistently high fall rates, suggesting that disparities in care exist among hospitals (Staggs et al., 2015). One study of 800 medical units in 470 hospitals found that 87 percent of the variation in 24-month fall rates was due to between-unit differences, and with the exception of patient days, low- and high-fall units did not differ on nurse staffing or any other unit or hospital characteristic variable (Staggs et al., 2015). This finding suggests that there remains room for improvement in units with high fall rates.

       

      While major injuries (e.g., fractures, closed head injuries, internal bleeding) (Mintz et al., 2022) have the biggest impact on patient outcomes, data from the Network of Patient Safety Databases (NPSD) between 2014 and 2022 showed that in-hospital falls more frequently result in moderate injuries, as defined by National Database of Nursing Quality Indicators (2010). These injuries, including skin tears, avulsions, hematomas, significant bruising, dislocations and lacerations requiring suturing, affected 35.9%, 45.2%, 49.8%, and 51% of adults (18-64 years), mature adults (65-74 years), older adults (75-84 years), and aged adults (85+years) who fell, respectively. The residual harm to the patient, after discovery of the fall and after any attempts to minimize adverse consequences, also increased with age. For example, 38.7% of adults who fell in the hospital experienced residual harm compared to 56.8% of older adults and 61% of aged adults. Levels of harm can be categorized following WHO definitions (2009); the NPSD Chartbook reported that 24.2% of falls were followed by mild to moderate levels of harm, 0.4% by severe harms, and 0.1% by death. By focusing on falls with major and moderate injuries, the goal of this hospital harm eCQM is to raise awareness of fall rates and, ultimately, to improve patient safety by preventing falls with injury in all hospital patients.

       

      References: 

      1. AHRQ. (2019). Patient Safety Primer: Falls. Retrieved July 24, 2019, from AHRQ PSNet website: https://psnet.ahrq.gov/primers/primer/40/Falls 
      2. Currie, L. (2008). Fall and Injury Prevention. In E. Hughes RG (Ed.), Patient Safety and Quality: An Evidence-Based Handbook for Nurses (pp. 195–250). Rockville: Agency for Healthcare Research and Quality. 
      3. National Database of Nursing Quality Indicators. (2010). Guidelines for data collection on the American Nurses Association’s National Quality Forum endorsed measures. Kansas City: University of Kansas Medical Center.
      4. Mintz, J., Duprey, M. S., Zullo, A. R., Lee, Y., Kiel, D. P., Daiello, L. A., Rodriguez, K. E., Venkatesh, A. K., & Berry, S. D. (2022). Identification of Fall-Related Injuries in Nursing Home Residents Using Administrative Claims Data. The journals of gerontology. Series A, Biological sciences and medical sciences, 77(7), 1421–1429. 
      5. Network of Patient Safety Databases Chartbook, 2022. Rockville, MD: Agency for Healthcare Research and Quality; September 2022. AHRQ Pub. No. 22-0051. https://www.ahrq.gov/sites/default/files/wysiwyg/npsd/data/npsd-chartbook-2022.pdf
      6. Network of Patient Safety Databases Chartbook, 2023. Rockville, MD: Agency for Healthcare Research and Quality; September 2023. AHRQ Pub. No. 23-0090. https://www.ahrq.gov/sites/default/files/wysiwyg/npsd/data/npsd-falls-chartbook-2023.pdf
      7. Network of Patient Safety Databases Chartbook, 2023. Rockville, MD: Agency for Healthcare Research and Quality; September 2023. AHRQ Pub. No. 23-0082  https://www.ahrq.gov/sites/default/files/wysiwyg/npsd/data/npsd-chartbook-2023.pdf
      8. Staggs, V. S., Mion, L. C., & Shorr, R. I. (2015). Consistent differences in medical unit fall rates: Implications for research and practice. Journal of the American Geriatrics Society, 63(5), 983–987. https://doi.org/10.1111/jgs.13387 
      9. World Health Organization & WHO Patient Safety. (2010) Conceptual framework for the international classification for patient safety version 1.1: final technical report January 2009. World Health Organization. https://apps.who.int/iris/handle/10665/70882

         
    • 2.3 Anticipated Impact

      This eCQM captures the number of patients who experience harm in the form of major and moderate injuries during their inpatient hospitalization. Inpatient falls are among the most common incidents reported in hospitals and can increase length of stay and patient costs. Falls (including unplanned or unintended descents to the floor) can result in patient injury ranging from minor abrasion or bruising to death. 

       

      Falls can result in additional healthcare costs due to increased length of stay and use of additional resources, such as diagnostic imaging. Falls with injury also result in higher patient costs in the inpatient setting. The estimated additional patient costs associated with inpatient falls are $2,680-$15,491 per inpatient stay (Bysshe, 2017). A multi-site prospective cohort study demonstrated that “patients who had an in-hospital fall had a mean increase in LOS of 8 days (95% CI, 5.8-10.4; P < 0.001) compared with non-fallers and incurred mean additional hospital costs of $6,669 (95% CI, $3,888-$9,450; P < 0.001). Patients with a fall-related injury had a mean increase in LOS of 4 days (95% CI, 1.8-6.6; P = 0.001) compared with those who fell without injury (Morello, 2015).

       

      A multi-center study conducted in two US health care systems by Dykes, et al, 2023, demonstrated that “the average total cost of a fall was $62, 521 ($36,776 direct costs), and the average total cost of a fall with any injury was $64,526”. The implementation of evidence-based falls prevention program, Fall TIPS Program (Tailoring Interventions for Patient Safety), was associated with $22 million in savings at study sites across the 5-year study period or $14,600 in net avoided costs per 1000 patient-days.

       

      By focusing on falls with major and moderate injuries, the goal of this hospital harm eCQM is to improve patient safety by preventing falls with injury in all hospital patients and increase hospital monitoring of fall rates. The purpose of measuring the rate of falls with major and moderate injury events is to improve hospitals’ practices for monitoring patients at high risk for falls with injury, implement best practices for prevention in high-risk patients and, in so doing, to reduce the frequency of patient falls with injury. 

       

      Performance Results from Beta Testing: Risk-adjusted rates showed substantial variation in performance scores from 0.0 to 0.257 (95% CI, 0.111-0.324) falls per 1,000 hospital encounter days across the 12 test hospitals. Performance scores were as follows: 

      • Minimum: 0
      • Median: 0.053
      • Mean: 0.08
      • Maximum: 0.2575

       

      See Table 1 and Exhibit 2 in the logic model attachment for a distribution of performance scores across sites. 

       

      References: 

      1. Morello RT, Barker AL, Watts JJ, et al. The extra resource burden of in-hospital falls: A cost of falls study. Med J Aust. 2015;203(9):367.e1-367.e8. doi:10.5694/mja15.00296.
      2. Bysshe T, Yue Gao M, Krysta Heaney-Huls M, et al. Draft Final Report Estimating the Additional Hospital Inpatient Cost and Mortality Associated with Selected Hospital Acquired Conditions.; 2017. www.ahrq.gov. 
      3. Dykes PC, Curtin-Bowen M, Lipsitz S, et al. Cost of Inpatient Falls and Cost-Benefit Analysis of Implementation of an Evidence-Based Fall Prevention Program. JAMA Health Forum. 2023;4(1):e225125. doi:10.1001/jamahealthforum.2022.5125
      2.5 Health Care Quality Landscape

      There is only one existing outcome consensus-based entity (CBE) - endorsed falls with injury measure for acute care setting – “PSI 08: In Hospital Fall-Associated Fracture Rate (CBE #0531, endorsed as part of PSI 90 composite). PSI 08 identifies patients with a claim for a fall-associated fracture during an inpatient encounter. PSI-08 is a claims-based measure, and as such is focused solely on the Medicare fee-for-service population. Additionally, the numerator for this measure is limited to fractures, and does not include fall-associated moderate injuries such as lacerations. Therefore, the Hospital Harm – Falls with Injury measure provides the opportunity to assess the rate of falls with injury in a much larger patient population, and it will ultimately enable CMS to replace PSI 08 in the CMS programs where it is currently used.

      2.6 Meaningfulness to Target Population

      The guidelines developed by Schoberer et al. (2022), the National Institute for Health and Care Excellence (NICE), and the Registered Nurses’ Association of Ontario included patients, patient advocates, and caregivers on their development panels. The World Falls Group (WFG) guidelines development process included feedback from older adults obtained through early and meaningful involvement in the consensus process. The RNAO guideline development process also included consideration of a survey questionnaire sent to key stakeholders, which included patients and caretakers.   

       

      Based on the feedback from public comments, patient/caregiver representatives agreed that the rate of hospital-acquired falls resulting in major or moderate injury is important to measure and can help improve care for patients. During an additional Technical Expert Panel (TEP) meeting, one member additionally stressed that the proposed measure has importance from a patient safety standpoint.

       

      References: 

      1. Montero-Odasso, M., van der Velde, N., Martin, F. C., Petrovic, M., Tan, M. P., Ryg, J., Aguilar-Navarro, S., Alexander, N. B., Becker, C., Blain, H., Bourke, R., Cameron, I. D., Camicioli, R., Clemson, L., Close, J., Delbaere, K., Duan, L., Duque, G., Dyer, S. M., … Rixt Zijlstra, G. A. (2022). World guidelines for falls prevention and management for older adults: a global initiative. Age and Ageing, 51(9), 1–36
      2. NICE. Falls in Older People: Assessing Risk and Prevention. London, UK; 2013
      3. RNAO. Preventing Falls and Reducing Injury from Falls. 4th edition. Toronto, ON; 2017
      4. Schoberer, D., Breimaier, H. E., Zuschnegg, J., Findling, T., Schaffer, S., & Archan, T. (2022). Fall prevention in hospitals and nursing homes: Clinical practice guideline. Worldviews on Evidence-Based Nursing, 19, 86-93
         
    • 3.1 Feasibility Assessment

      Thirteen hospitals participated in the evaluation of feasibility—four Epic and nine Allscripts users. All hospital sites confirmed that the data elements used in the measure are captured within the EHR in a structured and codified manner either using nationally accepted terminology standards or local system codes that could be easily mapped. However, one Epic hospital did not always use their structured fields to capture a fall that occurred during hospitalization. For this reason, the site opted to not proceed with reliability and validity phases of testing. Of note, three other Epic sites used in all testing phases did not encounter the same workflow challenges. Please see Table 2 in logic model attachment for combined feasibility scores for data availability, data accuracy, data standards, and workflow across all 13 hospitals. 

      3.2 Attach Feasibility Scorecard
      Falls_COMBINED_Feasibility_Scorecard_EXTERNAL 11 01 2023.xlsx
      3.3 Feasibility Informed Final Measure

      There were no changes to the measure specification as a result of feasibility testing. Any issues identified were site-specific (as described above). 

    • 3.4a Fees, Licensing, or Other Requirements

      There are no fees associated with the use of this eCQM. Value sets are housed in the Value Set Authority Center (VSAC), which is provided by the National Library of Medicine (NLM), in coordination with the Office of the National Coordinator for Health Information Technology and the Centers for Medicare & Medicaid Services.

       

      Viewing or downloading value sets requires a free Unified Medical Language System® (UMLS) Metathesaurus License, due to usage restrictions on some of the codes included in the value sets. Individuals interested in accessing value set content can request a UMLS license at https://uts.nlm.nih.gov/uts/.  

      3.4 Proprietary Information
      Not a proprietary measure and no proprietary components
    • 4.1.3 Characteristics of Measured Entities

      Hospital test site characteristics are shown in Table 3 in the logic model attachment.

