Dual Medicare and Veteran Health Administration use and ambulatory care sensitive hospitalizations.

OBJECTIVE: The objective of the study is to examine the association between ambulatory care sensitive hospitalizations (ACSH) and dual Medicare/Veteran Health Administration use. PARTICIPANTS: A nationally representative sample of Medicare beneficiaries, who participated in the Medicare Current Beneficiary Survey (MCBS). DESIGN/MEASUREMENTS: Cross-sectional analyses (N = 44,988) of linked fee-for-service Medicare claims and survey data from multiple years of the MCBS (2001-2005). Any ACSH and specific types of ACSH were measured using the list of prevention quality indicators developed by the Agency for Healthcare Research and Quality. Among veterans, dual Medicare/VHA use was defined as having inpatient or outpatient visits paid by VHA and consisted of three categories: 1) predominant-VHA use; 2) some VHA use and no VHA use. Unadjusted group differences in any ACSH were tested using chi-square tests. Logistic regressions were used to analyze the association between dual Medicare/VHA use and ACSH after controlling for demographic, socio-economic status, health status, functional status, smoking status and obesity. All analyses accounted for the complex design of the MCBS. RESULTS: Among inpatient users, 10.1% had ACSH events for acute conditions and 15.8% for chronic conditions. Among all survey respondents, 5% had any ACSH event. Among predominant-VHA users the rate was 4.9% and among veterans with some VHA use it was 3.7%. In bivariate and multivariate analyses, dual Medicare/VHA use was not significantly associated with any ACSH. CONCLUSION: In a representative sample of Medicare beneficiaries, despite low income and health status, veterans with dual Medicare/VHA use were as likely as veterans without dual use to have any ACSH, perhaps due to expanded healthcare access and emphasis on primary care in the VHA system.

Primary Care Provider Perceptions and Practices Regarding Dosing Units for Oral Liquid Medications.

INTRODUCTION: To prevent errors, health care professional and safety organizations recommend using milliliters (mL) alone for oral liquid medication dosing instructions and devices. In 2018, for federal incentives under the Quality Payment Program, one requirement is for electronic health records to automatically use mL alone whenever oral liquid medications are prescribed. Current perceptions and practices of primary care providers (PCPs) regarding dosing units for oral liquid medications were assessed. METHODS: Pediatricians, family practitioners, nurse practitioners, and internists participating in the 2015 DocStyles Web-based survey were asked about their perceptions and practices regarding dosing units for oral liquid medications. RESULTS: Three fifths of PCPs (59.0%) reported that using mL alone is safest for dosing oral liquid medications; however, nearly three quarters (72.0%) thought that patients/caregivers prefer instructions that include spoon-based units. Within each specialty, fewer PCPs reported they would prescribe using mL alone than reported that using mL alone is safest (P < .0001 for all). Among PCPs who think milliliter-only dosing is safest, those who perceived patients/caregivers prefer including spoon-based units were less likely to prescribe using mL alone (odds ratio 0.45, 95% confidence interval 0.34-0.59). Pediatricians were more likely than other PCPs to report that it is safest to use mL alone (80.8% vs 54.7%) and that they would use mL alone when prescribing (56.8% vs 30.9%) (P < .0001 for both). CONCLUSIONS: Because less than two thirds of pediatricians and one third of other PCPs would use mL alone in dosing instructions, additional education to encourage prescribing and communicating with patients/caregivers using mL alone may be needed.

Ambulatory Care Sensitive Hospitalizations among Medicaid Beneficiaries with Chronic Conditions.

OBJECTIVES: This study examined the relationship between ambulatory care sensitive hospitalizations (ACSH) and patient-level and county-level variables. METHODS: Utilizing a retrospective cohort approach, multi-state Medicaid claims data from 2007-2008 was used to examine ACSH at baseline and follow-up periods. The study cohort consisted of adult, non-elderly Medicaid beneficiaries with chronic physical conditions, who were continuously enrolled in fee-for-service programs, not enrolled in Medicare, and did not die during the study period (N=7,021). The dependent variable, ACSH, was calculated in the follow-up year using an algorithm from the Agency for Healthcare Research and Quality algorithm. Patient-level (demographic, health status, continuity of care) and county-level (density of healthcare providers and facilities, socio-economic characteristics, local economic conditions) factors were included as independent variables. Multivariable logistic regression models were used to examine the relationship between ACSH and independent variables. RESULTS: In this study population, 8.2% had an ACSH. African-Americans were more likely to have an ACSH [AOR=1.55, 95% CI 1.16, 2.07] than Caucasians. Adults with schizophrenia were more likely to have an ACSH, compared to those without schizophrenia [AOR=1.54, 95% CI 1.16, 2.04]. Residents in counties with a higher number of community mental health centers [AOR=0.88, 95% CI 0.80, 0.97] and rural health centers [AOR=0.98, 95% CI 0.95, 0.99] were less likely to have an ASCH. CONCLUSIONS: Programs and interventions designed to reduce the risk of ACSH may be needed to target specific population subgroups and improve healthcare infrastructure.

