Wearable technologies promise to redefine assessment of health behaviors, yet their clinical implementation remains a challenge. To address this gap, two of the NIH’s Big Data to Knowledge Centers of Excellence organized a workshop on potential clinical applications of wearables. A workgroup comprised of 14 stakeholders from diverse backgrounds (hospital administration, clinical medicine, academia, insurance, and the commercial device industry) discussed two successful digital health interventions that involve wearables to identify common features responsible for their success. Seven features were identified including: a clearly defined problem, integration into a system of healthcare delivery, technology support, personalized experience, focus on end-user experience, alignment with reimbursement models, and inclusion of clinician champions. Health providers and systems keen to establish new models of care inclusive of wearables may consider these features during program design. A better understanding of these features is necessary to guide future clinical applications of wearable technology.

ImportanceAntibiotic resistance is a global health issue. Up to 50% of antibiotics are inappropriately prescribed, the majority of which are for acute respiratory tract infections (ARTI).ObjectiveTo evaluate the impact of unblinded normative comparison on rates of inappropriate antibiotic prescribing for ARTI.DesignNon-randomised, controlled interventional trial over 1 year followed by an open intervention in the second year.SettingPrimary care providers in a large regional healthcare system.ParticipantsThe test group consisted of 30 primary care providers in one geographical region; controls consisted of 162 primary care providers located in four other geographical regions.InterventionThe intervention consisted of provider and patient education and provider feedback via biweekly, unblinded normative comparison highlighting inappropriate antibiotic prescribing for ARTI. The intervention was applied to both groups during the second year.Main outcomes and measuresRate of inappropriate antibiotic prescription for ARTI.ResultsBaseline inappropriate antibiotic prescribing for ARTI was 60%. After 1 year, the test group rate of inappropriate antibiotic prescribing decreased 40%, from 51.9% to 31.0% (p<0.0001), whereas controls decreased 7% (61.3% to 57.0%, p<0.0001). In year 2, the test group decreased an additional 47% to an overall prescribing rate of 16.3%, and the control group decreased 40% to a prescribing rate of 34.5% after implementation of the same intervention.Conclusions and relevanceProvider and patient education followed by regular feedback to provider via normative comparison to their local peers through unblinded provider reports, lead to reductions in the rate of inappropriate antibiotic prescribing for ARTI and overall antibiotic prescribing rates.

Modest changes in routine hospital care can improve the hospital environment impacting sleep and access to health knowledge, leading to improvements in hospital outcomes. Sleep-wake patterns of hospitalized patients represent a potential avenue for further enhancing hospital quality and safety.

Hypertension (HTN) is the most common chronic disease in the U.S., and the standard model of office-based care delivery has yielded suboptimal outcomes, with approximately 50% of affected patients not achieving blood pressure (BP) control. Poor population-level BP control has been primarily attributed to therapeutic inertia and low patient engagement. New models of care delivery utilizing patient-generated health data, comprehensive assessment of social health determinants, computerized algorithms generating tailored interventions, frequent communication and reporting, and non-physician providers organized as an integrated practice unit, have the potential to transform population-based HTN control. This review will highlight the importance of these elements and construct the rationale for a reengineered model of care delivery for populations with HTN.

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Background Diabetes is present in 10.5% of the US population and accounts for 14.3% of all office-based physician visits made by adults. Despite this established office-based approach, the disease and its adverse outcomes including glycemic control and clinical events tend to worsen over time. Available home technology now provides accurate, reliable data that can be transmitted directly to the electronic medical record. Objective This study aims to evaluate the impact of a virtual, home-based diabetes management program on clinical measures of diabetes control compared to usual care. Methods We evaluated glycemic control and other diabetes-related measures after 1 year in 763 patients with type 2 diabetes enrolled into a home-based digital medicine diabetes program and compared them to 794 patients matched for age, sex, race, BMI, hemoglobin A1c (HbA1c), creatinine, estimated glomerular filtration rate, and insulin use in a usual care group after 1 year. Digital medicine patients completed questionnaires online, received medication management and lifestyle recommendations from a clinical pharmacist or advanced practice provider and a health coach, and were asked to submit blood glucose readings using a commercially available Bluetooth-enabled glucose meter that transmitted data directly to the electronic medical record. Results After 1 year, usual care patients demonstrated no significant changes in HbA1c (mean 7.3, SE 1.7 to mean 7.3, SE 1.6; P=.41) or changes in the proportion of patients with HbA1c≥9.0 (n=117, 15% to n=113, 14%; P=.51). Digital medicine patients demonstrated improvements in HbA1c (mean 7.3, SE 1.5 to mean 6.9, SE 1.2; P<.001) and significant changes in the proportion of patients with HbA1c≥9.0 (n=107, 14% to n=49, 6%; P<.001), diabetes distress (n=198, 26% to n=122, 16%; P<.001), and hypoglycemic episodes (n=313, 41.1% to n=91, 11.9%; P<.001). Conclusions A digital diabetes program is associated with significant improvement in glycemic control and other diabetes measures. The use of a virtual health intervention using connected devices was widely accepted across a broad range of ethnic diversity, ages, and levels of health literacy.

Objectives: Statins are effective in reducing low-density lipoprotein cholesterol (LDL-C) and cardiovascular disease risk, however, despite treatment, patients in the real-world setting may not reach lipid goals. Ensuring timely treatment intensification (TI) is important in reducing the risk of adverse outcomes. The objectives of this study, conducted in patients with ASCVD and baseline LDL-C.70 despite existing statin therapy were to (a) To evaluate predictors of TI (b) To assess the association between TI and LDL-C lowering. Methods: A retrospective cohort study was conducted using Truven MarketScan database from 2013-2017. The first prescription of statin between January 2014-February 2016 was identified as the index date. A preindex period of 180 days to identify baseline characteristics and a follow-up period of 360 days for outcomes was determined. TI was defined as an increase in the intensity of statin from low to moderate, moderate to high or low to high. Logistic regression was conducted to identify predictors of TI. To measure the effect of TI on LDL-C reduction, multiple linear regression with follow-up LDL-C as the outcome was performed. Results: There were 14,395 patients included in the study with 3,909 patients having follow-up LDL-C values. TI in the follow-up was observed in 1,914 (13.30%) patients. Age older than 75 [OR: 0.7(95%CI: 0.59-0.84)] was associated with lower likelihood of TI in the multivariable logistic regression model. Baseline characteristics positively associated with TI included higher LDL-C value and adherence, low and moderate statin intensity, male sex and diabetes. For patients with follow-up LDL-C values, TI was associated with significant mean reduction in LDL-C (b: -9.68, p=,0.0001). Conclusions: Even though TI was associated with significant LDL-C lowering, a significant proportion of patients not at goal were not intensified. Future studies need to examine the relationship of TI with factors such as patient adherence and side-effects as well as cardiovascular outcomes.