       

      • Vendor and location: Nine used Allscripts as their EHR and are headquartered in the Northeastern region of the United States. Four used Epic as their EHR and are headquartered in various regions (Northeast, Southeast and West).
      • Bed size: Three hospitals had between 100-199 beds, eight hospitals had between 200-499 beds, and two hospitals had >499 beds. 
      • Teaching status: Three hospitals were major teaching hospitals and nine were community teaching hospitals. Teaching intensity is often measured by the ratio of interns and residents to beds. In this report, major teaching hospitals are those with an intern- and resident-to-bed ratio (IRB) of 0.25 (one resident for every four beds) or above and at least 50 beds, while community teaching hospitals include hospitals with an IRB of less than 0.25 or teaching hospitals with fewer than 50 beds. 
      4.1.1 Data Used for Testing

      We recruited 4 health systems consisting of 13 individual hospital sites.  One hospital in the Northeast region only participated in alpha (feasibility) testing. We collected data for calendar year 2021 (January 1, 2021 – December 31, 2021) from 12 hospitals. 

      4.1.4 Characteristics of Units of the Eligible Population

      We collected data for calendar year 2021 (January 1, 2021 – December 31, 2021) from 12 test sites. Tables 4 and 5 in the logic model attachment provide information on measure denominator population including age, sex, race, ethnicity, primary payer, comorbidity, and medications. The number of encounters in the measure denominator ranged from a low of 451 to a high of 40,286 across test sites. Note that while the measure is inpatient based, the measure denominator includes emergency department visits and observation stays that were eventually admitted.

      4.1.2 Differences in Data

      Hospital 13 (located in the Northeast region) only participated in alpha (feasibility) testing. This was due to inconsistent workflows around clinical documentation that a fall occurred during hospitalization. Of note, this was an Epic site, and 3 other Epic sites used in all testing phases did not encounter the same workflow challenges.

       

      Measure score level reliability testing used data from the full denominator population in Hospitals 1-12. Measure data element level validity testing, on the other hand, were based on subsamples drawn from the measure initial population using the approach of random sampling without replacement.  These subsamples served as the foundation upon which clinical abstractors compared data exported from the EHR (eData) to data manually abstracted from patients’ medical charts (mData, or “gold standard”).  This process is commonly known as the parallel-form comparison. When drawing the subsamples, we held constant the distribution of patient characteristics exhibited in the initial population to the extent possible (e.g., % of male, % of white, % of black, etc. in the abstraction sample are comparable to those in the initial population to the extent possible).
       

    • 4.2.1 Level(s) of Reliability Testing Conducted
      Accountable Entity-Level (e.g., signal-to-noise analysis)
      4.2.2 Method(s) of Reliability Testing

      We applied split-half and test-retest approaches to estimate the reliability of this risk-adjusted measure at the accountable entity (hospital) level, using the intracluster correlation coefficient (ICC) as an estimator. As formulas are not allowed in the online form, see logic model attachment pg. 6-7 for the methodology.

       

      The higher the ICC, the greater the statistical reliability of the measure, and the greater the proportion of variation that can be attributed to systematic differences in performance across hospitals (i.e., signal as opposed to noise). We used the rubric established by Landis and Koch (1977) to interpret ICCs:

      • 0 – 0.2: slight agreement 
      • 0.21 – 0.39: fair agreement
      • 0.4 – 0.59: moderate agreement
      • 0.6 – 0.79: substantial agreement
      • 0.8 – 0.99: almost perfect agreement
      • 1: perfect agreement

       

      References

      1. Dickens, William T. "Error components in grouped data: is it ever worth weighting?." The Review of Economics and Statistics (1990): 328-333.
      2. Landis, J. Richard, and Gary G. Koch. "The measurement of observer agreement for categorical data." biometrics (1977): 159-174.
      3. Spearman-Brown Prophecy Formula” in: Frey, B. (2018). The SAGE encyclopedia of educational research, measurement, and evaluation (Vols. 1-4). Thousand Oaks, CA: SAGE Publications, Inc. doi: 10.4135/9781506326139
      4.2.3 Reliability Testing Results

      Signal-to-noise reliability was estimated as an intraclass correlation coefficient based on a two-way mixed model with facility random effects (C,1). 

      • Minimum: 0.195
      • 25th percentile: 0.746
      • Median: 0.826
      • 75th percentile: 0.892
      • Maximum: 0.948

       

      Exhibit 3 in the logic model attachment shows the distribution of SNRs across test sites. 

      Table 2. Accountable Entity–Level Reliability Testing Results by Denominator-Target Population Size
      Accountable Entity-Level Reliability Testing Results
      &nbsp; Overall Minimum Decile_1 Decile_2 Decile_3 Decile_4 Decile_5 Decile_6 Decile_7 Decile_8 Decile_9 Decile_10 Maximum
      Reliability 0.826 0.195 0.948
      Mean Performance Score 12 1 1
      N of Entities 193398 451 40286
      4.2.4 Interpretation of Reliability Results

      HH-Falls demonstrates high signal-to-noise reliability at most test facilities. ICC estimates ranged from 0.195 to 0.948 across test sites, with a mean and median equal to 0.762 and 0.826, respectively. ICCs at 10 of the 12 hospitals were at least 0.6 with 2 hospitals having lower values (0.46 and 0.195) due to very small numerators and denominators (i.e., site 4 is a children’s hospital but was evaluated since patients aged 18-20 years were included in their population). Decile analysis was not possible with only 12 facilities reporting complete data. Overall, testing results showed that HH-Falls, as currently specified, can distinguish the true performance in hospital acquired falls with major or moderate injury from one hospital to another.

    • 4.3.1 Level(s) of Validity Testing Conducted
      Person– or encounter–level (i.e., data element) (e.g., sensitivity and specificity)
      Accountable entity–level (e.g., criterion validity)
      4.3.2 Type of accountable entity-level validity testing conducted
      Empirical validity testing at the accountable entity-level (e.g., criterion validity, construct validity, known groups analysis)
      4.3.3 Method(s) of Validity Testing

      To empirically assess data element validity, we compared data exported from the EHR (eData) to data manually abstracted from patients’ medical charts (mData) for a subsample of measure initial population. We then quantified the validity by calculating four statistics that tell us if the measure is subject to false positives and false negatives: 

      • Positive Predictive Value (PPV)—describes the probability that a patient who experienced the harm during hospitalization, per the EHR, is confirmed as a positive case per the clinical abstractor. 
      • Sensitivity— describes the probability that an encounter where the patient experienced the harm per the mData was correctly classified as having the same in the eData.   
      • Negative Predictive Value (NPV)—describes the probability that a patient who did not experience the harm per the eData is confirmed as a negative case with mData (either because the encounter is excluded from the denominator or numerator negative).   
      • Specificity— describes the probability that a patient who did not experience a harm per clinical abstraction was correctly classified as not experiencing the harm by the eData. 

       

      This process of data comparison is frequently known as the parallel-form comparison. As formulas are not allowed in the online form, see logic model attachment p.7-8 for methodology. 

       

      To assess measure score validity, we used face validity. Specifically, we reviewed the measure specification and results with members from our Hospital Harm Technical Expert Panel (TEP) and Technical Advisory Group (TAG). We collected feedback on the precision of the measure specifications, importance of the measure outcome, and whether the performance scores can be used to distinguish good from poor hospital-level quality. 

       

      To evaluate the empirical impact of each exclusion criterion:

      1. Using the full denominator data, we removed exclusion criteria one at a time from the measure logic and calculated the marginal and relative increase in the number of numerator and denominator encounters as a result.  
      2. Using the abstraction data, we compared each excluded sample case to the electronic information stored in the patient’s medical record to assess whether the automated exclusion truly met the clinical criteria for exclusion.   
      4.3.4 Validity Testing Results

      As shown in table 6 in the logic model attachment, across all sites there is a 4.6% increase in the denominator and a 3.6% increase in the numerator when removing the one measure exclusion. This, along with the face validity in excluding present on admissions falls, is evidence that the exclusion occurs frequently enough to justify its use in the measure. 

       

      See tables 7-10 in the logic model attachment for PPV, sensitivity, NPV, and specificity values across sites. 

       

      Face validity results are as follows: 

      • 16 of 16 members (100%), including 3 patient and family caregiver representatives, voted “yes” that the measured outcome (rate of in-hospital falls resulting in major or moderate injury) was important to measure and can improve care for patients.
      • 15 of 16 members (94%), including 3 patient and family caregiver representatives, voted “yes” that measure specifications were precise and that it appears to measure what it is supposed to (i.e., face validity). The individual who voted “no” questioned the need for any risk-adjustment (in response to which our team explained that risk-adjustment only accounts for patient characteristics present on admission, is designed to support fair comparisons across hospitals that treat very different types of patients, and does not reduce hospitals’ motivation to prevent falls with injury).
      • 14 of 16 members (88%), including 3 patient and family caregiver representatives, voted “yes”, that the measure’s performance scores provide an accurate reflection of hospital-level quality, and scores resulting from the measure (Hospital Harm: Falls with Injury) can be used to distinguish good from poor hospital-level quality related to hospital-acquired falls with major or moderate injury. Of the two members who voted “no:” 1) one felt that hospital-level quality needs to be measured by more than just one element (in response to which our team indicated CMS’ intent to use this measure as part of a patient safety composite eCQM that will add to a comprehensive portfolio of other quality measures already implemented into CMS programs); and 2) another individual indicated that the improvement opportunity for moderate injury is less than for serious injury and requested to see a breakdown of the various types of falls (which was provided in a follow-up email).
         
      4.3.5 Interpretation of Validity Results

      HH-Falls excludes inpatient hospitalizations where the patient has a fall diagnosis present on admission. This criterion uses the structured diagnosis information and its POA status to determine if patients had a fall prior to the start of the encounter. Overall, the measure exclusion is necessary to reduce the measure’s false positive rate and to prevent hospitals from being penalized by including falls that occurred prior to the encounter, when injuries resulting from these falls may be diagnosed later in a hospital stay. 

       

      Testing results indicate strong concordance and inter-rater agreement between data exported from the EHR and data in the patient chart. For the measure numerator, PPV denotes the probability that an EHR-reported fall with injury is a valid fall with injury based on the clinical review of patients’ medical records. Numerator PPV across all test sites was 98.77%. For measure denominator exclusions, PPV denotes the probability that cases excluded from the measure per the EHR truly met the clinical rationale for exclusion. Denominator exclusion PPV across all test sites was 100%. 
       

    • 4.4.1 Methods used to address risk factors
      Statistical risk model with risk factors
      4.4.2 Conceptual Model Rationale

      It is well understood that there are major risk factors for falls with injury, many of which are outside hospitals’ control (e.g., age, frailty), which is why current practice guidelines emphasize risk assessment and mitigation. It is also well understood that misguided efforts to reduce fall rates to zero (i.e., by limiting patient activity or movement, installing bed or chair alarms) may cause other harms that are likely to exceed fall-related harms (see, for example, https://psnet.ahrq.gov/perspective/implementing-fall-prevention-program and https://psnet.ahrq.gov/web-mm/failure-ensure-patient-safety-leads-patient-falls-nursing-homes).

