A telemedicine bridge clinic improves access and reduces cost for opioid use disorder care.

Objective: We evaluated the impact of a telemedicine bridge clinic on treatment outcomes and cost for patients with opioid use disorder. Telemedicine bridge clinics deliver low-barrier rapid assessment of patients with opioid use disorder via audio-only and audiovisual telemedicine to facilitate induction on medication therapy and connection to ongoing care. Methods: A pre-post analysis of UPMC Health Plan member claims was performed to evaluate the impact of this intervention on the trajectory of care for patients with continuous coverage before and after bridge clinic visit(s). Results: Analysis included 150 UPMC Health Plan members evaluated at the bridge clinic between April 2020 and October 2021. At least one buprenorphine prescription was filled within 30 days by 91% of patients; median proportion of days covered by buprenorphine was 73.3%, 54.4%, and 50.6% at 30, 90, and 180 days after an initial visit compared to median of no buprenorphine claims 30 days prior among the same patients. Patients had an 18% decline in unplanned care utilization 30 days after initial Bridge Clinic visit, with a 62% reduction in unplanned care cost per member per month (PMPM), 38% reduction in medical cost PMPM, and 10% reduction in total PMPM (medical + pharmacy cost) at 180 days. Primary care, outpatient behavioral health, and laboratory costs increased while emergency department, urgent care, and inpatient costs declined. Conclusion: Utilization of a telemedicine bridge clinic was associated with buprenorphine initiation, linkage to ongoing care with retention including medication treatment, reduced unplanned care cost, and overall savings.

A Novel Framework to Address the Complexities of Housing Insecurity and Its Associated Health Outcomes and Inequities: "Give, Partner, Invest".

The association between housing insecurity and reduced access to healthcare, diminished mental and physical health, and increased mortality is well-known. This association, along with structural racism, social inequities, and lack of economic opportunities, continues to widen the gap in health outcomes and other disparities between those in higher and lower socio-economic strata in the United States and throughout the advanced economies of the world. System-wide infrastructure failures at municipal, state, and federal government levels have inadequately addressed the difficulty with housing affordability and stability and its associated impact on health outcomes and inequities. Healthcare systems are uniquely poised to help fill this gap and engage with proposed solutions. Strategies that incorporate multiple investment pathways and emphasize community-based partnerships and innovation have the potential for broad public health impacts. In this manuscript, we describe a novel framework, "Give, Partner, Invest," which was created and utilized by the University of Pittsburgh Medical Center (UPMC) Insurance Services Division (ISD) as part of the Integrated Delivery and Finance System to demonstrate the financial, policy, partnership, and workforce levers that could make substantive investments in affordable housing and community-based interventions to improve the health and well-being of our communities. Further, we address housing policy limitations and infrastructure challenges and offer potential solutions.

A Payer-Provider Partnership for Endocrine Targeted Automatic eConsults: Implementation and Early Impact on Diabetes and Cost Outcomes.

In the United States, many individuals with diabetes mellitus (DM) do not achieve treatment goals despite the availability of effective interventions. Provider clinical inertia is one cause of these unfavorable outcomes. Targeted automatic eConsults (TACos) are an emerging technology-based intervention with potential to address clinical inertia in primary care (PC). TACos prospectively identify at-risk patients and use unsolicited specialist recommendations to prompt treatment intensification. Through a payer-provider collaboration, a TACos intervention was piloted for adults with uncontrolled DM (HbA1c >8%) to understand impact on DM clinical inertia and outcomes. Clinical inertia was assessed by measuring whether a PC provider implemented recommended therapeutic changes. Six-month changes in HbA1c and health care costs per member per month were evaluated using an observational matched design and intention-to-treat (ITT) analysis. The analysis included 196 individuals who received a TACos between February 2021 and August 2021 (ITT group) matched to 392 controls based on clinical and demographic criteria. TACos recommendations were implemented 65% of the time. Median percent change in HbA1c was significantly greater for the ITT group versus controls (-10.9% vs. -10.2%; P = 0.0359). Median total costs were 7.9% lower in the ITT group (P = 0.0900). A per protocol analysis was done to examine effects between ITT group individuals with an implemented TACos recommendation (n = 126) and controls. Median percent change in HbA1c was significantly greater (-19.5% vs. -10.2%; P < 0.0001), but there was no difference in total costs (-7.9%; P = 0.1753). TACos may feasibly address clinical inertia in PC and improve HbA1c for uncontrolled DM.

