Lauffenburger JC, DiFrancesco MF, Barlev RA, Robertson T, Kim E, Coll MD, Haff N, Fontanet CP, Hanken K, Oran R, Avorn J, Choudhry NK. Overcoming Decisional Gaps in High-Risk Prescribing by Junior Physicians Using Simulation-Based Training: Protocol for a Randomized Controlled Trial. JMIR Res Protoc 2022;11:e31464.Abstract
BACKGROUND: Gaps between rational thought and actual decisions are increasingly recognized as a reason why people make suboptimal choices in states of heightened emotion, such as stress. These observations may help explain why high-risk medications continue to be prescribed to acutely ill hospitalized older adults despite widely accepted recommendations against these practices. Role playing and other efforts, such as simulation training, have demonstrated benefits to help people avoid decisional gaps but have not been tested to reduce overprescribing of high-risk medications. OBJECTIVE: This study aims to evaluate the impact of a simulation-based training program designed to address decisional gaps on prescribing of high-risk medications compared with control. METHODS: In this 2-arm pragmatic trial, we are randomizing at least 36 first-year medical resident physicians (ie, interns) who provide care on inpatient general medicine services at a large academic medical center to either intervention (simulation-based training) or control (online educational training). The intervention comprises a 40-minute immersive individual simulation training consisting of a reality-based patient care scenario in a simulated environment at the beginning of their inpatient service rotation. The simulation focuses on 3 types of high-risk medications, including benzodiazepines, antipsychotics, and sedative hypnotics (Z-drugs), in older adults, and is specifically designed to help the physicians identify their reactions and prescribing decisions in stressful situations that are common in the inpatient setting. The simulation scenario is followed by a semistructured debriefing with an expert facilitator. The trial's primary outcome is the number of medication doses for any of the high-risk medications prescribed by the interns to patients aged 65 years or older who were not taking one of the medications upon admission. Secondary outcomes include prescribing by all providers on the care team, being discharged on 1 of the medications, and prescribing of related medications (eg, melatonin, trazodone), or the medications of interest for the control intervention. These outcomes will be measured using electronic health record data. RESULTS: Recruitment of interns began on March 29, 2021. Recruitment for the trial ended in Q42021, with follow-up completed by Q12022. CONCLUSIONS: This trial will evaluate the impact of a simulation-based training program designed using behavioral science principles on prescribing of high-risk medications by junior physicians. If the intervention is shown to be effective, this approach could potentially be reproducible by others and for a broader set of behaviors. TRIAL REGISTRATION: NCT04668248; INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): PRR1-10.2196/31464.
Carlile N, Tantillo S, Brown M, Bates DW, Choudhry NK. A novel modality for real-time measurement of provider happiness. JAMIA Open 2022;5:ooac009.Abstract
OBJECTIVE: Physician burnout is at epidemic proportions, impacts clinical outcomes, and is very costly. Although there is emerging data about effective interventions, most physicians at risk of burnout do not seek help. Survey-based measures exist which can quantify burnout within populations, but these are usually only administered episodically. We hypothesized that a novel modality for real-time measurement of happiness and stressors would be acceptable, scalable, and could provide new actionable insights. MATERIALS: We developed a novel informatics system consisting of a networked smart button device, server, and analytics for measuring happiness, and stressors in real-time during clinical work. We performed an observational cohort study in 3 primary care clinics. Random and fixed effects modeling was used to analyze predictors of stress and happiness and we conducted a survey of usability and user acceptance of the novel system. RESULTS: We captured 455 recordings across 392 provider days from 14 primary care providers. In total, 85% of users found the device easy to use, and 87% would recommend the system to their colleagues. Happiness and stressors were observed in all working hours of the day, with a 22% reduction in feeling (the proportion of happiness to stressors) across a clinical day. DISCUSSION: We tested a novel system which providers found easy to use and enabled collection of detailed data. Limitations included being an observational study within a small number of clinics. A simple, unintrusive, scalable informatics system capable of measuring happiness, and stressors in real-time could be useful to healthcare organizations and teams.
Choudhry NK, Kronish IM, Vongpatanasin W, Ferdinand KC, Pavlik VN, Egan BM, Schoenthaler A, Houston Miller N, Hyman DJ. Medication Adherence and Blood Pressure Control: A Scientific Statement From the American Heart Association. Hypertension 2022;79(1):e1-e14.Abstract
The widespread treatment of hypertension and resultant improvement in blood pressure have been major contributors to the dramatic age-specific decline in heart disease and stroke. Despite this progress, a persistent gap remains between stated public health targets and achieved blood pressure control rates. Many factors may be important contributors to the gap between population hypertension control goals and currently observed control levels. Among them is the extent to which patients adhere to prescribed treatment. The goal of this scientific statement is to summarize the current state of knowledge of the contribution of medication nonadherence to the national prevalence of poor blood pressure control, methods for measuring medication adherence and their associated challenges, risk factors for antihypertensive medication nonadherence, and strategies for improving adherence to antihypertensive medications at both the individual and health system levels.