       

      Conceptually, risk factors for in-hospital falls with injury can be separated into two categories: risk factors for falling, given hospitalization; and risk factors for moderate or serious injury, given a fall. Some personal characteristics are risk factors for falling but are unlikely to affect the risk of injury given a fall, whereas other personal characteristics are risk factors for injury given a fall, but are unlikely to affect the risk of falling. Our review below focuses on risk factors for falls with injury in the inpatient setting; a much larger literature describes risk factors for falls in ambulatory settings (over several years). Patient attributes (demographics, comorbid conditions, clinical signs and symptoms, functional risk factors, and others) present at the start of care are integral components of the risk model, in that they directly influence the measured outcome and hospitals have less control. 

       

      Social factors have been shown to have relatively little marginal impact on the risk of falls with injury in inpatient settings, except as shown in the attached conceptual model. As summarized by Noel (2021), non-Hispanic Black “(NHB) adults have higher bone mineral density (BMD), lower prevalence of osteoporosis, and lower rates of fracture compared with NHW adults. Research on Hispanic adults, however, is less clear, with conflicting evidence regarding BMD, osteoporosis, and fractures. Although Asian populations generally show lower BMD, higher prevalence of osteoporosis, and lower fracture rates compared with NHW adults, data are limited... there is considerable variation within these groups based on origin for genetic, lifestyle, social, cultural, and environmental factors.” Because the impact of social factors on the risk of inpatient falls with injury appears to be mediated through clinical characteristics such as osteoporosis and other comorbidities, we adjust for those latter factors (rather than social factors) in our final model. Some of the factors described below were tested but proved not to be independent risk factors for falls with injury in the available data. The risk-adjustment model will be updated annually (from the existing feature set) and additional risk factors will be added to the model as needed.

       

      Age

      Advanced age is recognized as a risk factor for falling and for fall-related injuries among hospitalized patients, although it may serve largely as a proxy for frailty and related concepts that cannot be measured directly. For example, the Network of Patient Safety Databases (NPSD) Falls Chartbook 2023 analyzed patient safety events from 2014 to 2022 and demonstrated that the residual harm after a fall, defined by the extent of harm to the patient after discovery of the incident and after any attempts to minimize adverse consequences, increased with age. Specifically, 38.7% of adults (18-64 years) experienced residual harm compared to 56.8% of older adults (75-84 years) and 61% of aged adults (85+ years). The current risk model for AHRQ PSI 08, based on 11,536 in-hospital fall-associated fractures among over 58 million patients, estimates a nearly linear effect of age from <40 years to >85 years.

       

      Weight Loss

      Several studies have reported increased risk of harmful falls in patients with malnutrition and low BMI (Lackoff, 2019), especially in the older elderly population (>80 years) (Vivanti, 2010, Bellanti, 2022). Based on a systematic review and meta-analysis by Neri et al. (2020), obesity increases the risk of falls but is a protective factor for injury, given falls (due to greater bone mineral density and less kinetic energy transmission to bone). The current risk model for AHRQ PSI 08, based on 11,536 in-hospital fall-associated fractures among over 58 million patients, estimates an adjusted OR of 1.51 (95% CI: 1.44-1.58) for comorbid weight loss.

       

      Delirium

      Delirium is common among hospitalized older adults, “with studies suggesting that up to 31% of older adults have delirium on hospital admission”. In a systematic review, Sillner et al. (2019) reported that “the median risk of falling with delirium among the studies was 12% (range from 6% to 67%) with smaller studies on the higher end of the range. The risk of falling was lower in the comparison group without delirium in all studies (median 2%, range 1% to 47%). Accordingly, the RR for falls with delirium was elevated and significant in all studies but one (median RR = 4.5, range 1.4–12.6).” The current risk model for AHRQ PSI 08, based on 11,536 in-hospital fall-associated fractures among over 58 million patients, estimates an adjusted OR of 1.28 (95% CI: 1.20-1.37) for comorbid psychotic delirium.

       

      Dementia

      Patients with dementia have increased risk of falls during hospitalization (Jørgensen, 2015, Morello, 2015, Thurman, 2008, Homann, 2013, Sterke, 2012, Oliver, 2007). For example, a study by Jørgensen, et. al. (2015) demonstrated significantly increased odds of in-hospital fall-related major injuries among individuals with dementia, compared with patients without dementia (OR = 2.34, CI: 1.87–2.92). The use of psychotropic drugs, even at low defined daily dose (0.25 DDD), to treat symptoms of dementia further increases the risk of in-hospital falls (Sterke, 2012). The current risk model for AHRQ PSI 08, based on 11,536 in-hospital fall-associated fractures among over 58 million patients, estimates an adjusted OR of 1.72 (95% CI: 1.64-1.81) for comorbid dementia.

       

      Depression 

      Depression has been identified as one of the risk factors for falls (Homann, 2013, Thurman, 2008, Djurovic, 2021). For example, the retrospective case-control study by Djurovic, 2021, confirmed that depression is a statistically significant risk factor for falls (P<0.001), recognizing “a causal link between depressive symptoms and the falls.” Antidepressants are considered to be an independent risk factor for falls. For example, in the retrospective case-control study by Castaldi (2022), antidepressants had a significant correlation with increased risk of falls (OR: 2.18; CI 95%: 1.32-3.59). The current risk model for AHRQ PSI 08, based on 11,536 in-hospital fall-associated fractures among over 58 million patients, estimates an adjusted OR of 1.34 (95% CI: 1.28-1.39) for comorbid depression.

       

      Psychosis/Psychotic disorders 

      Psychosis and psychotic disorders have been found risk factors for falls. Study findings demonstrate increased immobility as well as bone density loss associated with psychotic disorders (Forns et al., 2021; Stubbs et al., 2018). For example, in the multivariable analysis of predictors of fractures by Stubbs (2018), psychosis was an independent and significant predictor for fall-related fractures requiring hospitalization (HR: 2.05, 95% CI 1.53-2.73). The current risk model for AHRQ PSI 08, based on 11,536 in-hospital fall-associated fractures among over 58 million patients, estimates an adjusted OR of 1.28 (95% CI: 1.20-1.37) for comorbid psychosis.

       

      Other Neurologic Disorders

      Neurological disorders put patients at a higher risk for injurious falls during hospitalization. These conditions include peripheral neuropathy, disorders of gait and balance (Homann, 2013, Thurman, 2008, Djurovic, 2021), epilepsy, including seizure disorder (Homann, 2013, Spritzer 2015, Pati, 2013), Parkinson disease, multiple sclerosis, stroke, and other neurological disorders (Gianni, 2014, Forns, 2021, Cameron, 2018, Jørgensen, 2015, Allen, 2013, Thurman, 2008, Homann, 2013). For example, a study by Forns, et al. (2021) comparing patients with Parkinson disease with (PDP) and without psychosis (PD), found that PDP patients had higher risk for falls and fractures than those without psychosis. This effect was noted separately for falls (IRR = 1.48; 95% CI, 1.43–1.54) and any fractures (IRR = 1.17; 95% CI, 1.08–1.27) as well as for specific types of fracture, including pelvis and hip fractures. The current risk model for AHRQ PSI 08, based on 11,536 in-hospital fall-associated fractures among over 58 million patients, estimates adjusted odds ratios of 1.13 (95% CI: 1.07-1.19) for comorbid other neurologic disorders and 1.23 (95% CI: 1.14-1.31) for seizures.

       

      Sex 

      In papers by Aryee (2017) and Hodgson (2023), male sex was associated with increased risk of falls. The current risk model for AHRQ PSI 08, based on 11,536 in-hospital fall-associated fractures among over 58 million patients, suggests that male sex is associated with higher risk of these adverse events up to 54 years, but lower risk above that age.

       

      Surgery

      Aryee (2017) reported that surgery was a statistically significant protective risk factor. Patients after a recent lower limb amputation may be at increased risk of falling, compared with other surgical and medical patients, according to IHI and VA Fall Prevention Group. The current risk model for AHRQ PSI 08, based on 11,536 in-hospital fall-associated fractures among over 58 million patients, estimates an adjusted OR of 0.063 (95% CI: 0.059-0.068) for medical patients, relative to surgical patients. However, this estimate must be interpreted in the context of other features in the model.

       

      Bone disorders 

      In systematic reviews by Wildes (2015) and Frattura (2022), bone disorders including cancers involving bones were found to be significant risk factors for falls and falls with injuries. For example, Frattura’s review of 11 papers on 1237 patients with osteoporosis undergoing TKA found “pre-operative fall prevalence ranged from 23% to 63%, while post-operative values ranged from 12% to 38%”. In Jørgensen’s (2015) analysis of administrative data on patients 65 years and older with in-hospital falls causing fractures or head injuries with need for surgery or intensive observation, osteoporosis was a significant risk factor for falls with injuries (OR = 1.68, CI: 1.43-1.99). 

       

      Leukemia/lymphoma 

      Several studies found hematological and other cancers to be a risk factor for falls (Martí-Dillet, 2023, Lorca, 2019, Kong, 2014). For example, in the prospective study by Martí-Dillet (2023) of 6090 patients hospitalized with cancer, patients with hematological cancers had the second highest incidence of falls (24.8%), after lung cancer. The current risk model for AHRQ PSI 08, based on 11,536 in-hospital fall-associated fractures among over 58 million patients, estimates an adjusted OR of 1.44 (95% CI: 1.23-1.68) for leukemia and 1.22 (95%CI: 1.06-1.39) for lymphoma.

       

      Liver disease

      Severe liver disease as well as management of severe liver disease increases risk of falls and bleeding due to injuries associated with falls (O’Leary, 2019, Murphy, 2019, Acharya,2021). Acharya (2021) described gait abnormalities among patients with liver cirrhosis listed for deceased solitary liver transplant from 2011 to 2015: “abnormal tandem gait (TG) trended towards increased falls (OR 3.3, P=0.08). 49% had abnormal TG, 61% had cognitive dysfunction (CD), 32.7% had CD plus abnormal TG, 62% had prior overt hepatic encephalopathy (OHE), and 14.7% had falls”. The current risk model for AHRQ PSI 08, based on 11,536 in-hospital fall-associated fractures among over 58 million patients, estimates an adjusted OR of 1.45 (95% CI: 1.30-1.63) for severe and 1.13 (95%CI: 1.05-1.21) for mild liver disease.

       

      Coagulopathy 

      Coagulation disorders and anticoagulant medications put patients at a higher risk for developing bleeding after a fall. IHI and VA Fall Prevention Group identify coagulation issues that put the patient at risk for injury in the event of a fall such as bleeding, anticoagulant use, and abnormal platelet count. “Anticoagulants are commonly used in elderly patients to reduce the risk of potential stroke, but this potential benefit must be weighed against the risk of falls with potentially fatal bleeds” (Llompart-Pou, 2017). “In the regression model for the dependent variable of falling, anemia (OR=2.26, p<0.001) was associated with more than twice the risk of falling.” (Pandya, 2008). The current risk model for AHRQ PSI 08, based on 11,536 in-hospital fall-associated fractures among over 58 million patients, estimates an adjusted OR of 1.08 (95% CI: 1.02-1.15) for comorbid coagulopathies.

       

      Medications POA

      There are several classes of medications, referred to as a fall-risk increasing drugs (FRIDs), especially in adults who are greater than 65 years or older, that increase risks of falls. If these medications were administered at home, with persisting effects at admission to the hospital, then they are appropriate for risk-adjustment.

