Patient characteristics and neighborhood attributes associated with hepatitis C screening and positivity in Philadelphia.

Among patients of an urban primary care network in Philadelphia with a universal hepatitis C virus (HCV) screening policy for patients born during 1945-1965, we examined whether being unscreened and HCV positivity were associated with attributes of the census tracts where patients resided, which we considered as proxies for social health determinants. For patients with at least one clinic visit between 2014 and mid-2017, we linked demographic and HCV screening information from electronic health records with metrics that described the census tracts where patients resided. We used generalized estimating equations to estimate adjusted relative risk ratios (aRRs) for being unscreened and HCV positive. Overall, 28% of 6,906 patients were unscreened. Black race, male gender, and residence in census tracts with relatively high levels of violent crime, low levels of educational attainment and household incomes, and evidence of residential segregation by Hispanic ethnicity were associated with lower aRRs for being unscreened. Among screened patients, 9% were HCV positive. Factors associated with lower risks of being unscreened were, in general, associated with higher HCV positivity. Attributes of census tracts where patients reside are probably less apparent to clinicians than patients' gender or race but might reflect unmeasured patient characteristics that affected screening practices, along with preconceptions regarding the likelihood of HCV infection based on prior screening observations or implicit biases. Approaching complete detection of HCV-infected people would be hastened by focusing on residents of census tracts with attributes associated with higher infection levels or, if known, higher infection levels directly.

Addressing the Gap in Data Communication from Home Health Care to Primary Care during Care Transitions: Completeness of an Interoperability Data Standard.

In a future where home health care is no longer an information silo, patient information will be communicated along transitions in care to improve care. Evidence-based practice in the United States supports home health care patients to see their primary care team within the first two weeks of hospital discharge to reduce rehospitalization risk. We sought to identify a parsimonious set of home health care data to be communicated to primary care for the post-hospitalization visit. Anticipating electronic dataset communication, we investigated the completeness of the international reference terminology, Logical Observation Identifiers Names and Codes (LOINC), for coverage of the data to be communicated. We conducted deductive qualitative analysis in three steps: (1) identify home health care data available for the visit by mapping home health care to the information needed for the visit; (2) reduce the resulting home health care data set to a parsimonious set clinicians wanted for the post-hospitalization visit by eliciting primary care clinician input; and (3) map the parsimonious dataset to LOINC and assess LOINC completeness. Our study reduced the number of standardized home health care assessment questions by 40% to a parsimonious set of 33 concepts that primary care team physicians wanted for the post-hospitalization visit. Findings indicate all home health care concepts in the parsimonious dataset mapped to the information needed for the post-hospitalization visit, and 84% of the home health care concepts mapped to a LOINC term. The results indicate data flow of parsimonious home health care dataset to primary care for the post-hospitalization visit is possible using existing LOINC codes, and would require adding some codes to LOINC for communication of a complete parsimonious data set.

Neighborhood Effects on Missed Appointments in a Large Urban Academic Multispecialty Practice.

BACKGROUND: Missed appointments diminish the continuity and quality of care. OBJECTIVE: To determine whether missing scheduled appointments is associated with characteristics of the populations in places where patients reside. DESIGN: Retrospective cross-sectional study using data extracted from electronic health records linked to population descriptors for each patient's census tract of residence. PATIENTS: A total of 58,981 patients ≥18 years of age with 275,682 scheduled appointments during 2014-2015 at a multispecialty outpatient practice. MAIN MEASURES: We used multinomial generalized linear mixed models to examine associations between the outcomes of scheduled appointments (arrived, canceled, or missed) and selected characteristics of the populations in patients' census tracts of residence (racial/ethnic segregation based on population composition, levels of poverty, violent crime, and perceived safety and social capital), controlling for patients' age, gender, type of insurance, and type of clinic service. KEY RESULTS: Overall, 17.5% of appointments were missed. For appointments among patients residing in census tracts in the highest versus lowest quartile for each population metric, adjusted odds ratios (aORs) for missed appointments were 1.27 (CI 1.19, 1.35) for the rate of violent crime, 1.27 (CI 1.20, 1.34) for the proportion Hispanic, 1.19 (CI 1.12, 1.27) for the proportion living in poverty, 1.13 (CI 1.05, 1.20) for the proportion of the census tract population that was Black, and 1.06 (CI 1.01, 1.11 for perceived neighborhood safety. CONCLUSIONS: Characteristics of the places where patients reside are associated with missing scheduled appointments, including high levels of racial/ethnic segregation, poverty, and violent crime and low levels of perceived neighborhood safety. As such, targeting efforts to improve access for patients living in such neighborhoods will be particularly important to address underlying social determinants of access to health care.

