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Objective To compare health care expenditures between older US adults (≥50 years) with pain who were prescribed opioid medications and those who were not. Design Cross-sectional. Setting Community-based adults in the 2015 Medical Expenditure Panel Survey (MEPS). Subjects Nationally representative sample of US adults alive for the calendar year, aged 50 years or older, who reported having pain in the past four weeks. Methods Older US adults (≥50 years) with pain in the 2015 MEPS data were identified. The key independent variable was opioid prescription status (prescribed opioid vs not prescribed opioid). Hierarchical linear regression models assessed health care expenditures (inpatient, outpatient, office-based, emergency room, prescription medications, other, and total) in US dollars for opioid prescription status from a community-dwelling US population perspective, adjusting for covariates. Results The 2015 study cohort provided a national estimate of 50,898,592 noninstitutionalized US adults aged ≥50 years with pain in the past four weeks (prescribed opioid N = 16,757,516 [32.9%], not prescribed opioid N = 34,141,076 [67.1%]). After adjusting for covariates, individuals prescribed an opioid had 61% greater outpatient (β = 0.477, P 0.05). Conclusions This study raises awareness of the economic impact associated with opioid use among US older adults with pain. Future research should investigate these variables in greater depth, over longer time periods, and in additional populations.

Purpose Documentation of Stage 1 and Stage 2 Meaningful Use (MU) criteria for patients with type 2 diabetes mellitus between clinical pharmacists and other healthcare providers was compared. Methods Patients seen at 8 clinics in a federally qualified health center who had type 2 diabetes mellitus and were seen by clinical pharmacists or other healthcare providers were randomly selected for inclusion in this retrospective study. Approximately half of the encounters were evaluated for Stage 1 MU compliance (encounters from October through December 2014), and half were evaluated for Stage 2 MU compliance (encounters from January through March 2015). Categorical and descriptive variables were analyzed by calculating frequencies and percentages. Chi-square tests were used to compare groups with an a priori level of significance set at 0.05. Results A total of 790 patients were seen by 46 providers, 8 (18%) of whom were clinical pharmacists. The study also included 24 medical doctors, 8 family nurse practitioners, 1 physician assistant, and 5 doctors of osteopathic medicine. Other healthcare providers more consistently documented 5 Stage 1 MU criteria than did clinical pharmacists. Clinical pharmacists more consistently documented 2 core objectives than did other healthcare providers. Otherwise, no significant differences in documentation were detected. Conclusion Other healthcare providers more consistently documented 5 Stage 1 MU criteria than did clinical pharmacists. Clinical pharmacists more consistently documented 2 core objectives than did other healthcare providers.

Background Arizona Medicaid developed a Health Information Exchange (HIE) system called the Arizona Medical Information Exchange (AMIE). Objective To evaluate physicians’ perceptions regarding AMIE’s impact on health outcomes and healthcare costs. Measurements A focus-group guide was developed and included five domains: perceived impact of AMIE on (1) quality of care; (2) workflow and efficiency; (3) healthcare costs; (4) system usability; and (5) AMIE data content. Qualitative data were analyzed using analytical coding. Results A total of 29 clinicians participated in the study. The attendance rate was 66% (N¼19) for the first and last month of focus-group meetings and 52% (N¼15) for the focus group meetings conducted during the second month. The benefits most frequently mentioned during the focus groups included: (1) identification of “doctor shopping”; (2) averting duplicative testing; and (3) increased efficiency of clinical information gathering. The most frequent disadvantage mentioned was the limited availability of data in the AMIE system. Conclusion Respondents reported that AMIE had the potential to improve care, but they felt that AMIE impact was limited due to the data available.

Objective Pharmacy clinical decision-support (CDS) software that contains drugedrug interaction (DDI) information may augment pharmacists’ ability to detect clinically significant interactions. However, studies indicate these systems may miss some important interactions. The purpose of this study was to assess the performance of pharmacy CDS programs to detect clinically important DDIs. Design Researchers made on-site visits to 64 participating Arizona pharmacies between December 2008 and November 2009 to analyze the ability of pharmacy information systems and associated CDS to detect DDIs. Software evaluation was conducted to determine whether DDI alerts arose from prescription orders entered into the pharmacy computer systems for a standardized fictitious patient. The fictitious patient’s orders consisted of 18 different medications including 19 drug pairsd13 of which were clinically significant DDIs, and six were non-interacting drug pairs. Measurements The sensitivity, specificity, positive predictive value, negative predictive value, and percentage of correct responses were measured for each of the pharmacy CDS systems. Results Only 18 (28%) of the 64 pharmacies correctly identified eligible interactions and non-interactions. The median percentage of correct DDI responses was 89% (range 47e100%) for participating pharmacies. The median sensitivity to detect well-established interactions was 0.85 (range 0.23e1.0); median specificity was 1.0 (range 0.83e1.0); median positive predictive value was 1.0 (range 0.88e1.0); and median negative predictive value was 0.75 (range 0.38e1.0). Conclusions These study results indicate that many pharmacy clinical decision-support systems perform less than optimally with respect to identifying well-known, clinically relevant interactions. Comprehensive system improvements regarding the manner in which pharmacy information systems identify potential DDIs are warranted.