      • Opioids: Seppala, 2018; Park, 2015; Callis, 2016; Yoshikawa, 2020; Cox, 2014. 
      • CNS depressants: Callis, 2016 (antipsychotics, hypnotics, opioids, benzodiazepines, antiepileptics); Aryee, 2017 (active treatment on CNS agents); Seppala, 2018(antipsychotics, antidepressants, TCAs, SSRIs, benzodiazepines, short-acting benzodiazepines, long-acting benzodiazepines, antiepileptic); Park, 2015 (sedatives, hypnotics, antidepressants including tricyclic antidepressants, selective serotonin reuptake inhibitors, and serotonin norepinephrine reuptake inhibitors); Shuto, 2010 (antiparkinsonian agents, anti-anxiety agents and hypnotic agents); O'Neill, 2019 (anticonvulsant, benzodiazepine anticonvulsant, haloperidol, tricyclic antidepressant); Dominigue, 2018 (lorazepam); Currie, 2008 (sedatives, hypnotics, psychotropics, antiepileptics). 
      • Antihypertensives: Kahlaee, 2018; Shimbo, 2016 (ACE-i,ARB,CCB,BB, vasodilators); Shuto, 2010 (ARB); De Vries, 2018; 2019 American Geriatrics Society (AGS) Beers criteria (alpha blockers, Alpha agonist, calcium channel blockers) 
      • Diuretics: Kahlaee, 2018; O'Neill, 2019; Seppala, 2018; Berry, 2012; Lim, 2009 (increase bone loss on loop diuretics). 
      • Antidepressants: Woolcott, 2009; 2019 AGS Beers criteria; De Jong, 2013; Castaldi, 2021; Park, 2015.

       

      Mediating Factors 

      Several care processes and intermediate factors (or mediators) may also contribute to the occurrence of falls with injuries. These factors are largely within the hospital’s control and are therefore not considered as risk factors. For example, in the NPSD Falls Chartbook 2023 analysis of patient safety reports from 2014 through 2022, 22.9% of in-hospital falls were associated with injury or residual harm among patients ambulating without assistance prior to falling, versus only 6.4% among patients ambulating with assistance. Assistance during ambulation may not decrease the risk of falling, but it appears to reduce the risk of injury as the patient is assisted to the ground. Other process factors are summarized in the Importance section. Other mediating factors include keeping the bed in low position, keeping the call light and personal items in reach, educating the patient and family regarding fall risk, providing non-slip footwear, and visibly identifying each applicable patient as being at risk for fall (e.g., Falling Star).

       

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      48. By the 2019 American Geriatrics Society Beers Criteria® Update Expert Panel. American Geriatrics Society 2019 Updated AGS Beers Criteria® for Potentially Inappropriate Medication Use in Older Adults. J Am Geriatr Soc. 2019;67(4):674-694. doi:10.1111/jgs.15767
      49. Berry SD, Mittleman MA, Zhang Y, et al. New loop diuretic prescriptions may be an acute risk factor for falls in the nursing home. Pharmacoepidemiol Drug Saf. 2012;21(5):560-563. doi:10.1002/pds.3256
      50. Lim LS, Fink HA, Blackwell T, Taylor BC, Ensrud KE. Loop diuretic use and rates of hip bone loss and risk of falls and fractures in older women. J Am Geriatr Soc. 2009;57(5):855-862. doi:10.1111/j.1532-5415.2009.02195.x
      51. Woolcott, J. C., Richardson, K. J., Wiens, M. O., Patel, B., Marin, J., Khan, K. M., & Marra, C. A. (2009). Meta-analysis of the impact of 9 medication classes on falls in elderly persons. Archives of internal medicine, 169(21), 1952–1960. https://doi.org/10.1001/archinternmed.2009.357
      52. De Jong MR, Van der Elst M, Hartholt KA. Drug-related falls in older patients: implicated drugs, consequences, and possible prevention strategies. Ther Adv Drug Saf. 2013;4(4):147-154. doi:10.1177/2042098613486829
      53. Castaldi S, Principi N, Carnevali D, et al. Correlation between fall risk increasing drugs (FRIDs) and fall events at a rehabilitation hospital. Acta Biomed. 2022;92(6):e2021397. Published 2022 Jan 19. doi:10.23750/abm.v92i6.11340 
      54. Dykes PC, Curtin-Bowen M, Lipsitz S, et al. Cost of Inpatient Falls and Cost-Benefit Analysis of Implementation of an Evidence-Based Fall Prevention Program. JAMA Health Forum. 2023;4(1):e225125. doi:10.1001/jamahealthforum.2022.5125  
      55. Dunne, T. J., Gaboury, I., & Ashe, M. C. (2014). Falls in hospital increase length of stay regardless of degree of harm. Journal of evaluation in clinical practice, 20(4), 396–400. https://doi.org/10.1111/jep.12144
      56. Kim, J., Lee, E., Jung, Y., Kwon, H. & Lee, S. (2022). Patient-level and organizational-level factors influencing in-hospital falls. Journal of Advanced Nursing, 78(11), 3641–3651. https://doi.org/10.1111/jan.15254 
      57. Zecevic, A. A., Chesworth, B. M., Zaric, G. S., Huang, Q., Salmon, A., McAuslan, D., Welch, R., & Brunton, D. (2012). Estimating the cost of serious injurious falls in a Canadian acute care hospital. Canadian journal on aging = La revue canadienne du vieillissement, 31(2), 139–147. https://doi.org/10.1017/S0714980812000037
      58. Morello, R. T., Barker, A. L., Watts, J. J., Haines, T., Zavarsek, S. S., Hill, K. D., Brand, C., Sherrington, C., Wolfe, R., Bohensky, M. A., & Stoelwinder, J. U. (2015). The extra resource burden of in-hospital falls: a cost of falls study. The Medical journal of Australia, 203(9), 367. https://doi.org/10.5694/mja15.00296
      59. Najafpour, Z., Godarzi, Z., Arab, M., & Yaseri, M. (2019). Risk Factors for Falls in Hospital In-Patients: A Prospective Nested Case Control Study. International journal of health policy and management, 8(5), 300–306. https://doi.org/10.15171/ijhpm.2019.11
      60. Noel SE, Santos MP, Wright NC. Racial and Ethnic Disparities in Bone Health and Outcomes in the United States. J Bone Miner Res. 2021 Oct;36(10):1881-1905. doi: 10.1002/jbmr.4417.
      4.4.2a Attach Conceptual Model
      Graphic for Falls RA Conceptual Model 11 01 2023.zip
      4.4.3 Risk Factor Characteristics Across Measured Entities

      Tables 4 and 5 in the logic model attachment show substantial variation in the distribution of risk variables across the 12 measured entities. For example, mean age varied from 20.1 (SD=3.0) years at Site 4 (a children’s hospital that admits young adults) to 69.3 (SD=18.2) years at Site 7. The percentage of Black patients varied from 5.3% at Site 7 to 34.3% at Site 1. The percentage of Hispanic patients varied from 3.0% at Site 1 to 86.3% at Site 9. The percentage of Medicaid-enrolled patients varied from 10.4% at Site 1 to 54.8% at Site 4. Most comorbidities and home medication-related variables also demonstrated substantial variation across sites; for example, the prevalence of obesity varied across non-children's hospitals from 10.5% at Sites 5 and 6 to 51.1% at Site 1.

      4.4.4 Risk Adjustment Modeling and/or Stratification Results

      The final risk-adjustment model was estimated using cluster-adjusted Poisson regression with an exposure time offset term (Stay_days) run on the entire dataset. All risk factors were dichotomous (0/1) except for age, as described above. Data sources included:

      • ICD-10-CM diagnosis codes for comorbidities present on admission, including Obesity, Weight loss or malnutrition, Coagulation disorder, Delirium, Dementia, Depression, Seizures and epilepsy, Leukemia or lymphoma, Liver disease (moderate or severe), Malignant bone disease, Neurological movement disorders, Other neurological disorders, Osteoporosis, Neuropathy, Psychosis, and Stroke (POA);
      • Anesthesia record for surgery (CHECK);
      • EHR home medication list for Antidepressants, Antihypertensives, CNS depressants, Diuretics, and Opioids;
      • EHR hospital medication record for Anticoagulants; and
      • EHR demographic fields for age, sex, race, ethnicity, and primary payer.

       

      After feature selection with 100-fold cross-validation and testing on the hold-out test set, the only retained risk factors were age (in linear form), weight loss or malnutrition POA, delirium POA, dementia POA, and other neurological disorders POA. We tested models forcing in other factors and found only one statistically significant effect at the p<0.1 level (i.e., home opioid medication) and no meaningful improvement in any metric of model performance (e.g., AUC, Brier score, AIC/BIC). 

       

      Guided by the conceptual model, we developed the baseline risk adjustment model for Falls with injury using the following process. 

      1. Randomly partitioned the full denominator data into a 70% training set and a 30% hold-out (model performance or evaluation) test set.
      2. Created contingency tables for all categorical features to identify any that had zero cells for either the positive or negative outcome. These features were not considered further due to anticipated model convergence problems (i.e., quasi-complete separation). For continuous variables, such as age, we ran locally weighted bivariate regressions (i.e., locally weighted scatterplot smoothing, or LOWESS) to understand the functional form of the relationship. This analysis confirmed that the risk of fall with injury was linearly related to age through nearly all the age distribution, from about 30 to 90 years of age. 
      3. Fit one model using the least absolute shrinkage and selection operator (LASSO) on the training set using 100-fold cross-validation (CV). This step helped to assess model fit on the training set, while facilitating parameter tuning (e.g., the lambda regularization parameter in the cross-validation [CV]-based LASSO). We chose the final model where the regularization parameter (lambda) was set to lambda1se, i.e., “one-standard-error” (i.e., the largest lambda at which the mean squared error (MSE) is within one standard error of the minimum MSE.). This rule is standard practice for improving generalization, and its suitability was confirmed using the hold-out test set.
      4. Fit an Elastic net model with the set of initial features on the training set using a 100-fold cross-validation (CV) and finally assessing generalizability on the hold-out test set. The final model selected was the one where the regularization parameter was lambda1se.  Elastic net was developed by Zou and Hastie in 2005 by combining the improvements of LASSO and ridge regression. Its main advantage is in handling multicollinearity. It outperforms LASSO in prediction accuracy and provides a unique solution due to the ridge regression penalty term. 
      5. Compared selected features (or risk factors) across the two models by consulting with clinicians to confirm that no feature was included incorrectly from a clinical standpoint. We eventually decided to use the features chosen by Elastic net. 
      6. The final risk-adjustment model was a cluster-adjusted Poisson model with an offset for patient stay days, accounting for the fact that in-hospital falls followed a Poisson distribution with stay days as an indicator of exposure time. The model was estimated on the entire dataset using the set of features selected by Elastic net through 100-fold cross-validation and testing on the hold-out test set. 
      7. The risk-adjustment model was also tested with additional social drivers of health variables (Medicaid insurance, Hispanic ethnicity, Race), considered individually and collectively.