Hypoglycemia symptoms are reduced in hospitalized patients with diabetes.

AIMS: Hospitalized patients with diabetes are have an impaired ability to detect hypoglycemia events. The purpose of this study was to compare hypoglycemia symptom scores (HSS) in hospitalized patients with diabetes after a documented blood glucose (BG) <70mg/dl with recalled HSS with outpatient hypoglycemia events. METHODS: Non-critically ill hospitalized patients with diabetes grouped as symptomatic (n=23) or asymptomatic (n=32) at time of index hypoglycemia completed a standardized HSS-Questionnaires (HSS-Q) related to the inpatient event and to recall of symptoms with outpatient hypoglycemia. RESULTS: After controlling for BG at time of index hypoglycemia (49.8±11.4 vs. 57.4±6.8mg/dl, p=0.02), symptomatic patients reported higher HSS than asymptomatic patients with the inpatient event (11.6±7.3 vs. 1.5±3.4, p<0.001) and in the outpatient setting (13.9±8.6 vs. 10.1±10.6, p<0.01). Recurrent hypoglycemia was more frequent in asymptomatic patients (13% vs. 44%, p=0.015) during the hospitalization. CONCLUSIONS: Compared to symptomatic patients, asymptomatic patients had lower inpatient and outpatient HSS and more frequent recurrent hypoglycemia events. These results suggest modification of glycemic management strategies in high risk patients to reduce risk for hypoglycemia events.

Effect of a Primary Care-Based Diabetes Education Model on Provider Referrals and Patient Participation.

PURPOSE: The purpose of this study was to evaluate the impact of a primary care (PC)-based delivery model on diabetes self-management education and support (DSMES) referrals and participation. Despite evidence that DSMES is a critical component of diabetes care, referrals and participation remain low. METHODS: PC practices were assigned to the intervention (n = 6) or usual care (n = 6). Intervention practices had direct access to a diabetes educator (DE) and applied patient-centered medical home elements to DSMES delivery. Usual care practices referred patients to traditional hospital-based outpatient DSMES programs. DSMES referrals and participation were examined for patients with diabetes, 18 to 75 years old, presenting to PC over 18 months (n = 4,894) and compared between groups. RESULTS: Compared to the usual care group, a higher percentage of patients in the intervention practices were referred to DSMES (18.4% vs 13.4%; P < .0001), and of those referred, a higher percentage of patients in the intervention practices participated in DSMES (34.9% vs 26.1%; P = .02). Patient-level factors predicting referrals were obesity (odds ratio [OR] = 1.6), higher A1C (OR = 1.4), female (OR = 1.3), and younger age (OR = 0.98). The only patient-level factor that predicted DSMES participation was lower A1C (OR = 0.9). CONCLUSIONS: This study demonstrates the positive influence of a PC-based intervention on DSMES referral and participation. However, modest improvements in DSMES rates, even with targeted efforts to address reported barriers, raise questions as to what is truly needed to drive meaningful change.

A Diabetes Education Model in Primary Care: Provider and Staff Perspectives.

PURPOSE: The purpose of this qualitative study was to explore the effectiveness of Glucose to Goal (G2G), a diabetes self-management education and support (DSMES) model for primary care (PC). METHODS: PC providers and staff were recruited from 5 PC practices participating in the 18-month intervention to participate in focus groups and interviews, which were used to gain insights about their perspectives on DSMES and how G2G was implemented across the intervention. Data were collected by qualitative researchers at baseline, midpoint, and study completion. RESULTS: At baseline, PC participants held a favorable view of DSMES and welcomed having a diabetes educator (DE) in their practice. Most participants suggested DEs would be helpful in meeting patients' nutrition needs but should give therapeutic advice only with a doctor's oversight. Participants anticipated that having a DE onsite would mitigate transportation, scheduling, communication, and cost barriers. Participant viewpoints about G2G remained unchanged from midpoint to study end, while barriers regarding location and transportation were perceived as being reduced by having a DE in the practice. Despite referral rates remaining low in some practices, many concerns stated at earlier timepoints appeared to have been attenuated by G2G components (eg, bringing the DE onsite, preidentifying patients, and DE ability to communicate and make diabetes management recommendations). CONCLUSIONS: This study demonstrates that G2G, providing DSMES in PC, appeared to be a welcome service where acceptance of and enthusiasm for the model grew over the course of the intervention.