Hospital length of stay and all-cause 30-day readmissions among high-risk Medicaid beneficiaries.

This study examined the association between index hospitalization characteristics and the risk of all-cause 30-day readmission among high-risk Medicaid beneficiaries using multilevel analyses. A retrospective cohort with a baseline and a follow-up period was used. The study population consisted of Medicaid beneficiaries (21-64 years old) with selected chronic conditions, continuous fee-for-service enrollment through the observation period, and at least 1 inpatient encounter during the follow-up period (N = 15,806). The outcome of 30-day readmission was measured using inpatient admissions within 30-days from the discharge date of the first observed hospitalization. Key independent variables included length of stay, reason for admission, and month of index hospitalization (seasonality). Multilevel logistic regression that accounted for beneficiaries nested within counties was used to examine this association, after controlling for patient-level and county-level characteristics. In this study population, 16.7% had all-cause 30-day readmissions. Adults with greater lengths of stay during the index hospitalization were more likely to have 30-day readmissions (adjusted odds ratio [AOR]: 1.03, 95% confidence interval [CI]: 1.02-1.04). Adults who were hospitalized for cardiovascular conditions (AOR: 1.20, 95% CI: 1.08-1.33), diabetes (AOR: 1.23, 95% CI: 1.10-1.39), cancer (AOR: 1.55, 95% CI: 1.26-1.90), and mental health conditions (AOR: 2.17, 95% CI: 1.98-2.38) were more likely to have 30-day readmissions compared to those without these conditions.

Dispatch from the non-HITECH-incented Health IT world: electronic medication history adoption and utilization.

OBJECTIVE: To document national trends of electronic medication history use in the ambulatory setting and describe the characteristics and predicting factors of providers who regularly use medication history transaction capabilities through their e-prescribing systems. MATERIALS AND METHODS: The study used provider-initiated medication history data requests, electronically sent over an e-prescribing network from all 50 states and the District of Columbia. Data from 138,000 prescribers were evaluated using multivariate analyses from 2007 to 2013. RESULTS: Medication history use showed significant growth, increasing from 8 to 850 million history requests during the study period. Prescribers on the network for <5 years had a lower likelihood of requests than those on the network for 5 or more years. Although descriptive analyses showed that prescribers in rural areas were alongside e-prescribing, and requesting medication histories more often than those in large and small cities, these findings were not significant in multivariate analyses. Providers in orthopedic surgery and internal medicine had a higher likelihood of more requests than family practice prescribers, with 12% and 7% higher likelihood, respectively. DISCUSSION: Early adopters of e-prescribing have remained medication history users and have continually increased their volume of requests for medication histories. CONCLUSION: Despite the fact that the use of medication histories through e-prescribing networks in the ambulatory care setting has not been encouraged through federal incentive programs, there has been substantial growth in the use of medication histories offered through e-prescribing networks.

Consensus recommendations for systematic evaluation of drug-drug interaction evidence for clinical decision support.

BACKGROUND: Healthcare organizations, compendia, and drug knowledgebase vendors use varying methods to evaluate and synthesize evidence on drug-drug interactions (DDIs). This situation has a negative effect on electronic prescribing and medication information systems that warn clinicians of potentially harmful medication combinations. OBJECTIVE: The aim of this study was to provide recommendations for systematic evaluation of evidence for DDIs from the scientific literature, drug product labeling, and regulatory documents. METHODS: A conference series was conducted to develop a structured process to improve the quality of DDI alerting systems. Three expert workgroups were assembled to address the goals of the conference. The Evidence Workgroup consisted of 18 individuals with expertise in pharmacology, drug information, biomedical informatics, and clinical decision support. Workgroup members met via webinar 12 times from January 2013 to February 2014. Two in-person meetings were conducted in May and September 2013 to reach consensus on recommendations. RESULTS: We developed expert consensus answers to the following three key questions. (i) What is the best approach to evaluate DDI evidence? (ii) What evidence is required for a DDI to be applicable to an entire class of drugs? (iii) How should a structured evaluation process be vetted and validated? CONCLUSION: Evidence-based decision support for DDIs requires consistent application of transparent and systematic methods to evaluate the evidence. Drug compendia and clinical decision support systems in which these recommendations are implemented should be able to provide higher-quality information about DDIs.

Adoption of electronic prescribing for controlled substances among providers and pharmacies.

OBJECTIVES: Electronic prescribing for Schedule II through V controlled substances was legalized in the United States by the Drug Enforcement Administration in June 2010. However, little information exists about adoption and use of the electronic prescribing of controlled substances (EPCS) at the national level. Therefore, the objective of this study is to present the first information about national trends surrounding the adoption and use of the newly allowed EPCS by providers and pharmacies in the United States. STUDY DESIGN: Trends of EPCS adoption and use were examined for the number of EPCS, number of pharmacies enabled to accept EPCS, and the number of providers prescribing controlled substances electronically. METHODS: Using nationally representative transactional Surescripts data from July 2012 to December 2013, we examined EPCS trends. RESULTS: During the study period, the total number of EPCS increased from 1535 to 52,423, and the number and percentage of all pharmacies enabled for EPCS increased from 8768 (13%) to 20,498 pharmacies (30%). The proportion of all providers prescribing controlled substances electronically is currently 1%, but increasing steadily each month. CONCLUSIONS: There is a positive national growth for EPCS in pharmacy preparedness to accept EPCS, the number of EPCS prescriptions sent each month, and the number of providers with the ability to send EPCS.