       

      References

       

      1. T. Hastie, R. Tibshirani, J. Friedman, The Elements of Statistical Learning (Springer, 2001), vol. 1.
      2. H. Zou and T. Hastie, “Regularization and variable selection via the elastic net," Journal of the Royal Statistical Society: Series B (Statistical Methodology), vol. 67, no. 2, pp. 301-320, 2005.
      4.4.4a Attach Risk Adjustment Modeling and/or Stratification Specifications
      FALLS WITH INJURY RISK MODEL 11 01 2023.xlsx
      4.4.5 Calibration and Discrimination

      We summarize model performance using the following measures:

      • Overall model discrimination as assessed by C-statistic. The C-statistic is the area under the receiver-operator curve (i.e., AUC) that measures the discriminative ability of a regression model across all levels of risk. It also describes the probability that a randomly selected patient who experienced a fall with injury had a higher expected value than a randomly selected patient who did not experience that event. The AUC was 0.781 in the holdout test set (based on Elastic net) and 0.852 for the final Poisson model. These values indicate strong discrimination performance, relative to a random classifier with AUC=0.5.
        •    The precision-recall (PR) curve and the area under the curve (AUPRC). The PR curve and AUPRC are less sensitive to data imbalance or class imbalance (i.e., very rare events) than the AUC. The AUPRC was 0.00166 in the holdout test set (based on Elastic net), indicating poor prediction at the individual patient level but reasonable performance relative to a random classifier with AUPRC=0.00043. 
        •    Model calibration was assessed across deciles of patient risk using Hosmer-Lemeshow plots. The deciles of risk are ten mutually exclusive groups containing equal numbers of discharges, ranging from very low-risk patients (according to the model) to high-risk patients. We do not provide Hosmer-Lemeshow test statistics because, given the large sample size of our data, the null hypothesis is almost always rejected. Moreover, the plots provide more detail on model fit than the overall Hosmer-Lemeshow statistic. Because over 53% of events occurred in the highest-risk decile, and nearly 76% occurred in the highest-risk quintile, the decile analysis is statistically unstable.
        •    A preferred approach in this situation is to estimate calibration belts suggested by Nattino et al. (2017). Calibration belts are an advance over the conventional Hosmer-Lemeshow plot, as the latter has the limitation of undue sensitivity to the choice of bins and extreme fluctuations in the observed-to-expected ratios in bins with few harm events. The null hypothesis of perfect calibration is never rejected at the 95% confidence level (p=0.052).
         

       

      References:

      1. Nattino, G., Lemeshow, S., Phillips, G., Finazzi, S., & Bertolini, G. (2017). Assessing the calibration of dichotomous outcome models with the calibration belt. The Stata Journal, 17(4), 1003-1014
      4.4.5a Attach Calibration and Discrimination Testing Results
      FALLS WITH INJURY CALIBRATION AND DISCRIMINATION TESTING_11 01 2023.pdf
      4.4.6 Interpretation of Risk Factor Findings

      See above. 

      4.4.7 Final Approach to Address Risk Factors
      Statistical risk model with risk factors
      Risk adjustment approach
      On
      Risk adjustment approach
      Off
      Specify number of risk factors

      5 (age, weight loss, delirium, dementia, other neurologic disorders)

      Conceptual model for risk adjustment
      Off
      Conceptual model for risk adjustment
      On
    • 5.1 Contributions Towards Advancing Health Equity

      There may exist disparities in the rate of in-hospital falls. According to a report from the Leapfrog Group, the rate of in-hospital falls with hip fracture is significantly higher for patients insured by Medicare and Medicaid than for privately insured patients. This analysis also found the rate of in-hospital fall with hip fracture is also significantly lower for Non-Hispanic Black and Hispanic patients than for White patients. 

       

      Using data from 12 hospitals we conducted a social disparities analysis. Our results align with the literature as we found:

       

      • Hispanic patients have significantly lower risk of fall with injury (OR=0.36; 95% CI, 0.10-0.91) than non-Hispanic patients, after adjusting for age and other factors in the risk-adjustment model.
      • Black patients (OR=0.48; 36; 95% CI, 0.24-0.88) and patients of "other" race (OR=0.47; 95% CI, 0.23-0.89) have significantly lower risk of fall with injury than patients of White or "unknown" race, after adjusting for age and other factors in the risk-adjustment model. 
      • Racial/ethnic differences are likely to reflect known variation in the prevalence of osteoporosis, as we find very few false negative cases (see above).  
      • Risk of fall with injury is unrelated to Medicaid or uninsured status (OR=0.99), or dual eligibility among Medicare beneficiaries, after adjusting for age and other factors in the risk-adjustment model. 

       

      Reference:

      1. Gangopadhyaya, A., Pugazhendhi, A., Austin, M., Campione, A., & Danforth, M. (2023) Racial, ethnic, and payer disparities in adverse safety events: Are there differences across Leapfrog Hospital Safety Grades? The Leapfrog Group. https://www.leapfroggroup.org/racial-ethnic-and-payer-disparities-adverse-safety-events-are-there-differences-across-leapfrog
    • 6.2.1 Actions of Measured Entities to Improve Performance

      Certain protocols and prevention measures to reduce patient falls with injury include using fall risk assessment tools to gauge individual patient risk, implementing fall prevention protocols directed at individual patient risk factors, and implementing environmental rounds to assess and correct environmental fall hazards. Recommended clinical guidelines and practices to reduce falls and injuries from falls in hospitals support many prevention activities including implementing multifactorial interventions (see clinical practice guidelines tables 11 to 27 in logic model attachment ) and tailoring interventions to individual patient's conditions and needs (WFG, 2022, RNAO, 2017; ACS NSQIP/AGS, 2016; NICE, 2013). The proposed measure would enable hospitals to track and trend the number and rate of falls with major and moderate injuries to assess and improve fall intervention efforts over time and compare their performance with that of other organizations. We collected feedback from 4 measured entities (hospital systems) on measure usability. All 4 measured entities (100%) agreed that the information produced by the performance measure is easy to understand and useful for decision making. Additionally, we polled 3 patients/family caregivers and all agreed that the measure outcome is important to know and can help improve care for patients. 

       

      References: 

      1. Montero-Odasso, M., van der Velde, N., Martin, F. C., Petrovic, M., Tan, M. P., Ryg, J., Aguilar-Navarro, S., Alexander, N. B., Becker, C., Blain, H., Bourke, R., Cameron, I. D., Camicioli, R., Clemson, L., Close, J., Delbaere, K., Duan, L., Duque, G., Dyer, S. M., Rixt Zijlstra, G. A. (2022). World guidelines for falls prevention and management for older adults: a global initiative. Age and Ageing, 51(9), 1–36
      2. RNAO. Preventing Falls and Reducing Injury from Falls. 4th edition. Toronto, ON; 2017
      3. NICE. Falls in Older People: Assessing Risk and Prevention. London, UK; 2013
      4. ACS National Surgical Quality Improvement Program (NSQIP)/American Geriatrics Society (AGS). Optimal Perioperative Management of the Geriatric Patient: Best Practices Guideline from ACS NSQIP/AGS.; 2016. https://www.facs.org/-/media/files/quality-programs/geriatric/acs-nsqip-geriatric-2016-guidelines.ashx?la=en. Accessed July 9, 2019. 

    • Submitted by Jamar Haggans,… (not verified) on Wed, 12/20/2023 - 17:58

      Permalink

      AOTA supports the Hospital Harm – Falls with Injury measure for the Hospital Inpatient Quality Reporting Program and Medicare Promoting Interoperability Program for Eligible Hospitals and Critical Access Hospitals. This measure will raise awareness of fall rates, improving patient safety and lowering healthcare costs. Occupational therapy practitioners are often involved with hospital falls programs and work to reduce fall risk by providing skilled, evidenced-based interventions that address physical, cognitive, and psychosocial factors inhibiting safe performance in meaningful everyday activities. 

       

      AOTA encourages the measure developer to review language of denominator exclusion. As written, the denominator exclusion of patients who have a fall diagnosis upon admission may cause confusion and inappropriate use of fall diagnosis in attempt to exclude patients from this measure. It is not clear that the fall diagnosis at admission pertains to a fall that occurred outside of the time of the inpatient hospitalization. 

      Organization
      American Occupational Therapy Association

      Submitted by MPickering01 on Thu, 01/11/2024 - 18:30

      Permalink

      CBE #4120e Hospital Harm- Falls with Injury is also a measure under consideration for potential inclusion in the Hospital Inpatient Quality Reporting Program; Medicare Promoting Interoperability Program for Eligible Hospitals and Critical Access Hospitals (CAHs) as MUC2023-048 and is currently undergoing review by the Pre-Rulemaking Measure Review (PRMR) committees. Prior to its review, the measure was posted for PRMR public comment, and received 13 comments, which can be found here: https://p4qm.org/sites/default/files/2024-01/Compiled-MUC-List-Public-Comment-Posting.xlsx. Please review and consider these PRMR comments for MUC2023-048 in addition to any submitted within the public comment section of this measure’s webpage. If there are no comments listed in the public comment section of this webpage, then none were submitted.

    • Submitted by MPickering01 on Mon, 01/08/2024 - 18:33

      Permalink

      Importance

      Importance Rating
      Met
      Importance

      Strengths:

      • Developer cites evidence that falls with injury in the inpatient setting are still prevalent in the U.S., with  700,000-1,000,000 inpatient falls occurring annually, with more than one-third resulting in injury and up to 11,000 resulting in patient death (AHRQ, 2019; Currie, 2008). Data from the Network of Patient Safety Databases (NPSD) between 2014 and 2022 show in-hospital falls more frequently result in moderate injuries, as defined by National Database of Nursing Quality Indicators (2010), including skin tears, avulsions, hematomas, significant bruising, dislocations and lacerations requiring suturing, affected 35.9%, 45.2%, 49.8%, and 51% of adults (18-64 years), mature adults (65-74 years), older adults (75-84 years), and aged adults (85+years) who fell, respectively. Additionally, the devleoper cites evidence of increased health care costs due to fall with injury.
      • Therefore, the developer posits that the goal of this hospital harm eCQM is to improve patient safety by preventing falls with injury in all hospital patients and increase hospital monitoring of fall rates. The purpose of measuring the rate of falls with major and moderate injury events is to improve hospitals’ practices for monitoring patients at high risk for falls with injury, implement best practices for prevention in high-risk patients and, in so doing, to reduce the frequency of patient falls with injury.
      • The developer also reports risk-adjusted rates showing variation in performance scores from 0.0 to 0.257 (95% CI, 0.111-0.324) falls per 1,000 hospital encounter days across the 12 test hospitals.
      • A similar measure currently exists (PSI 08); however, the developer posits that it is limited to the Medicare population and uses claims data. Thus, the Hospital Harm – Falls with Injury measure would provide the opportunity to assess the rate of falls with injury in a much larger patient population and ultimately enabling CMS to replace PSI 08 in the CMS programs where it is currently used.
      • Lastly, the developer notes that through public commenting on the measure, patient/caregiver representatives agreed the rate of hospital-acquired falls resulting in major or moderate injury is important to measure and can help improve care for patients.

      Limitations:

      • None

      Rationale:

      The developer articulates a sound business case for the measure with supportive evidence of the potential impact and meaningfulness to the target population.

      Feasibility Acceptance

      Feasibility Rating
      Met
      Feasibility Acceptance

      Strengths:

      • The developer conducted a feasibility assessment across 13 hospitals—four Epic and nine Allscripts users. All hospital sites confirmed that the data elements used in the measure are captured within the EHR in a structured and codified manner either using nationally accepted terminology standards or local system codes that could be easily mapped.
      • There were no changes to the measure specification as a result of feasibility testing, and there are no fees associated with the use of this eCQM.

      Limitations:

      • One Epic hospital did not always use their structured fields to capture a fall that occurred during hospitalization, as indicated in the Results tab for EHR #13 in the Feasibility scorecard. For this reason, the site opted to not proceed with reliability and validity phases of testing. The developer notes that the test site is aware of their documentation challenges and will work to make improvements to better enable capture going forward. The developer further states that three other Epic sites used in all testing phases did not encounter the same workflow challenges.