Patient Health Goals Elicited During Home Care Admission: A Categorization.

Home care agencies are initiating "patient health goal elicitation" activities as part of home care admission planning. We categorized elicited goals and identified "clinically informative" goals at a home care agency. We examined patient goals that admitting clinicians documented in the point-of-care electronic health record; conducted content analysis on patient goal data to develop a coding scheme; grouped goal themes into codes; assigned codes to each goal; and identified goals that were in the patient voice. Of the 1,763 patient records, 16% lacked a goal; only 15 goals were in a patient's voice. Nurse and physician experts identified 12 of the 20 codes as clinically important accounting for 82% of goal occurrences. The most frequent goal documented was safety/falls (23%). Training and consistent communication of the intent and operationalization of patient goal elicitation may address the absence of patient voice and the less than universal recording of home care patients' goals.

The long-term financial impact of electronic health record implementation.

OBJECTIVE: To examine the financial impact of electronic health record (EHR) implementation on ambulatory practices. METHODS: We tracked the practice productivity (ie, number of patient visits) and reimbursement of 30 ambulatory practices for 2 years after EHR implementation and compared each practice to their pre-EHR implementation baseline. RESULTS: Reimbursements significantly increased after EHR implementation even though practice productivity (ie, the number of patient visits) decreased over the 2-year observation period. We saw no evidence of upcoding or increased reimbursement rates to explain the increased revenues. Instead, they were associated with an increase in ancillary office procedures (eg, drawing blood, immunizations, wound care, ultrasounds). DISCUSSION: The bottom line result-that EHR implementation is associated with increased revenues-is reassuring and offers a basis for further EHR investment. While the productivity losses are consistent with field reports, they also reflect a type of efficiency-the practices are receiving more reimbursement for fewer seeing patients. For the practices still seeing fewer patients after 2 years, the solution likely involves advancing their EHR functionality to include analytics. Although they may still see fewer patients, with EHR analytics, they can focus on seeing the right patients. CONCLUSIONS: Practice reimbursements increased after EHR implementation, but there was a long-term decrease in the number of patient visits seen in this ambulatory practice context.

Burden of Cardiovascular Disease among Multi-Racial and Ethnic Populations in the United States: an Update from the National Health Interview Surveys.

PURPOSE: The study aimed to provide new evidence of health disparities in cardiovascular disease (CVD) and diabetes mellitus (DM), and to examine their associations with lifestyle-related risk factors across the U.S. multi-racial and ethnic groups. METHODS: The analysis included a randomized population sample of 68,321 subjects aged ≥18 years old who participated in the U.S. 2012 and 2013 National Health Interview Surveys. Hypertension, coronary heart disease (CHD), stroke, and DM were classified according to participants' self-report of physician diagnosis. Assessments of risk factors were measured using standard survey instruments. Associations of risk factors with hypertension, CHD, stroke, and DM were analyzed using univariable and multivariable analysis methods. RESULTS: Non-Hispanic (NH)-Blacks had significantly higher odds of hypertension, while Hispanics had significantly lower odds of hypertension, and NH-Asians and Hispanics had significantly lower odds of stroke than NH-Whites (p < 0.001). All minority groups, NH-Blacks, NH-Asians, and Hispanics had significantly higher odds of DM, but they had significantly lower odds of CHD than NH-Whites (p < 0.001). Increased body weight, cigarette smoking, and physical inactivity were significantly associated with increased odds of hypertension, CHD, stroke, and DM (p < 0.001). However, the strengths of associations between lifestyle-related factors and the study outcomes were different across racial and ethnic groups. NH-Asians with BMI ≥30 kg/m(2) had the highest odds ratios (OR, 95% CI) for hypertension (5.37, 4.01-7.18), CHD (2.93, 1.90-4.52), and stroke (2.23, 1.08-4.61), and had the second highest odd ratios for DM (3.78, 2.68-5.35) than NH-Whites, NH-Blacks, and Hispanics. CONCLUSION: CVD and DM disproportionately affect the U.S. multi-racial and ethnic population. Although lifestyle-related risk factors are significantly associated with increased odds of CVD and DM, the magnitudes of these associations are different by race and ethnicity.