Personalized hypertension treatment recommendations by a data-driven model

BACKGROUND: Hypertension is a prevalent cardiovascular disease with severe longer-term implications. Conventional management based on clinical guidelines does not facilitate personalized treatment that accounts for a richer set of patient characteristics. METHODS: Records from 1/1/2012 to 1/1/2020 at the Boston Medical Center were used, selecting patients with either a hypertension diagnosis or meeting diagnostic criteria (≥ 130 mmHg systolic or ≥ 90 mmHg diastolic, n = 42,752). Models were developed to recommend a class of antihypertensive medications for each patient based on their characteristics. Regression immunized against outliers was combined with a nearest neighbor approach to associate with each patient an affinity group of other patients. This group was then used to make predictions of future Systolic Blood Pressure (SBP) under each prescription type. For each patient, we leveraged these predictions to select the class of medication that minimized their future predicted SBP. RESULTS: The proposed model, built with a distributionally robust learning procedure, leads to a reduction of 14.28 mmHg in SBP, on average. This reduction is 70.30% larger than the reduction achieved by the standard-of-care and 7.08% better than the corresponding reduction achieved by the 2nd best model which uses ordinary least squares regression. All derived models outperform following the previous prescription or the current ground truth prescription in the record. We randomly sampled and manually reviewed 350 patient records; 87.71% of these model-generated prescription recommendations passed a sanity check by clinicians. CONCLUSION: Our data-driven approach for personalized hypertension treatment yielded significant improvement compared to the standard-of-care. The model implied potential benefits of computationally deprescribing and can support situations with clinical equipoise.GM135930 - National Institute of General Medical Sciences; UL54 TR004130 - National Center for Advancing Translational Sciences; IIS-1914792 - National Science Foundation; DMS-1664644 - National Science Foundation; CCF-2200052 - National Science FoundationPublished versio

Identifying and addressing barriers to implementing core electronic health record use metrics for ambulatory care: Virtual consensus conference proceedings

Precise, reliable, valid metrics that are cost-effective and require reasonable implementation time and effort are needed to drive electronic health record (EHR) improvements and decrease EHR burden. Differences exist between research and vendor definitions of metrics. PROCESS:  We convened three stakeholder groups (health system informatics leaders, EHR vendor representatives, and researchers) in a virtual workshop series to achieve consensus on barriers, solutions, and next steps to implementing the core EHR use metrics in ambulatory care. CONCLUSION:  Actionable solutions identified to address core categories of EHR metric implementation challenges include: (1) maintaining broad stakeholder engagement, (2) reaching agreement on standardized measure definitions across vendors, (3) integrating clinician perspectives, and (4) addressing cognitive and EHR burden. Building upon the momentum of this workshop\u27s outputs offers promise for overcoming barriers to implementing EHR use metrics

Relaxation spectrum recovery using Fourier transforms

EThOS - Electronic Theses Online ServiceGBUnited Kingdo

Relaxation spectrum recovery using Fourier transforms

EThOS - Electronic Theses Online ServiceGBUnited Kingdo

Impact of Clinical Decision Support on Antibiotic Prescribing for Acute Respiratory Infections: a Cluster Randomized Implementation Trial

BACKGROUND: Clinical decision support (CDS) is a promising tool for reducing antibiotic prescribing for acute respiratory infections (ARIs). OBJECTIVE: To assess the impact of previously effective CDS on antibiotic-prescribing rates for ARIs when adapted and implemented in diverse primary care settings. DESIGN: Cluster randomized clinical trial (RCT) implementing a CDS tool designed to guide evidence-based evaluation and treatment of streptococcal pharyngitis and pneumonia. SETTING: Two large academic health system primary care networks with a mix of providers. PARTICIPANTS: All primary care practices within each health system were invited. All providers within participating clinic were considered a participant. Practices were randomized selection to a control or intervention group. INTERVENTIONS: Intervention practice providers had access to an integrated clinical prediction rule (iCPR) system designed to determine the risk of bacterial infection from reason for visit of sore throat, cough, or upper respiratory infection and guide evidence-based evaluation and treatment. MAIN OUTCOME(S): Change in overall antibiotic prescription rates. MEASURE(S): Frequency, rates, and type of antibiotics prescribed in intervention and controls groups. RESULTS: 33 primary care practices participated with 541 providers and 100,573 patient visits. Intervention providers completed the tool in 6.9% of eligible visits. Antibiotics were prescribed in 35% and 36% of intervention and control visits, respectively, showing no statistically significant difference. There were also no differences in rates of orders for rapid streptococcal tests (RR, 0.94; P = 0.11) or chest X-rays (RR, 1.01; P = 0.999) between groups. CONCLUSIONS: The iCPR tool was not effective in reducing antibiotic prescription rates for upper respiratory infections in diverse primary care settings. This has implications for the generalizability of CDS tools as they are adapted to heterogeneous clinical contexts. TRIAL REGISTRATION: Clinicaltrials.gov (NCT02534987). Registered August 26, 2015 at https://clinicaltrials.gov ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (10.1007/s11606-020-06096-3) contains supplementary material, which is available to authorized users

Telehealth Policy, Practice, and Education: a Position Statement of the Society of General Internal Medicine

Telehealth services, specifically telemedicine audio-video and audio-only patient encounters, expanded dramatically during the COVID-19 pandemic through temporary waivers and flexibilities tied to the public health emergency. Early studies demonstrate significant potential to advance the quintuple aim (patient experience, health outcomes, cost, clinician well-being, and equity). Supported well, telemedicine can particularly improve patient satisfaction, health outcomes, and equity. Implemented poorly, telemedicine can facilitate unsafe care, worsen disparities, and waste resources. Without further action from lawmakers and agencies, payment will end for many telemedicine services currently used by millions of Americans at the end of 2024. Policymakers, health systems, clinicians, and educators must decide how to support, implement, and sustain telemedicine, and long-term studies and clinical practice guidelines are emerging to provide direction. In this position statement, we use clinical vignettes to review relevant literature and highlight where key actions are needed. These include areas where telemedicine must be expanded (e.g., to support chronic disease management) and where guidelines are needed (e.g., to prevent inequitable offering of telemedicine services and prevent unsafe or low-value care). We provide policy, clinical practice, and education recommendations for telemedicine on behalf of the Society of General Internal Medicine. Policy recommendations include ending geographic and site restrictions, expanding the definition of telemedicine to include audio-only services, establishing appropriate telemedicine service codes, and expanding broadband access to all Americans. Clinical practice recommendations include ensuring appropriate telemedicine use (for limited acute care situations or in conjunction with in-person services to extend longitudinal care relationships), that the choice of modality be done through patient-clinician shared decision-making, and that health systems design telemedicine services through community partnerships to ensure equitable implementation. Education recommendations include developing telemedicine-specific educational strategies for trainees that align with accreditation body competencies and providing educators with protected time and faculty development resources