      Rationale:

      • The developer conducted a feasibility assessment across 13 hospitals—four Epic and nine Allscripts users. All hospital sites confirmed that the data elements used in the measure are captured within the EHR in a structured and codified manner either using nationally accepted terminology standards or local system codes that could be easily mapped.
      • One Epic hospital did not always use their structured fields to capture a fall that occurred during hospitalization, as indicated in the Results tab for EHR #13 in the Feasibility scorecard. For this reason, the site opted to not proceed with reliability and validity phases of testing. The developer notes that the test site is aware of their documentation challenges and will work to make improvements to better enable capture going forward. The developer further states that three other Epic sites used in all testing phases did not encounter the same workflow challenges.
      • There were no changes to the measure specification as a result of feasibility testing, and there are no fees associated with the use of this eCQM.

      Scientific Acceptability

      Scientific Acceptability Reliability Rating
      Met
      Scientific Acceptability Reliability

      Strengths:

      • Measure is well-defined and specified.
      • Accountable entity-level reliability was assessed with signal-to-noise analysis performed on a 2021 feasibility dataset with 193,398 persons across 12 entities. The median reliability is 0.83. Ten hospitals (83%) have a reliability >0.6.

      Limitations:

      • Only 12 entities were used in the reliability calculations. Two of the 12 entities (17%) have a reliability less than the threshold of 0.6.

      Rationale:

      • Over 80% of the entities can be expected to have a reliability above the threshold of 0.6.
      • Mitigation for entities with low number of persons should be considered.
      Scientific Acceptability Validity Rating
      Met
      Scientific Acceptability Validity

      Strengths:

      • The developer conducted both data element- and accountable entity-level testing.
      • For the data element testing, testing results show strong inter-rater agreement between data exported from the EHR and data in the patient chart. For the measure numerator, the positive predictive value (PPV) denotes the probability that an EHR-reported fall with injury is a valid fall with injury based on the clinical review of patients’ medical records. Numerator PPV across all test sites was 98.77%. For measure denominator exclusions, PPV denotes the probability that cases excluded from the measure per the EHR truly met the clinical rationale for exclusion. Denominator exclusion PPV across all test sites was 100%.
      • For the accountable entity-level testing, the developer conducted face validity of the measure score by collecting feedback from its TEP on the precision of the measure specifications, importance of the measure outcome, and whether the performance scores can be used to distinguish good from poor hospital-level quality. The developer found that 14 of 16 members (88%), including 3 patient and family caregiver representatives, voted “yes”, that the measure’s performance scores provide an accurate reflection of hospital-level quality, and scores resulting from the measure can be used to distinguish good from poor hospital-level quality related to hospital-acquired falls with major or moderate injury.
      • Of the two members who voted “no:” 1) one felt that hospital-level quality needs to be measured by more than just one element (in response to which our team indicated CMS’ intent to use this measure as part of a patient safety composite eCQM that will add to a comprehensive portfolio of other quality measures already implemented into CMS programs); and 2) another individual indicated that the improvement opportunity for moderate injury is less than for serious injury and requested to see a breakdown of the various types of falls (which was provided in a follow-up email).
      • After the measure was submitted to Battelle, the developer added more information in response to the staff assessments: The TEP was composed of clinicians from a range of specialties, health care quality subject matter experts, and three patient/caregiver representatives.

      Limitations:

      • None

      Rationale:

      • The developer conducted both data element- and accountable entity-level testing.
      • For the data element testing, testing results show strong inter-rater agreement between data exported from the EHR and data in the patient chart. For the measure numerator, the positive predictive value (PPV) denotes the probability that an EHR-reported fall with injury is a valid fall with injury based on the clinical review of patients’ medical records. Numerator PPV across all test sites was 98.77%. For measure denominator exclusions, PPV denotes the probability that cases excluded from the measure per the EHR truly met the clinical rationale for exclusion. Denominator exclusion PPV across all test sites was 100%.
      • For the accountable entity-level testing, the developer conducted face validity of the measure score by collecting feedback from its TEP on the precision of the measure specifications, importance of the measure outcome, and whether the performance scores can be used to distinguish good from poor hospital-level quality. The developer found that 14 of 16 members (88%), including 3 patient and family caregiver representatives, voted “yes”, that the measure’s performance scores provide an accurate reflection of hospital-level quality, and scores resulting from the measure can be used to distinguish good from poor hospital-level quality related to hospital-acquired falls with major or moderate injury.
      • Of the two members who voted “no:” 1) one felt that hospital-level quality needs to be measured by more than just one element (in response to which our team indicated CMS’ intent to use this measure as part of a patient safety composite eCQM that will add to a comprehensive portfolio of other quality measures already implemented into CMS programs); and 2) another individual indicated that the improvement opportunity for moderate injury is less than for serious injury and requested to see a breakdown of the various types of falls (which was provided in a follow-up email).

      Equity

      Equity Rating
      Met
      Equity

      Strengths:

      • Developer used data from 12 hospitals to evaluate disparities in falls with injury by race, ethnicity, and insurance status; all analyses adjusted for age and other RA model variables
      • Hispanic patients have lower risk of fall with injury than non-Hispanic patients; Black and "other" race have lower risk that White patients; there is no relationship between insurance status (Medicaid, dual, uninsured) and risk of fall with injury.

      Limitations:

      • None

      Rationale:

      • Developer analyzed data to evaluate possible disparities by race, ethnicity, and insurance status. It determined that race/ethnic differences in risk of falls with injury were mediated by clinical risk factors.

      Use and Usability

      Use and Usability Rating
      Met
      Use and Usability

      Strengths:

      • Developer indicates that the measure is planned for use in public reporting and payment programs
      • After the measure was submitted to Battelle, the developer added more information in response to the staff assessment: The measure is planned for initial rollout in CMS's Hospital IQR program, and CMS has signaled potential future inclusion of Hospital Harm eCQMs in the HAC Reduction Program.
      • Developer cites several sets of guidelines for fall prevention, which call for multifactorial interventions and tailoring interventions to individual patient needs; in addition, developers suggest implementing fall risk assessment tools, fall prevention protocols, and environmental rounds to assess and correct hazards
      • Feedback: 100% of entities (n=4) agreed the information from the measure is easy to understand and useful for decision-making.

      Limitations:

      • None

      Rationale:

      • Developer indicates that the measure is planned for use in public reporting and payment programs but do not provide any other information such as program name, purpose, geographic coverage, level of analysis, etc. Developer cites guidelines for fall prevention, as well as fall risk assessment tools and fall prevention protocols to be tailored to individual patients needs, and environmental rounds to assess and correct hazards. Measured entities consulted (n=4) agreed that information from the measure is useful for decision-making.

      Summary

      N/A

    • Submitted by Amber on Fri, 01/12/2024 - 11:58

      Permalink

      Importance

      Importance Rating
      Met
      Importance

      Agree with staff assessment. Fall rates in hospitals is a continuous improvement opportunity. 

      Feasibility Acceptance

      Feasibility Rating
      Met
      Feasibility Acceptance

      Agree with staff assessment. Documentation and coding poses limited accuracy for small hospitals as injurious falls are not always captured via a claim.

      Scientific Acceptability

      Scientific Acceptability Reliability Rating
      Met
      Scientific Acceptability Reliability

      Agree with staff assessment.

      Scientific Acceptability Validity Rating
      Met
      Scientific Acceptability Validity

      Agree with staff assessment.

      Equity

      Equity Rating
      Met
      Equity

      Agree with staff assessment. Stratifying this measure is not useful. All patients fall or are at risk for falling.

      Use and Usability

      Use and Usability Rating
      Met
      Use and Usability

      Agree with staff assessment.

      Summary

      This measure is appropriate as fall prevention strategies are useful for all size hospitals. Accuracy of this measure may be limited due to it being collected via claims. Rural hospitals do not always capture an injurious fall on a claim.

      Submitted by Antoinette on Fri, 01/12/2024 - 13:08

      Permalink

      Importance

      Importance Rating
      Met
      Importance

      Agree with staff assessment.  Clear evidence on the prevalence, risks and costs of inpatient falls.  Noted importance of monitoring patients at high risk for falls with injury and implementing best practices to mitigate risk.  Supported by patient/caregiver feedback.

      Feasibility Acceptance

      Feasibility Rating
      Met
      Feasibility Acceptance

      Agree with staff assessment.  No associated costs, structured and codified EHR data collection, value sets available in VSAC.

      Scientific Acceptability

      Scientific Acceptability Reliability Rating
      Met
      Scientific Acceptability Reliability

      Agree with staff assessment.  Majority of sites in pilot have reliability >0.06 with two sites having small Ns in sample.

      Scientific Acceptability Validity Rating
      Met
      Scientific Acceptability Validity

      Agree with staff assessment.  High face validity based on TEP feedback that included patient and family representatives. Numerator and denominator PPV >98%.

      Equity

      Equity Rating
      Met
      Equity

      Agree with staff assessment. Conducted a social disparities analysis among 12 hospitals.  Found lower risk of fall with injury among Hispanic, Black and other race individuals. Risk of fall with injury is unrelated to Medicaid or uninsured status

      Use and Usability

      Use and Usability Rating
      Met
      Use and Usability

      Agree with staff assessment.  Measure for public use but lacking implementation plan.

      Summary

      Measure will help to identify high risk patients for highly prevalent problem, and implement best practices for prevention of falls with injury in high risk patients.

      Submitted by rbartel on Sun, 01/14/2024 - 10:31

      Permalink

      Importance

      Importance Rating
      Met
      Importance

      Agree with staff’s assessment. Falls are an area that continues to contribute to cost, length of stay and negative patient outcomes.

      Feasibility Acceptance

      Feasibility Rating
      Met
      Feasibility Acceptance

      Agree with staff assessment. EHR are very useful in the collection of this data. It does bother me that any hospital no matter how small isn’t using their EHR in the collection of this information about falls.

      Scientific Acceptability

      Scientific Acceptability Reliability Rating
      Met
      Scientific Acceptability Reliability

      Agree with staff assessment. Only concern was that all the hospitals involved were teaching hospitals. Wish one or two hospitals would have been non-teaching.

      Scientific Acceptability Validity Rating
      Met
      Scientific Acceptability Validity

      Agree with staff assessment. I think the TEP had a strong make up and included three patient/family member.

      Equity

      Equity Rating
      Met
      Equity

      Agree with staff assessment. They evaluated using race, ethnicity and insurance status. 

      Use and Usability

      Use and Usability Rating
      Met
      Use and Usability

      Agree with staff assessment. Will use public reporting which builds transparency and trust among staff and patients.

      Summary

      N@

      Submitted by Jason H Wasfy on Tue, 01/16/2024 - 09:06

      Permalink

      Importance

      Importance Rating
      Met
      Importance

      Agree with staff.  I do think that the fact that falls present on admission have to be excluded however might suggest some misclassification (ie do patients with prior falls ever get coded as not present on admission?)

      Feasibility Acceptance

      Feasibility Rating
      Met
      Feasibility Acceptance

      Agree with staff, reassuring done in multiple EHRs

      Scientific Acceptability

      Scientific Acceptability Reliability Rating
      Met
      Scientific Acceptability Reliability

      Agree w staff

      Scientific Acceptability Validity Rating
      Met
      Scientific Acceptability Validity

      Agree w staff

      Equity

      Equity Rating
      Not met but addressable
      Equity

      Agree w staff although would be helpful to understand more how osteoporosis (presumably though injury vs no injury) mediates this assocation.  Are falls without injury similar across groups?

