Purpose: Studies suggest that implementing physical therapist discharge recommendations decreases 30-day hospital readmissions but may have miscounted readmissions. The purpose of this study was to obtain accurate readmission data and calculate the risk for readmission when therapists' recommendations are not followed. Methods: We conducted this prospective cohort study at a hospital in the Northwestern United States. Two hundred sixty-nine hospitalized participants with physical therapy orders consented to medical record data extraction and participated in a follow-up phone call. We compared physical therapist discharge recommendations to the actual discharge setting and services established for participants as documented in their medical record. Thirty days after discharge, we called participants to ask whether they had been readmitted to any hospital. We then analyzed associations between readmissions and the mismatch of therapist recommendations and participants' actual discharge locations and services received. Results: Twenty-one percent of discharges did not match the location and/or services recommended by the physical therapist. Sixty-six of the 269 participants were readmitted within 30 days. The odds for readmission were 2.3 times greater among participants who did not discharge to the location with the services recommended by their physical therapist. Conclusions: When physical therapist discharge location and services recommendations are not implemented, patients have increased risk of hospital readmission. This finding should inform stakeholders involved in discharge planning on the effect of following recommendations.

Objective: To discover if quality indicator (QI) codes are associated with patient falls in inpatient rehabilitation facilities (IRFs). Design: This retrospective cohort study explored differences between patients who fell and those who did not fall. We analyzed potential associations between QI codes and falls using univariable and multivariable logistic regression models.Setting: We collected data from electronic medical records at 4 IRFs. Participants: In 2020, our 4 data collection sites admitted and discharged a total of 1742 patients older than 14 years . We only excluded patients (N=43) from statistical analysis if they were discharged before admission data had been assigned. Main Outcome Measures: Using a data extraction report, we collected age, sex, race and ethnicity, diagnosis, falls, and QI codes for communication, self-care, and mobility performance. Staff documented communication codes on a 1-4 scale and self-care and mobility codes on a 1-6 scale, with higher codes representing greater independence.Results: Ninety-seven patients (5.71%) fell in the 4 IRFs over a 12-month period. The group who fell had lower QI codes for communication, selfcare, and mobility. When adjusting for bed mobility, transfer, and stair-climbing ability, low performance with understanding, walking 10 feet, and toileting were significantly associated with falls. Patients with admission QI codes below 4 for understanding had 78% higher odds of falling. If they were assigned admission QI codes below 3 for walking 10 feet or toileting, they had 2 times greater odds of falling. We did not find a significant association between falls and patients' diagnosis, age, sex, or race and ethnicity in our sample.Conclusions: Communication, self-care, and mobility QI codes appear to be significantly associated with falls. Future research should explore how to use these required codes to better identify patients likely to fall in IRFs. Archives of Physical Medicine and Rehabilitation 2023;104:1394-401 & COPY; 2023 by the American Congress of Rehabilitation Medicine.

OBJECTIVE:To create a fall risk assessment tool for inpatient rehabilitation facilities (IRFs) using available data and compare its predictive accuracy with that of the Morse Fall Scale (MFS).DESIGN:We conducted a secondary analysis from a retrospective cohort study. Using a nomogram that displayed the contributions of QI codes associated with falls in a multivariable logistic regression model, we created a novel fall risk assessment, the Inpatient Rehabilitation Fall Scale (IRF Scale). To compare the predictive accuracy of the IRF Scale and MFS, we used receiver operator characteristic (ROC) curve analysis.SETTING:We included data from 4 IRFs owned and operated by Intermountain Health.PARTICIPANTS:Data came from the medical records of 1699 patients. All participants were over the age of 14 and were admitted and discharged from 1 of the 4 sites between January 1 and December 31, 2020.INTERVENTIONS:Not applicable.MAIN OUTCOME MEASURE(S):We assigned point values on the IRF Scale based on the adjusted associations of QI codes with falls. Using ROC curve analysis, we discovered an optimal cutoff score, sensitivity, specificity, and overall AUC of the IRF Scale and MFS.RESULTS:ROC curve analysis revealed the optimal IRF Scale cutoff score of 22.4 yielded a sensitivity of 0.74 and a specificity of 0.63. With an AUC of 0.72, the IRF Scale demonstrated acceptable accuracy at identifying patients who fell in our retrospective cohort.CONCLUSIONS:Because the IRF Scale uses readily available data, it minimizes staff assessment and outperforms the MFS at identifying patients who previously fell. Prospective research is needed to investigate if it can adequately identify in advance which patients will fall during their IRF stay.

Objective Acute care physical therapists recommend discharge locations and services in part to help prevent falls during post-discharge recovery. Therapists may use standardized tests to inform their recommendation decisions, but evidence linking test scores with fall risk after discharge is lacking. The primary purpose of this study was to explore the associations between Tinetti Performance-Oriented Mobility Assessment (POMA) and Activity Measure for Post-Acute Care Inpatient Mobility Short Form (AM-PAC IMSF) scores and falls in the first 30 days after hospital discharge. Anticipating that agreement between therapist recommendations and discharge locations and services (discharge agreement), age, and sex could impact those associations, these factors were included in this investigation. Methods In this observational cohort study, 258 hospitalized patients consented to medical record data extraction and answered a phone survey 30 days after discharge to report whether they had experienced a fall since leaving the hospital. POMA and AM-PAC IMSF tests were administered for every patient. Participants’ age, sex, diagnosis, last POMA score, last AM-PAC IMSF score, physical therapist discharge recommendations, actual discharge location and services, discharge date, and phone number were collected from their medical records. Results When analyzed alone, higher POMA scores were associated with lower odds of falling, but the association was not significant after adjustment for other factors. Neither AM-PAC IMSF scores, age, nor sex were associated with falls. Discharge agreement, however, was associated with 59% lower odds of falling after adjustment for other factors. Conclusion Participants, who were discharged to the location with the services recommended by their physical therapist, were less likely to fall. Tinetti POMA and AM-PAC IMSF scores did not discriminate well participants who would fall. Impact Findings in this study inform those involved in discharge planning on the value of implementing physical therapist recommendations in reducing fall risk after hospital discharge. Lay Summary If physical therapist discharge recommendations are implemented, patients are less likely to fall during the month after hospital discharge. Balance and mobility test scores may provide therapists valuable information, but they are limited in their ability to identify who will fall after discharge.