Implementation and cost validation of a real-time benefit tool.

OBJECTIVES: To assess the accuracy of a real-time benefit tool (RTBT) that is compliant with the standards of the National Council for Prescription Drug Programs (NCPDP) in a large academic medical center. STUDY DESIGN: Observational study of electronic health records and pharmacy records from July 14, 2019, through January 14, 2020, across all ambulatory clinics and outpatient pharmacies in the health system. METHODS: Main assessments included (1) demographic characteristics of patients in whom the RTBT was used and those in whom it was not used, (2) types of changes most frequently made to medication orders upon reviewing the RTBT, and (3) comparison of the out-of-pocket costs for prescriptions vs the RTBT-generated estimates. RESULTS: The most common modifications made to prescriptions due to RTBT use were changes in days' supply (44%) and the quantity of medication (69%). In more than 98% of prescription orders, patients' out-of-pocket costs were either equivalent to or lower than the estimates generated by the RTBT. CONCLUSIONS: Current standards established by NCPDP yield accurate patient out-of-pocket estimates and could serve as a national standard for all Part D sponsors.

The perceived impact of the COVID-19 pandemic on medical students' future careers.

Background: The COVID-19 pandemic disrupted medical education on multiple levels, and medical students have been forced to adjust to distance learning, altered clinical opportunities, and standardized testing inconsistencies. We sought to identify the effects of these dramatic deviations on medical students' career plans. Methods: We conducted a cross-sectional online survey of medical students between July 13, 2020, and September 9, 2020 in order to assess the implications of the COVID-19 pandemic on students' career decisions. Descriptive statistics were calculated for all variables. Results: Of the 585 eligible medical students, we had a final sample of 76 responses (n=76) (13% response rate). Students felt neutral regarding having more time to explore research projects (Mean ± SD; 3.06 ± 1.18) and hobbies (3.43 ± 1.28). Most survey respondents somewhat disagreed that they considered quitting medical school during the pandemic (1.55 ± 1.10). Students somewhat agreed that they view the field of medicine more positively since the onset of the COVID-19 pandemic (3.60 ± 1.09). Respondents somewhat agreed that they would be unable to explore other specialties and find their best fit (3.55 ± 1.32). We found that the minority (4/66, 6%) of students had considered changing their specialty. Students felt neutral in terms of their Step 1 (3.25 ± 1.05) or Step 2 (2.81 ± 1.02) score deterring them from future career opportunities. Conclusions:  Most medical students have experienced barriers in their career pathway as a direct cause of COVID-19 restrictions on medical education, including the ability to explore different specialties to discover their best fit or find a chance to network with mentors. However, despite these obstacles, most students remain committed to medicine.

The Value of Unstructured Electronic Health Record Data in Geriatric Syndrome Case Identification.

OBJECTIVES: To examine the value of unstructured electronic health record (EHR) data (free-text notes) in identifying a set of geriatric syndromes. DESIGN: Retrospective analysis of unstructured EHR notes using a natural language processing (NLP) algorithm. SETTING: Large multispecialty group. PARTICIPANTS: Older adults (N=18,341; average age 75.9, 58.9% female). MEASUREMENTS: We compared the number of geriatric syndrome cases identified using structured claims and structured and unstructured EHR data. We also calculated these rates using a population-level claims database as a reference and identified comparable epidemiological rates in peer-reviewed literature as a benchmark. RESULTS: Using insurance claims data resulted in a geriatric syndrome prevalence ranging from 0.03% for lack of social support to 8.3% for walking difficulty. Using structured EHR data resulted in similar prevalence rates, ranging from 0.03% for malnutrition to 7.85% for walking difficulty. Incorporating unstructured EHR notes, enabled by applying the NLP algorithm, identified considerably higher rates of geriatric syndromes: absence of fecal control (2.1%, 2.3 times as much as structured claims and EHR data combined), decubitus ulcer (1.4%, 1.7 times as much), dementia (6.7%, 1.5 times as much), falls (23.6%, 3.2 times as much), malnutrition (2.5%, 18.0 times as much), lack of social support (29.8%, 455.9 times as much), urinary retention (4.2%, 3.9 times as much), vision impairment (6.2%, 7.4 times as much), weight loss (19.2%, 2.9 as much), and walking difficulty (36.34%, 3.4 as much). The geriatric syndrome rates extracted from structured data were substantially lower than published epidemiological rates, although adding the NLP results considerably closed this gap. CONCLUSION: Claims and structured EHR data give an incomplete picture of burden related to geriatric syndromes. Geriatric syndromes are likely to be missed if unstructured data are not analyzed. Pragmatic NLP algorithms can assist with identifying individuals at high risk of experiencing geriatric syndromes and improving coordination of care for older adults.