      Use and Usability

      Use and Usability Rating
      Met
      Use and Usability

      Agree w staff

      Summary

      n/a

      Submitted by Vik Shah on Tue, 01/16/2024 - 13:37

      Permalink

      Importance

      Importance Rating
      Met
      Importance

      Agree reducing patient falls will improve patient safety and prevent injuries.

      Feasibility Acceptance

      Feasibility Rating
      Met
      Feasibility Acceptance

      Agree with staff recommendations.

      Scientific Acceptability

      Scientific Acceptability Reliability Rating
      Met
      Scientific Acceptability Reliability

      Agree with staff recommendations.

      Scientific Acceptability Validity Rating
      Met
      Scientific Acceptability Validity

      Agree with staff recommendations.

      Equity

      Equity Rating
      Met
      Equity

      Agree with staff recommendations.

      Use and Usability

      Use and Usability Rating
      Met
      Use and Usability

      Agree with staff recommendations.

      Summary

      Measuring patient falls to improve patient safety and prevent injury is important. The source of this information is EHR. Two systems, Epic and Allscripts, took part in the pilot.  It would be helpful to know if the workflow can be implemented with the top 10 EHR vendors.

      Submitted by Kyle A Hultz on Tue, 01/16/2024 - 17:34

      Permalink

      Importance

      Importance Rating
      Met
      Importance

      Agree, good business case with supporting evidence for an ongoing problem which has identified interventions which may prevent harm. There is a financial benefit to institutions, and clear physical benefit to patients.

      Feasibility Acceptance

      Feasibility Rating
      Met
      Feasibility Acceptance

      Agree with assessment. The data is easily mined from EHR's of which the author mention Epic and Allscripts. Many institutions are already tracking more complex and thorough data related to falls. There may be some documentation optimizations required if the measure is implemented as a CMS standard. 

      Scientific Acceptability

      Scientific Acceptability Reliability Rating
      Met
      Scientific Acceptability Reliability

      Agree with assessment that the threshold was met for reliability.

      Scientific Acceptability Validity Rating
      Met
      Scientific Acceptability Validity

      Agree with assessment. Strong inter-rater agreement and panel support including opinions of dissent. 

      Equity

      Equity Rating
      Met
      Equity

      Meets standards.

      Use and Usability

      Use and Usability Rating
      Met
      Use and Usability

      Agree. Will be publicly reported, and this measure will match evidence based guidelines for preventing falls in the institutional setting.

      Summary

      useful measure addressing an ongoing and pertinent patient safety metric. Supported by institutions and the public.

      Submitted by Marjorie Everson on Tue, 01/16/2024 - 19:34

      Permalink

      Importance

      Importance Rating
      Met
      Importance

      agree with staff assessment

      Feasibility Acceptance

      Feasibility Rating
      Met
      Feasibility Acceptance

      agree with staff assessment

      Scientific Acceptability

      Scientific Acceptability Reliability Rating
      Met
      Scientific Acceptability Reliability

      agree with staff assessment

      Scientific Acceptability Validity Rating
      Met
      Scientific Acceptability Validity

      agree with staff assessment

      Equity

      Equity Rating
      Met
      Equity

      agree with staff assessment

      Use and Usability

      Use and Usability Rating
      Met
      Use and Usability

      agree with staff, developer polled users and consumers to back this up.

      Summary

      this is an important expansion of PSI08 and has public support. 

      Submitted by Dr. Joshua Ardise on Wed, 01/17/2024 - 17:34

      Permalink

      Importance

      Importance Rating
      Met
      Importance

      I agree with the Staff's assessment.

      Feasibility Acceptance

      Feasibility Rating
      Met
      Feasibility Acceptance

      How will the measure address situations were the hospital fails to document the fall and/or injury?

      Scientific Acceptability

      Scientific Acceptability Reliability Rating
      Met
      Scientific Acceptability Reliability

      I agree with the Staff's assessment.

      Scientific Acceptability Validity Rating
      Met
      Scientific Acceptability Validity

      I agree with the Staff's assessment.

      Equity

      Equity Rating
      Met
      Equity

      I agree with the Staff's assessment.

      Use and Usability

      Use and Usability Rating
      Met
      Use and Usability

      I agree with the Staff's assessment.

      Summary

      N/A

      Submitted by Michael on Thu, 01/18/2024 - 00:19

      Permalink

      Importance

      Importance Rating
      Met
      Importance

      Well-established and defined.

      Feasibility Acceptance

      Feasibility Rating
      Met
      Feasibility Acceptance

      Agree with staff.  Do share concerns raised around exclusive use of claims vs other EHR data.

      Scientific Acceptability

      Scientific Acceptability Reliability Rating
      Met
      Scientific Acceptability Reliability

      Agree with staff.

      Scientific Acceptability Validity Rating
      Met
      Scientific Acceptability Validity

      Agree with staff.

      Equity

      Equity Rating
      Met
      Equity

      Considered.

      Use and Usability

      Use and Usability Rating
      Met
      Use and Usability

      Agree with staff.

      Summary

      Long-standing and well-recognized challenge worth supporting with measure.

      Submitted by David Clayman on Fri, 01/19/2024 - 10:18

      Permalink

      Importance

      Importance Rating
      Met
      Importance

      I agree with the Staff's assessment.

      Feasibility Acceptance

      Feasibility Rating
      Met
      Feasibility Acceptance

      I agree with the Staff's assessment.

      Scientific Acceptability

      Scientific Acceptability Reliability Rating
      Met
      Scientific Acceptability Reliability

      I agree with the Staff's assessment.

      Scientific Acceptability Validity Rating
      Met
      Scientific Acceptability Validity

      I agree with the Staff's assessment.

      Equity

      Equity Rating
      Met
      Equity

      I agree with the Staff's assessment.

      Use and Usability

      Use and Usability Rating
      Met
      Use and Usability

      I agree with the Staff's assessment.

      Summary

      The measure will improve patient safety and will bring increased awareness to fall rates.

      Submitted by Eleni Theodoropoulos on Fri, 01/19/2024 - 13:54

      Permalink

      Importance

      Importance Rating
      Met
      Importance

      Agree with staff assessment.  Important measure for patient safety.

      Feasibility Acceptance

      Feasibility Rating
      Met
      Feasibility Acceptance

      Agree with staff assessment. Helpful that the assessment evaluated collection in more than a singular EHR and that data is collected in a standardized way for valid data capture.

      Scientific Acceptability

      Scientific Acceptability Reliability Rating
      Met
      Scientific Acceptability Reliability

      Agree with staff assessment.

      Scientific Acceptability Validity Rating
      Met
      Scientific Acceptability Validity

      Agree with staff assessment.

      Equity

      Equity Rating
      Met
      Equity

      Agree with staff assessment.  Data analyzed across various factors (race, ethnicity, insurance status) to evaluate disparities. 

      Use and Usability

      Use and Usability Rating
      Met
      Use and Usability

      Agree with staff assessment.

      Summary

      N/A

      Submitted by Bonnie Zima on Fri, 01/19/2024 - 18:48

      Permalink

      Importance

      Importance Rating
      Met
      Importance

      This team does an excellent job with their scientific literature review.  Their argument for the importance of this measure include that inpatient falls are common, >1/3 result in injury, there is wide variation by unit, inpatient falls more frequently result in moderate injuries, and residual harm increases with age. In addition, they provide evidence that inpatient falls are costly and that additional costs can be attributed to increased LOS and use of additional resources.  The team concludes by citing a study of the Fall TIPS Program that was associated with $22M in savings/5 years. 

      In addition, there is wide performance gap using data from 12 test hospitals: Median performance was 0.053 with range of 0-0.2575, supporting room for improvement if measure is implemented.

      Feasibility Acceptance

      Feasibility Rating
      Met
      Feasibility Acceptance

      Data availability, data accuracy, data standards, and workflow were assessed across 13 hospitals, of which four used EPIC and nine used Allscripts. Overall, capture of data elements was excellent consistent with their careful attention to include structured data elements from certified EHRs. One EPIC hospital had workflow challenges to capture 100% of data elements and was not included in reliability/validity testing. 

      Scientific Acceptability

      Scientific Acceptability Reliability Rating
      Met
      Scientific Acceptability Reliability

      The measure specifications are well defined. 

      Signal-to-noise reliability was estimated as an intraclass correlation coefficient based on a two-way mixed model with facility random effects (C,1). The median was 0.826 which was excellent.  Even the 25%tile was 0.746.  ICCs at 10 of the 12 hospitals were at least 0.6.

      Two hospitals had lower values (0.46 and 0.195) due to very small numerators and denominators.  It was interesting that the team included a children’s hospital (site 4) with the rationale that patients 18-20 were included. If this measure is to be used by Medicare, it seemed a bit odd to include in testing this measure. 

      Scientific Acceptability Validity Rating
      Met
      Scientific Acceptability Validity

      Validity was assessed using three approaches. 

      Parallel-line comparison: To empirically assess data element validity, data exported from the EHR (eData) were compared to data manually abstracted from patients’ medical charts (mData) for a subsample of measure initial population.

      Findings: Numerator PPV across all test sites was 98.77%. Denominator exclusion PPV across all test sites was 100%.

      Sensitivity analyses: To evaluate the empirical impact of each exclusion criterion, using the full denominator data, they removed exclusion criteria one at a time from the measure logic and calculated the marginal and relative increase in the number of numerator and denominator encounters as a result. Using the abstraction data, they compared each excluded sample case to the electronic information stored in the patient’s medical record to assess whether the automated exclusion truly met the clinical criteria for exclusion.   

      Findings: Across all sites there is a 4.6% increase in the denominator and a 3.6% increase in the numerator when removing the one measure exclusion.

      Face Validity using stakeholder ratings:

      Findings: 

      16 of 16 members (100%), including 3 patient and family caregiver representatives, voted “yes” that the measured outcome (rate of in-hospital falls resulting in major or moderate injury) was important to measure and can improve care for patients.

      15 of 16 members (94%), including 3 patient and family caregiver representatives, voted “yes” that measure specifications were precise and that it appears to measure what it is supposed to (i.e., face validity). The individual who voted “no” questioned the need for any risk-adjustment (in response to which our team explained that risk-adjustment only accounts for patient characteristics present on admission, is designed to support fair comparisons across hospitals that treat very different types of patients, and does not reduce hospitals’ motivation to prevent falls with injury).

      14 of 16 members (88%), including 3 patient and family caregiver representatives, voted “yes”, that the measure’s performance scores provide an accurate reflection of hospital-level quality, and scores resulting from the measure (Hospital Harm: Falls with Injury) can be used to distinguish good from poor hospital-level quality related to hospital-acquired falls with major or moderate injury.

       

      In addition, Evidence of Meaningfulness to Target Population

      The guidelines developed by Schoberer et al. (2022), the National Institute for Health and Care Excellence (NICE), and the Registered Nurses’ Association of Ontario included patients, patient advocates, and caregivers on their development panels. The World Falls Group (WFG) guidelines development process included feedback from older adults obtained through early and meaningful involvement in the consensus process. The RNAO guideline development process also included consideration of a survey questionnaire sent to key stakeholders, which included patients and caretakers.   

