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Objective: To examine sociodemographic factors associated with having unmet needs in medications, mental health, and food security among older adults during the COVID‐19 pandemic. Data Sources and Study Setting: Primary data and secondary data from the electronic health records (EHR) in an age‐friendly academic health system in 2020 were used. Study Design: Observational study examining factors associated with having unmet needs in medications, food, and mental health. Data Collecting/Extraction Methods: Data from a computer‐assisted telephone interview and EHR on community‐dwelling older patients were analyzed. Principle Findings: Among 3400 eligible patients, 1921 (53.3%) (average age 76, SD 11) responded, with 857 (45%) of respondents having at least one unmet need. Unmet needs for medications were present in 595 (31.0%), for food in 196 (10.2%), and for mental health services in 292 (15.2%). Racial minorities had significantly higher probabilities of having unmet needs for medicine and food, and of being referred for services related to medications, food, and mental health. Patients living in more resource‐limited neighborhoods had a higher probability of being referred for mental health services. Conclusions: Age‐friendly health systems (AFHS) and their recognition should include assessing and addressing social risk factors among older adults. Proactive efforts to address unmet needs should be integral to AFHS. [ABSTRACT FROM AUTHOR]

Objectives: To assess the reliability of data in electronic health records (EHRs) for measuring processes of care among primary care physicians (PCPs) and examine the relationship between these measures and clinical outcomes.Data Sources/study Setting: EHR data from 15,370 patients with diabetes, 49,561 with hypertension, in a group practice serving four Northern California counties.Study Design/methods: Exploratory factor analysis (EFA) and multilevel analyses of the relationships between processes of care variables and factor scales with control of hemoglobin A1c, blood pressure (BP), and low density lipoprotein (LDL) among patients with diabetes and BP among patients with hypertension.Principal Findings: Volume of e-messages, number of days to the third-next-available appointment, and team communication emerged as reliable factors of PCP processes of care in EFA (Cronbach's alpha=0.73, 0.62, and 0.91). Volume of e-messages was associated with higher odds of LDL control (≤100) (OR=1.13, p<.05) among patients with diabetes. Frequent in-person visits were associated with better BP (OR=1.02, p<.01) and LDL control (OR=1.01, p<.01) among patients with diabetes, and better BP control (OR=1.04, p<.01) among patients with hypertension.Conclusions: The EHR offers process of care measures which can augment patient-reported measures of patient-centeredness. Two of them are significantly associated with clinical outcomes. Future research should examine their association with additional outcomes. [ABSTRACT FROM AUTHOR]

Objectives. To assess the reliability of data in electronic health records (EHRs) for measuring processes of care among primary care physicians (PCPs) and examine the relationship between these measures and clinical outcomes. Data Sources/Study Setting. EHR data from 15,370 patients with diabetes, 49,561 with hypertension, in a group practice serving four Northern California counties. Study Design/Methods. Exploratory factor analysis (EFA) and multilevel analyses of the relationships between processes of care variables and factor scales with control of hemoglobin A1c, blood pressure (BP), and low density lipoprotein (LDL) among patients with diabetes and BP among patients with hypertension. Principal Findings. Volume of e-messages, number of days to the third-next-available appointment, and team communication emerged as reliable factors of PCP processes of care in EFA (Cronbach’s alpha = 0.73, 0.62, and 0.91). Volume of e-messages was associated with higher odds of LDL control (≤100) (OR = 1.13, p < .05) among patients with diabetes. Frequent in-person visits were associated with better BP (OR = 1.02, p < .01) and LDL control (OR = 1.01, p < .01) among patients with diabetes, and better BP control (OR = 1.04, p < .01) among patients with hypertension. Conclusions. The EHR offers process of care measures which can augment patient-reported measures of patient-centeredness. Two of them are significantly associated with clinical outcomes. Future research should examine their association with additional outcomes. [ABSTRACT FROM AUTHOR]

Objectives. To use an innovative videotape analysis method to examine how clinic time was spent during elderly patients' visits to primary care physicians. Secondary objectives were to identify the factors that influence time allocations. Data Sources. A convenience sample of 392 videotapes of routine office visits conducted between 1998 and 2000 from multiple primary care practices in the United States, supplemented by patient and physician surveys. Research Design. Videotaped visits were examined for visit length and time devoted to specific topics—a novel approach to study time allocation. A survival analysis model analyzed the effects of patient, physician, and physician practice setting on how clinic time was spent. Principal Findings. Very limited amount of time was dedicated to specific topics in office visits. The median visit length was 15.7 minutes covering a median of six topics. About 5 minutes were spent on the longest topic whereas the remaining topics each received 1.1 minutes. While time spent by patient and physician on a topic responded to many factors, length of the visit overall varied little even when contents of visits varied widely. Macro factors associated with each site had more influence on visit and topic length than the nature of the problem patients presented. Conclusions. Many topics compete for visit time, resulting in small amount of time being spent on each topic. A highly regimented schedule might interfere with having sufficient time for patients with complex or multiple problems. Efforts to improve the quality of care need to recognize the time pressure on both patients and physicians, the effects of financial incentives, and the time costs of improving patient–physician interactions. [ABSTRACT FROM AUTHOR]