Defining and Assessing Geriatric Risk Factors and Associated Health Care Utilization Among Older Adults Using Claims and Electronic Health Records.

BACKGROUND: Using electronic health records (EHRs), in addition to claims, to systematically identify patients with factors associated with adverse outcomes (geriatric risk) among older adults can prove beneficial for population health management and clinical service delivery. OBJECTIVE: To define and compare geriatric risk factors derivable from claims, structured EHRs, and unstructured EHRs, and estimate the relationship between geriatric risk factors and health care utilization. RESEARCH DESIGN: We performed a retrospective cohort study of patients enrolled in a Medicare Advantage plan from 2011 to 2013 using both administrative claims and EHRs. We defined 10 individual geriatric risk factors and a summary geriatric risk index based on diagnosed conditions and pattern matching techniques applied to EHR free text. The prevalence of geriatric risk factors was estimated using claims, structured EHRs, and structured and unstructured EHRs combined. The association of geriatric risk index with any occurrence of hospitalizations, emergency department visits, and nursing home visits were estimated using logistic regression adjusted for demographic and comorbidity covariates. RESULTS: The prevalence of geriatric risk factors increased after adding unstructured EHR data to structured EHRs, compared with those derived from structured EHRs alone and claims alone. On the basis of claims, structured EHRs, and structured and unstructured EHRs combined, 12.9%, 15.0%, and 24.6% of the patients had 1 geriatric risk factor, respectively; 3.9%, 4.2%, and 15.8% had ≥2 geriatric risk factors, respectively. Statistically significant association between geriatric risk index and health care utilization was found independent of demographic and comorbidity covariates. For example, based on claims, estimated odds ratios for having 1 and ≥2 geriatric risk factors in year 1 were 1.49 (P<0.001) and 2.62 (P<0.001) in predicting any occurrence of hospitalizations in year 1, and 1.32 (P<0.001) and 1.34 (P=0.003) in predicting any occurrence of hospitalizations in year 2. CONCLUSIONS: The results demonstrate the feasibility and potential of using EHRs and claims for collecting new types of geriatric risk information that could augment the more commonly collected disease information to identify and move upstream the management of high-risk cases among older patients.

Comparing clinician descriptions of frailty and geriatric syndromes using electronic health records: a retrospective cohort study.

BACKGROUND: Geriatric syndromes, including frailty, are common in older adults and associated with adverse outcomes. We compared patients described in clinical notes as "frail" to other older adults with respect to geriatric syndrome burden and healthcare utilization. METHODS: We conducted a retrospective cohort study on 18,341 Medicare Advantage enrollees aged 65+ (members of a large nonprofit medical group in Massachusetts), analyzing up to three years of administrative claims and structured and unstructured electronic health record (EHR) data. We determined the presence of ten geriatric syndromes (falls, malnutrition, dementia, severe urinary control issues, absence of fecal control, visual impairment, walking difficulty, pressure ulcers, lack of social support, and weight loss) from claims and EHR data, and the presence of frailty descriptions in clinical notes with a pattern-matching natural language processing (NLP) algorithm. RESULTS: Of the 18,341 patients, we found that 2202 (12%) were described as "frail" in clinical notes. "Frail" patients were older (82.3 ± 6.8 vs 75.9 ± 5.9, p < .001) and had higher rates of healthcare utilization, including number of inpatient hospitalizations and emergency department visits, than the rest of the population (p < .001). "Frail" patients had on average 4.85 ± 1.72 of the ten geriatric syndromes studied, while non-frail patients had 2.35 ± 1.71 (p = .013). Falls, walking difficulty, malnutrition, weight loss, lack of social support and dementia were more highly correlated with frailty descriptions. The most common geriatric syndrome pattern among "frail" patients was a combination of walking difficulty, lack of social support, falls, and weight loss. CONCLUSIONS: Patients identified as "frail" by providers in clinical notes have higher rates of healthcare utilization and more geriatric syndromes than other patients. Certain geriatric syndromes were more highly correlated with descriptions of frailty than others.