Bridging the integration gap between patient-generated blood glucose data and electronic health records.

Telemedicine can facilitate population health management by extending the reach of providers to efficiently care for high-risk, high-utilization populations. However, for telemedicine to be maximally useful, data collected using telemedicine technologies must be reliable and readily available to healthcare providers. To address current gaps in integration of patient-generated health data into the electronic health record (EHR), we examined 2 patient-facing platforms, Epic MyChart and Apple HealthKit, both of which facilitated the uploading of blood glucose data into the EHR as part of a diabetes telemedicine intervention. All patients were offered use of the MyChart platform; we subsequently invited a purposive sample of patients who used the MyChart platform effectively (n = 5) to also use the Apple HealthKit platform. Patients reported both platforms helped with diabetes self-management, and providers appreciated the convenience of the processes for obtaining patient data. Providers stated that the EHR data presentation format for Apple HealthKit was challenging to interpret; however, they also valued the greater perceived accuracy the Apple HealthKit data. Our findings indicate that patient-facing platforms can feasibly facilitate transmission of patient-generated health data into the EHR and support telemedicine-based care.

Assessing electronic health record phenotypes against gold-standard diagnostic criteria for diabetes mellitus.

OBJECTIVE: We assessed the sensitivity and specificity of 8 electronic health record (EHR)-based phenotypes for diabetes mellitus against gold-standard American Diabetes Association (ADA) diagnostic criteria via chart review by clinical experts. MATERIALS AND METHODS: We identified EHR-based diabetes phenotype definitions that were developed for various purposes by a variety of users, including academic medical centers, Medicare, the New York City Health Department, and pharmacy benefit managers. We applied these definitions to a sample of 173 503 patients with records in the Duke Health System Enterprise Data Warehouse and at least 1 visit over a 5-year period (2007-2011). Of these patients, 22 679 (13%) met the criteria of 1 or more of the selected diabetes phenotype definitions. A statistically balanced sample of these patients was selected for chart review by clinical experts to determine the presence or absence of type 2 diabetes in the sample. RESULTS: The sensitivity (62-94%) and specificity (95-99%) of EHR-based type 2 diabetes phenotypes (compared with the gold standard ADA criteria via chart review) varied depending on the component criteria and timing of observations and measurements. DISCUSSION AND CONCLUSIONS: Researchers using EHR-based phenotype definitions should clearly specify the characteristics that comprise the definition, variations of ADA criteria, and how different phenotype definitions and components impact the patient populations retrieved and the intended application. Careful attention to phenotype definitions is critical if the promise of leveraging EHR data to improve individual and population health is to be fulfilled.

Potential mechanisms for reduced delivery of nitric oxide to peripheral tissues in diabetes mellitus.

Nitric oxide (NO) bioavailability is crucial for normal vascular endothelial function and health. Recent studies have demonstrated an endocrine role for NO equivalents that may be transported in the blood to peripheral tissue beds, where under hypoxic conditions they can liberate NO and cause vasodilation. Exercise training improves endothelial function but its effect on NO bioavailability in peripheral tissues during acute exercise stress in CVD is unclear. This paper will present evidence and discuss possible mechanisms by which NO delivery to peripheral tissues may be dysfunctional in diabetic subjects.

Effectiveness and tolerability of adding ezetimibe to niacin-based regimens for treatment of primary hyperlipidemia.

OBJECTIVE: To determine the effectiveness and tolerability of adding ezetimibe, 10 mg daily, to niacin-based regimens for dyslipidemia. METHODS: We conducted a retrospective review of medical records of 53 patients in 2 lipid clinics who received ezetimibe as add-on therapy to stable doses of niacin and other lipid medications. Mean percentage changes of lipoprotein cholesterol and triglyceride levels were determined. Safety and tolerability measures included adverse events, serum hepatic transaminases, and hemoglobin A1c (in patients with diabetes). RESULTS: Most study subjects (81%) had established atherosclerotic disease. The niacin formulation was extended-release in 31 patients (58%), immediate-release in 17 (32%), and slow-release in 5 (9%). Most patients (75%) were also taking a statin. Add-on ezetimibe therapy yielded mean reductions of 18% for total cholesterol (P<0.001), 25% for low-density lipoprotein (LDL) cholesterol (P<0.001), and 17% for triglycerides (P<0.001). High-density lipoprotein (HDL) cholesterol did not change significantly (+2%). Only 7 patients (13%) met Adult Treatment Panel III (ATP III) LDL cholesterol goals before the addition of ezetimibe, but 24 (45%; P<0.001 compared with baseline) attained these goals after addition of ezetimibe to the therapeutic regimen. Ezetimibe effectiveness did not correlate with the baseline dose of niacin or the dose/efficacy of the statin used. The addition of ezetimibe to niacin-based therapy for dyslipidemia was well tolerated. No patient had clinically significant elevations in hepatic enzyme or hemoglobin A1c levels or discontinued the ezetimibe therapy permanently. CONCLUSION: In our study, the addition of ezetimibe to niacin-based regimens lowered the LDL cholesterol level by 25% and did not change the level of HDL cholesterol. This combination can be useful in multidrug regimens for high-risk patients with dyslipidemia who are not achieving ATP III treatment goals.