Randomized trial of a health IT tool to support between-visit-based laboratory monitoring for chronic disease medication prescriptions.

BACKGROUND: Lack of timely medication intensification and inadequate medication safety monitoring are two prevalent and potentially modifiable barriers to effective and safe chronic care. Innovative applications of health information technology tools may help support chronic disease management. OBJECTIVE: To examine the clinical impact of a novel health IT tool designed to facilitate between-visit ordering and tracking of future laboratory testing. DESIGN AND PARTICIPANTS: Clinical trial randomized at the provider level (n = 44 primary care physicians); patient-level outcomes among 3,655 primary care patients prescribed 5,454 oral medicines for hyperlipidemia, diabetes, and/or hypertension management over a 12-month period. MAIN MEASURES: Time from prescription to corresponding follow-up laboratory testing; proportion of follow-up time that patients achieved corresponding risk factor control (A1c, LDL); adverse event laboratory monitoring 4 weeks after medicine prescription. KEY RESULTS: Patients whose physicians were allocated to the intervention (n = 1,143) had earlier LDL laboratory assessment compared to similar patients (n = 703) of control physicians [adjusted hazard ratio (aHR): 1.15 (1.01-1.32), p = 0.04]. Among patients with elevated LDL (486 intervention, 324 control), there was decreased time to LDL goal in the intervention group [aHR 1.26 (0.99-1.62)]. However, overall there were no significant differences between study arms in time spent at LDL or HbA1c goal. Follow-up safety monitoring (e.g., creatinine, potassium, or transaminases) was relatively infrequent (ranging from 7 % to 29 % at 4 weeks) and not statistically different between arms. Intervention physicians indicated that lack of reimbursement for non-visit-based care was a barrier to use of the tool. CONCLUSIONS: A health IT tool to support between-visit laboratory monitoring improved the LDL testing interval but not LDL or HbA1c control, and it did not alter safety monitoring. Adoption of innovative tools to support physicians in non-visit-based chronic disease management may be limited by current visit-based financial and productivity incentives.

A "Hospital-Day-1" Model to Predict the Risk of Discharge to a Skilled Nursing Facility.

OBJECTIVE: To derive and validate a model to predict a patient's probability of skilled nursing facility (SNF) discharge using data available from day 1 of hospitalization. DESIGN: Using a retrospective cohort of 11,380 hospitalized patients, we obtained administrative and electronic medical data to identify predictors of SNF discharge. SETTING AND PARTICIPANTS: Single, urban academic medical center. Patients older than 50 years admitted to the medical service from July 2014 to August 2015. METHODS: Primary outcome defined as SNF discharge. We split the cohort into derivation and validation sets (80/20). We created 1000 bootstrapped samples of the derivation set and used backward selection logistic regression on each bootstrapped sample. The final model included variables selected in ≥60% of the samples. To create a point-based index, a point value was assigned to each predictor variable relative to the logistic regression coefficient. The model's discrimination, calibration, positive predictive value, and negative predictive value tested in the validation set. RESULTS: The overall frequency of SNF discharge was 12%. The final model included 11 variables. Significant demographic variables included age, marital status, insurance type, living alone, residence, and distance from hospital. The final model included 2 significant functional variables (mobility, bathing) and 3 significant clinical variables (admission mode, admission diagnosis, admission day of week). Impairment in mobility [odds ratio (OR) 1.8, 95% confidence interval (CI) 1.4-2.2] and impairment in bathing (OR 1.9, 95% CI 1.6-2.4) were both significant predictors of SNF discharge. The final model discriminated well in the validation cohort (c-statistic = 0.82) and was well calibrated. CONCLUSIONS/IMPLICATIONS: It is possible to predict the day of admission with good accuracy and clinical usability a patient's risk of SNF discharge. The ability to identify early in the hospitalization patients likely to use post-acute services has implications for clinicians, administrators, and policy makers working to improve discharge planning and care transitions.

Living Alone and Discharge to Skilled Nursing Facility Care after Hospitalization in Older Adults.

BACKGROUND/OBJECTIVES: Community-based older adults are increasingly living alone. When they become ill, they might need greater support from the healthcare system than would those who live with others. There also has been a growing concern about the high use of postacute care such as skilled nursing facility (SNF) care and the level of variation in this use between hospitals and regions. Our objective was to examine whether living alone contributed to the risk of being discharged to a SNF. DESIGN: Retrospective cohort study. SETTING: Massachusetts General Hospital. PARTICIPANTS: Community-dwelling individuals aged 50 and older admitted to the medical service and discharged alive between July 2014 and August 2015 (N = 7,029). MEASUREMENTS: We extracted demographic, clinical, and functional data from the electronic medical record and used multivariable logistic regression to determine whether living alone at the time of hospitalization was associated with subsequent discharge to a SNF. RESULTS: Of eligible individuals, 24.8% reported living alone before admission. Those living alone were more likely to be female, older, and more independent before admission than those living with others. Of all participants, 10.9% were discharged to a SNF. After adjustment, participants living alone had more than twice the odds of being discharged to a SNF (odds ratio = 2.23, 95% confidence interval = 1.85-2.69, P < .001). DISCUSSION: People living alone are more likely to be discharged to SNFs, even when compared to other individuals with similar levels of clinical complexity and functional status. To the extent that this variation is due to a lack of home support, it could be possible to reduce SNF use through additional home services after hospital discharge.