Changing BMI categories and healthcare expenditures among elderly Medicare beneficiaries.

To examine the association between changes in BMI categories and health-care expenditures among elderly Medicare beneficiaries using longitudinal data of the Medicare Current Beneficiary Survey (MCBS) 2000-2005. Changes in BMI were (i) Stayed Normal: individuals with a normal BMI at baseline and follow-up; (ii) Stayed Overweight individuals with overweight BMI at baseline and follow-up; (iii) Stayed Obese individuals with obese BMI at baseline and follow-up; (iv) Normal-Overweight: individuals with normal BMI at baseline and overweight BMI at follow-up; (v) Overweight-Obese: individuals with overweight BMI at baseline and obese BMI at follow-up; (vi) Overweight-Normal: individuals with overweight BMI at baseline and normal BMI at follow-up; (vii) Obese-Overweight: individuals with obese BMI at baseline and overweight BMI at follow-up. Ordinary Least Squares (OLS) models on logged Year 3 expenditures were used to analyze changes in expenditures between BMI categories. Overall, 35% Stayed Normal, 34% Stayed Overweight, 18% Stayed Obese, 4% gained weight from Normal-Overweight BMI, 3% gained weight from Overweight-Obese BMI, 5% lost weight from Overweight-Normal BMI, and 3% lost weight from Obese-Overweight BMI. Adjusted models revealed those who Stayed Obese had increased total and multiple expenditure types that were significantly higher than Stayed Normal including total (11%), outpatient (25%), prescription (9%), and medical provider (4%). Compared to Stayed Normal, total expenditures were both 26% higher for Obese-Overweight and Overweight-Obese. The current findings highlight the importance of maintaining a normal BMI in the elderly.

Multimorbidity, Mental Illness, and Quality of Care: Preventable Hospitalizations among Medicare Beneficiaries.

Background. Individuals with multimorbidity are vulnerable to poor quality of care due to issues related to care coordination. Ambulatory care sensitive hospitalizations (ACSHs) are widely accepted quality indicators because they can be avoided by timely, appropriate, and high-quality outpatient care. Objective. To examine the association between multimorbidity, mental illness, and ACSH. Study Design. We used a longitudinal panel design with data from multiple years (2000-2005) of Medicare Current Beneficiary Survey. Individuals were categorized into three groups: (1) multimorbidity with mental illness (MM/MI); (2) MM/no MI; (3) no MM. Multivariable logistic regressions were used to analyze the association between multimorbidity and ACSH. Results. Any ACSH rates varied from 10.8% in MM/MI group to 8.8% in MM/No MI group. Likelihood of any ACSH was higher among beneficiaries with MM/MI (AOR = 1.62; 95% CI = 1.14, 2.30) and MM (AOR = 1.54; 95% CI = 1.12, 2.11) compared to beneficiaries without multimorbidity. There was no statistically significant difference in likelihood of ACSH between MM/MI and MM/No MI groups. Conclusion. Multimorbidity (with or without MI) had an independent and significant association with any ACSH. However, presence of mental illness alone was not associated with poor quality of care as measured by ACSH.

Antidepressant use, depression, lifestyle factors, and new-onset diabetes.

We assessed the short-term association between antidepressant drug use and the risk of new-onset diabetes in 2-years of observation. This study used longitudinal data from the Medical Expenditure Panel Survey for years 2004-2007. Chi-square tests and logistic regressions were used to examine the link between use of antidepressants with and without depression, and new-onset diabetes, after controlling for independent variables in blocks. In unadjusted models, the risk of new-onset diabetes was significantly increased for persons using antidepressants with depression compared with those without antidepressant use or depression [odds ratio (OR)=2.12, 95% confidence interval (CI): 1.45-3.09]. When lifestyle risk factors were entered in the model, statistical significance disappeared [adjusted OR (AOR)=1.42, 95% CI: 0.98-2.08]. Independently, lifestyle risk factors significantly increased the risk of new-onset diabetes: hypertension (AOR=2.55, 95% CI: 1.86-3.50, P<0.001), lipid disorders (AOR=1.60, 95% CI: 1.14-2.24), overweight (AOR=2.01, 95% CI: 1.35-2.98), obesity (AOR=3.57, 95% CI: 2.50-5.10), and no physical exercise (AOR=1.98, 95% CI: 1.53-2.57, P<0.001). Future studies on the risk of new-onset diabetes by duration and intensity of antidepressant use and depression are needed.