      Comment and Question: 

      Time period for the encounter or episode of care will vary by patient. Thus, patients with longer LOS up to 120 days will have greater exposure to risk for fall during the hospitalization. Maybe I missed this. But the team consider exploring how incident of patient falls potentially varied by LOS? Crude and risk adjusted?  Cox-proportional hazard model?  Would this information also be useful to hospital administrators? 

      Equity

      Equity Rating
      Met
      Equity

      Using data from 12 hospitals, the team conducted a social disparities analysis. Our results align with the literature as we found:

      Hispanic patients have significantly lower risk of fall with injury (OR=0.36; 95% CI, 0.10-0.91) than non-Hispanic patients, after adjusting for age and other factors in the risk-adjustment model.

      Black patients (OR=0.48; 36; 95% CI, 0.24-0.88) and patients of "other" race (OR=0.47; 95% CI, 0.23-0.89) have significantly lower risk of fall with injury than patients of White or "unknown" race, after adjusting for age and other factors in the risk-adjustment model. 

      Racial/ethnic differences are likely to reflect known variation in the prevalence of osteoporosis, as we find very few false negative cases (see above). 

      Risk of fall with injury is unrelated to Medicaid or uninsured status (OR=0.99), or dual eligibility among Medicare beneficiaries, after adjusting for age and other factors in the risk-adjustment model.

      Use and Usability

      Use and Usability Rating
      Met
      Use and Usability

      Plans for use in public reporting and payment programs. The measure is planned for initial rollout in CMS's Hospital IQR program, and CMS has signaled potential future inclusion of Hospital Harm eCQMs in the HAC Reduction Program.  In addition, the developer that among 4 accountable entities, 100% agreed the information from the measure is easy to understand and useful for decision-making.

      Summary

      Development of this eCQM was led by Anna Michie, American Institutes for Research (AIR) through contracted out work from CMS. The application was well written and approach for development and testing was comprehensive.  Methods were clearly described and thoughtfully done.  This measure was developed with the intent to replace PSI 08 in the CMS programs where it is currently used.  A limitation of PSI-08 is that it is a claims-based measure, and as such is focused solely on the Medicare fee-for-service population. The strength of this revised measure is the wider capture of falls with moderate injury as well as use of structured EHR data elements that are routinely captured across different EHR types.  Overall, this is an outcome measure that will be useful to hospital to track inpatient falls, has capacity in the data elements to stratify by serous vs. moderate falls, and examine impact of quality improvement interventions to prevent inpatient falls/time. 

       

      Submitted by Sam Tierney on Sat, 01/20/2024 - 12:16

      Permalink

      Importance

      Importance Rating
      Not met but addressable
      Importance

      Although not included in the importance section, the developers describe that there are screening and other mitigation strategies to prevent falls.  This should be included in the measure information form - in the importance section to highlight the details of the interventions that would address fall prevention.

      Feasibility Acceptance

      Feasibility Rating
      Met
      Feasibility Acceptance

      The feasibility assessment from the developers demonstrates that the data needed for the measure are in structured fields.  

      Scientific Acceptability

      Scientific Acceptability Reliability Rating
      Not met but addressable
      Scientific Acceptability Reliability

      The information from the hospitals included in testing described them as major teaching and community teaching hospitals.  This is somewhat concerning because it doesn’t include non-teaching hospitals that are likely to be in rural areas.  As a result, it is not a representative sample.  

      The reliability results from the measure are very good.  However, I would like to see reliability tested in rural hospitals.


      The developers have also included a risk adjustment model that accounts for the major differences in rates.

      Scientific Acceptability Validity Rating
      Met
      Scientific Acceptability Validity

      Face validity was strong.

      Equity

      Equity Rating
      Met
      Equity

      The developer did a social disparities analysis and reported on the results.  

      Use and Usability

      Use and Usability Rating
      Met
      Use and Usability

      This measure is intended to replace psi 8 and is an expansion given that the emeasure allows for the collection of data beyond fractures.  

      Summary

      See comments above

      Submitted by Tarik Yuce on Sun, 01/21/2024 - 20:17

      Permalink

      Importance

      Importance Rating
      Met
      Importance

      Not much doubt that it is important to measure and report falls.

      Feasibility Acceptance

      Feasibility Rating
      Met
      Feasibility Acceptance

      Agree with staff comments.

      Scientific Acceptability

      Scientific Acceptability Reliability Rating
      Not met but addressable
      Scientific Acceptability Reliability

      Developers describe testing this measure across two vendor systems and 12 hospitals, which may not be a large enough or representative cohort.

      Scientific Acceptability Validity Rating
      Met
      Scientific Acceptability Validity

      Agree with staff comments.

      Equity

      Equity Rating
      Met
      Equity

      Agree with staff comments.

      Use and Usability

      Use and Usability Rating
      Met
      Use and Usability

      Agree with staff comments.

      Summary

      Measuring inpatient falls is important. The developers of this measure need to provide more details on specific measure components collected from the EHR to give us a better idea of feasibility and reliability. Beyond mandating the measurement and reporting of falls, there has to be an paired incentive for getting patients up out of bed with assistance. Otherwise, we may see a rise in keeping high risk patients bedbound. 

      Submitted by Aileen Schast on Mon, 01/22/2024 - 16:42

      Permalink

      Importance

      Importance Rating
      Met
      Importance

      As one of the most commonly occurring hospital acquired conditions, consistent measuring of patient harm due to falls in the hospital is important on its face.  The benefit of the eCQM is that it would improve upon the currently available PSI 08 measure by expanding beyond the Medicare populations.

      Feasibility Acceptance

      Feasibility Rating
      Met
      Feasibility Acceptance

      My only concern in this area is that no Cerner hospityals were included in the sample.  As one of the most commonly used EMR platforms, it seems odd not to include it.

      Scientific Acceptability

      Scientific Acceptability Reliability Rating
      Met
      Scientific Acceptability Reliability

      Reliability and Validity analytical processes are consistent with best methods and the scores are in line with accepted thresholds (with the exception of 2 scores below threshold in reliability testing).  

      Scientific Acceptability Validity Rating
      Met
      Scientific Acceptability Validity

      As the measure definition is clear and well defined, face validity is strong and will aid in practical acceptability as well.

      Equity

      Equity Rating
      Met
      Equity

      Merasure was evaluated across 13 institutions representing various populations.  The samples provided diversity across multiple equity variables.  That only urban hospitals were included is a limitation, but not a critical one.

      Use and Usability

      Use and Usability Rating
      Met
      Use and Usability

      The measure is ideal in that it is easily measured consistently and there are evidence b ased protocols/bundles related to fall prevention that will guide low-performing hospitals with tools to do better.

      Summary

      Acceptable as submitted

      Submitted by Ashley Tait-Dinger on Mon, 01/22/2024 - 17:46

      Permalink

      Importance

      Importance Rating
      Met
      Importance

      Agree with the staff assessment.  Applaud expansion to outside the Medicare population.

      Feasibility Acceptance

      Feasibility Rating
      Met
      Feasibility Acceptance

      Agree with the staff assessment. 

      Scientific Acceptability

      Scientific Acceptability Reliability Rating
      Met
      Scientific Acceptability Reliability

      Agree with the staff assessment. 

      Scientific Acceptability Validity Rating
      Met
      Scientific Acceptability Validity

      Agree with the staff assessment. 

      Equity

      Equity Rating
      Met
      Equity

      Agree with the staff assessment. 

      Use and Usability

      Use and Usability Rating
      Met
      Use and Usability

      Agree with the staff assessment. 

      Summary

      Agree this measure is very useful to the general public and broad application to all payers.

      Submitted by Anna Doubeni on Mon, 01/22/2024 - 18:11

      Permalink

      Importance

      Importance Rating
      Met
      Importance

      I agree with staff assessment.

      Feasibility Acceptance

      Feasibility Rating
      Not met but addressable
      Feasibility Acceptance

      Some of the public comments raise concerns regarding the feasibility of data collection particularly for any facility that is not using the EHRs tested (EPIC and Allscripts).  As of 2021 Cerner and Meditech were still significant players.

      There was also concern regarding the capture of diagnostic coding given that it is often noted in free text in physician notes or nurse reported.

      Scientific Acceptability

      Scientific Acceptability Reliability Rating
      Met
      Scientific Acceptability Reliability

      I agree with staff assessment.

      Scientific Acceptability Validity Rating
      Met
      Scientific Acceptability Validity

      I agree with staff assessment.

      Equity

      Equity Rating
      Met
      Equity

      I agree with staff assessment

      Use and Usability

      Use and Usability Rating
      Not met but addressable
      Use and Usability

      I agree with use in public reporting however use with payment programs may lead to reactive management. More than one public comment shared concerns of an unintended consequence of decreased mobilization which could also lead to harm.  I anticipate this unintended consequence is more likely to occur if used with payment models if the fear of a corrective action plan or impact on reimbursement leads to reactive management.  

      Summary

      Improving the quality of care including the reduction of falls inpt is important.  I echo the concerns of some of the public comments regarding the risk of decreased mobilization while in the hospital and potentially increasing the risk of falls in the home setting post-discharge.  

      Submitted by Ayers813 on Mon, 01/22/2024 - 23:16

      Permalink

      Importance

      Importance Rating
      Met
      Importance

      Agree with staff assessment .  Developer did a phenomenal job depicting the importance of the measure

      Feasibility Acceptance

      Feasibility Rating
      Met
      Feasibility Acceptance

      agree with staff assessment

      Scientific Acceptability

      Scientific Acceptability Reliability Rating
      Met
      Scientific Acceptability Reliability

      Agree with staff assessment

      Scientific Acceptability Validity Rating
      Met
      Scientific Acceptability Validity

      Agree with staff assessment

      Equity

      Equity Rating
      Met
      Equity

      agree with staff assessment

      Use and Usability

      Use and Usability Rating
      Met
      Use and Usability

      Agree with staff assessment

      Summary

      The developer has outlined the importance and need for the measure as well as provided scientific data to support.  

      Submitted by Jamie Wilcox on Mon, 01/22/2024 - 23:48

      Permalink

      Importance

      Importance Rating
      Met
      Importance

      Agree with staff assessment.

       

      Measuring this domain of care is essential to support health system attention to continuous improvement in this domain. 

      Feasibility Acceptance

      Feasibility Rating
      Met
      Feasibility Acceptance

      Agree with staff assessment. 

      Scientific Acceptability

      Scientific Acceptability Reliability Rating
      Met
      Scientific Acceptability Reliability

      Agree with staff assessment. 

      Scientific Acceptability Validity Rating
      Met
      Scientific Acceptability Validity

      Agree with staff assessment. 

      Equity

      Equity Rating
      Met
      Equity

      Agree with staff assessment. 

      Use and Usability

      Use and Usability Rating
      Met
      Use and Usability

      Agree with staff assessment. 

      Summary

      Public reporting of falls resulting in moderate and major injuries is an important measure for public reporting and helps sustain health system attention to continuous improvement in this area of clinical care. 

       

      The description of moderate injury remains unclear to me- specifically methods of determining "muscle sprain" and "laceration." Assisted falls, in which clinical staff slows decent to the floor to mitigate injury, can often result in skin tears (as does any physical mobilization to patients with fragile skin integrity). I  can imagine there is opportunity for misclassification as moderate level injury. I would like to learn more about what is classified as no injury and minor injury. 

       

      While fall events with injury are important for measurement and reporting- this must be paired with a measurement that encourages hospital standardized mobilization and activity programs for patients at all levels of fall risk. Otherwise, patients face higher risk of falling within immediate post-discharge recovery in home or post-acute environments.