Delivering on the value proposition of precision medicine: the view from healthcare payers.

A long-held assumption and expectation has been that genomics-based precision medicine will provide clinicians with the tools and therapies they need to consistently deliver the right treatment to the right patient while simultaneously reducing waste and yielding cost savings for health systems. The pace of discovery within the field of precision medicine has been remarkable, yet optimal uptake of new genetic tests and genetically targeted therapies will occur only if payers recognize their value and opt to cover them. Coverage decisions require clear evidence of clinical effectiveness and utility and an understanding of how adoption will impact healthcare costs and utilization within a payer's network. Research in precision medicine has often not considered the payer's perspective, and despite demonstrations of clinical effectiveness for many promising precision medicine innovations, coverage determinations have been deferred because relevant findings that payers can use to make informed decisions are lacking. Collaboration among payers, scientists, and clinicians is essential for accelerating uptake and value creation. By pairing clinical outcomes with claims and cost data and collaboratively conducting well-designed pragmatic clinical or observational studies, all stakeholders can learn from more meaningful and relevant outcomes. In turn, there will be a collective understanding of how precision medicine innovations impact the health of populations and care delivery within healthcare systems.

Connecting Smartphone and Wearable Fitness Tracker Data with a Nationally Used Electronic Health Record System for Diabetes Education to Facilitate Behavioral Goal Monitoring in Diabetes Care: Protocol for a Pragmatic Multi-Site Randomized Trial.

BACKGROUND: Mobile and wearable technology have been shown to be effective in improving diabetes self-management; however, integrating data from these technologies into clinical diabetes care to facilitate behavioral goal monitoring has not been explored. OBJECTIVE: The objective of this paper is to report on a study protocol for a pragmatic multi-site trial along with the intervention components, including the detailed connected health interface. This interface was developed to integrate patient self-monitoring data collected from a wearable fitness tracker and its companion smartphone app to an electronic health record system for diabetes self-management education and support (DSMES) to facilitate behavioral goal monitoring. METHODS: A 3-month multi-site pragmatic clinical trial was conducted with eligible patients with diabetes mellitus from DSMES programs. The Chronicle Diabetes system is currently freely available to diabetes educators through American Diabetes Association-recognized DSMES programs to set patient nutrition and physical activity goals. To integrate the goal-setting and self-monitoring intervention into the DSMES process, a connected interface in the Chronicle Diabetes system was developed. With the connected interface, patient self-monitoring information collected from smartphones and wearable fitness trackers can facilitate educators' monitoring of patients' adherence to their goals. Feasibility outcomes of the 3-month trial included hemoglobin A1c levels, weight, and the usability of the connected system. RESULTS: An interface designed to connect data from a wearable fitness tracker with a companion smartphone app for nutrition and physical activity self-monitoring into a diabetes education electronic health record system was successfully developed to enable diabetes educators to facilitate goal setting and monitoring. A total of 60 eligible patients with type 2 diabetes mellitus were randomized into either group 1) standard diabetes education or 2) standard education enhanced with the connected system. Data collection for the 3-month pragmatic trial is completed. Data analysis is in progress. CONCLUSIONS: If results of the pragmatic multi-site clinical trial show preliminary efficacy and usability of the connected system, a large-scale implementation trial will be conducted. TRIAL REGISTRATION: ClinicalTrials.gov NCT02664233; https://clinicaltrials.gov/ct2/show/NCT02664233 (Archived by WebCite at http://www.webcitation.org/6yDEwXHo5).

Use of an electronic health record to identify prevalent and incident cardiovascular disease in type 2 diabetes according to treatment strategy.

BACKGROUND: The increasing use of electronic health records (EHRs) in clinical practice offers the potential to investigate cardiovascular outcomes over time in patients with type 2 diabetes (T2D). OBJECTIVE: To develop a methodology for identifying prevalent and incident cardiovascular disease (CVD) in patients with T2D who are candidates for therapeutic intensification of glucose-lowering therapy. METHODS: Patients with glycated hemoglobin (HbA1c) ≥7% (53 mmol/mol) while receiving 1-2 oral diabetes medications (ODMs) were identified from an EHR (2005-2011) and grouped according to intensification with insulin (INS) (n=372), a different class of ODM (n=833), a glucagon-like peptide receptor 1 agonist (GLP-1RA) (n=59), or no additional therapy (NAT) (n=2017). Baseline prevalence of CVD was defined by documented International Classification of Diseases Ninth Edition (ICD-9) codes for coronary artery disease, cerebrovascular disease, or other CVD with first HbA1c ≥7% (53 mmol/mol). Incident CVD was defined as a new ICD-9 code different from existing codes over 4 years of follow-up. ICD-9 codes were validated by a chart review in a subset of patients. RESULTS: Sensitivity of ICD-9 codes for CVD ranged from 0.83 to 0.89 and specificity from 0.90 to 0.96. Baseline prevalent (INS vs ODM vs GLP-1RA vs NAT: 65% vs 39% vs 54% vs 59%, p<0.001) and incident CVD (Kaplan-Meier estimates: 58%, 31%, 52%, and 54%, p=0.002) were greater in INS group after controlling for differences in baseline HbA1c (9.2±2.0% vs 8.3±1.2% vs 8.2±1.3% vs 7.7±1.1% (77 vs 67 vs 66 vs 61 mmol/mol), p<0.001) and creatinine (1.15±0.96 vs 1.10±0.36 vs 1.01±0.35 vs 1.07±0.45 mg/dL, p=0.001). CONCLUSIONS: An EHR can be an effective method for identifying prevalent and incident CVD in patients with T2D.

Use of an Electronic Medical Record (EMR) to Identify Glycemic Intensification Strategies in Type 2 Diabetes.

BACKGROUND: Current treatment guidelines for type 2 diabetes (T2D) recommend individualized intensification of therapy for glycated hemoglobin (A1C) ≥ 7% in most patients. The purpose of this investigation was to explore the ability of an electronic medical record (EMR) to identify glycemic intensification strategies among T2D patients receiving pharmacologic therapy. METHODS: Patient records between 2005 and 2011 with documentation of A1C and active prescriptions for any diabetes medications were queried to identify potential candidates for intensification based on A1C ≥ 7% while on 1-2 oral diabetes medications (ODM). Patients with follow-up A1C values within 1 year of index A1C were grouped according to intensification with insulin, GLP-1 receptor agonists (GLP-1RA), a new class of ODM, or no intensification. Changes in A1C and continuation of intensification therapy were determined. RESULTS: A total of 4921 patients meeting inclusion criteria were intensified with insulin (n = 416), GLP-1RA (n = 68), ODM (n = 1408), or no additional therapy (n = 3029). Patients receiving insulin had higher baseline (9.3 ± 2.0 vs 8.3 ± 1.2 vs 8.3 ± 1.3 vs 7.6 ± 1.0%, P < .0001) and follow-up A1C (8.1 ± 1.6 vs 7.5 ± 1.2 vs 7.6 ± 1.3 vs 7.2 ± 1.1%, P < .0001) despite experiencing larger absolute A1C reductions (-1.2 ± 2.1 vs -0.8 ± 1.4 vs -0.7 ± 1.4 vs -0.3 ± 1.1%, P < .0001). Patients receiving GLP-1RA were more obese at baseline (BMI: 33.6 ± 7.1 vs 37.7 ± 6.1 vs 33.7 ± 6.8 vs 32.9 ± 7.1 kg/m(2), P < .0001) and follow-up (BMI: 33.9 ± 7.3 vs 36.6 ± 6.1 vs 33.8 ± 7.0 vs 32.4 ± 7.0 kg/m(2), P < .0001) despite experiencing more absolute weight reduction. Insulin was the most and GLP-1RA the least likely therapy to be continued. CONCLUSIONS: An EMR allows identification of prescribing practices and compliance with T2D treatment guidelines. Patients receiving intensification of glycemic medications had baseline A1C >8% suggesting that treatment recommendations are not being followed.