Powers, B.W.; Dzayich Antol D; ZHGS; RO; SWH; CNKY;. Association between Primary Care Payment Model and Telemedicine Use for Medicare Advantage Enrollees during the COVID-19 Pandemic. JAMA Health Forum 2021;2:e211597. PDF
Becker NV, Bakshi S, Martin KL, Bougrine A, Andrade J, Massey PR, Hirner JP, Eccleston J, Choudhry NK, Britton KA, Landman AB, Licurse AM, Carlile N, Mendu ML. Virtual Team Rounding: A Cross-Specialty Inpatient Care Staffing Program to Manage COVID-19 Surges. Acad Med 2021;96(12):1717-1721.Abstract
PROBLEM: The SARS-CoV-2 (COVID-19) pandemic presented numerous challenges to inpatient care, including overtaxed inpatient medicine services, surges in patient censuses, disrupted patient care and educational activities for trainees, underused providers in certain specialties, and personal protective equipment shortages and new requirements for physical distancing. In March 2020, as the COVID-19 surge began, an interdisciplinary group of administrators, providers, and trainees at Brigham and Women's Hospital created an inpatient virtual staffing model called the Virtual Team Rounding Program (VTRP). APPROACH: The conceptual framework guiding VTRP development was rapid-cycle innovation. The VTRP was designed iteratively using feedback from residents, physician assistants, attendings, and administrators from March to June 2020. The VTRP trained and deployed a diverse set of providers across specialties as "virtual rounders" to support inpatient teams by joining and participating in rounds via videoconference and completing documentation tasks during and after rounds. The program was rapidly scaled up from March to June 2020. OUTCOMES: In a survey of inpatient providers at the end of the pilot phase, 10/10 (100%) respondents reported they were getting either "a lot" or "a little" benefit from the VTRP and did not find the addition of the virtual rounder burdensome. During the scaling phase, the program grew to support 24 teams. In a survey at the end of the contraction phase, 117/187 (62.6%) inpatient providers who worked with a virtual rounder felt the rounder saved them time. VTRP leadership collaboratively and iteratively developed best practices for challenges encountered during implementation. NEXT STEPS: Virtual rounding provides a valuable extension of inpatient teams to manage COVID-19 surges. Future work will quantitatively and qualitatively assess the impact of the VTRP on inpatient provider satisfaction and well-being, virtual rounders' experiences, and patient care outcomes.
Fontanet CP, Choudhry NK, Isaac T, Sequist TD, Gopalakrishnan C, Gagne JJ, Jackevicius CA, Fischer MA, Solomon DH, Lauffenburger JC. Comparison of measures of medication adherence from pharmacy dispensing and insurer claims data. Health Serv Res 2021;:1-13.Abstract
OBJECTIVE: Medication nonadherence is linked to worsened clinical outcomes and increased costs. Existing system-level adherence interventions rely on insurer claims for patient identification and outcome measurement, yet suffer from incomplete capture and lags in data acquisition. Data from pharmacies regarding prescription filling, captured in retail dispensing, may be more efficient. DATA SOURCES: Pharmacy fill and insurer claims data. STUDY DESIGN: We compared adherence measured using pharmacy fill data to adherence using insurer claims data, expressed as proportion of days covered (PDC) over 12 months. Agreement was evaluated using correlation/validation metrics. We also explored the relationship between adherence in both sources and disease control using prediction modeling. DATA EXTRACTION METHODS: Large pragmatic trial of cardiometabolic disease in an integrated delivery network. PRINCIPAL FINDINGS: Among 1113 patients, adherence was higher in pharmacy fill (mean = 50.0%) versus claims data (mean = 47.4%), although they had moderately high correlation (R = 0.57, 95% CI: 0.53-0.61) with most patients (86.9%) being similarly classified as adherent or nonadherent. Sensitivity and specificity of pharmacy fill versus claims data were high (0.89, 95% CI: 0.86-0.91 and 0.80, 95% CI: 0.75-0.85). Pharmacy fill-based PDC predicted better disease control slightly more than claims-based PDC, although the difference was nonsignificant. CONCLUSIONS: Pharmacy fill data may be an alternative to insurer claims for adherence measurement.
Lauffenburger JC, Yom-Tov E, Keller PA, McDonnell ME, Bessette LG, Fontanet CP, Sears ES, Kim E, Hanken K, Buckley JJ, Barlev RA, Haff N, Choudhry NK. REinforcement learning to improve non-adherence for diabetes treatments by Optimising Response and Customising Engagement (REINFORCE): study protocol of a pragmatic randomised trial. BMJ Open 2021;11(12):e052091.Abstract
INTRODUCTION: Achieving optimal diabetes control requires several daily self-management behaviours, especially adherence to medication. Evidence supports the use of text messages to support adherence, but there remains much opportunity to improve their effectiveness. One key limitation is that message content has been generic. By contrast, reinforcement learning is a machine learning method that can be used to identify individuals' patterns of responsiveness by observing their response to cues and then optimising them accordingly. Despite its demonstrated benefits outside of healthcare, its application to tailoring communication for patients has received limited attention. The objective of this trial is to test the impact of a reinforcement learning-based text messaging programme on adherence to medication for patients with type 2 diabetes. METHODS AND ANALYSIS: In the REinforcement learning to Improve Non-adherence For diabetes treatments by Optimising Response and Customising Engagement (REINFORCE) trial, we are randomising 60 patients with suboptimal diabetes control treated with oral diabetes medications to receive a reinforcement learning intervention or control. Subjects in both arms will receive electronic pill bottles to use, and those in the intervention arm will receive up to daily text messages. The messages will be individually adapted using a reinforcement learning prediction algorithm based on daily adherence measurements from the pill bottles. The trial's primary outcome is average adherence to medication over the 6-month follow-up period. Secondary outcomes include diabetes control, measured by glycated haemoglobin A1c, and self-reported adherence. In sum, the REINFORCE trial will evaluate the effect of personalising the framing of text messages for patients to support medication adherence and provide insight into how this could be adapted at scale to improve other self-management interventions. ETHICS AND DISSEMINATION: This study was approved by the Mass General Brigham Institutional Review Board (IRB) (USA). Findings will be disseminated through peer-reviewed journals, reporting and conferences. TRIAL REGISTRATION NUMBER: (NCT04473326).
Fontanet CP, Choudhry NK, Wood W, Robertson T, Haff N, Oran R, Sears ES, Kim E, Hanken K, Barlev RA, Lauffenburger JC, Feldman CH. Randomised controlled trial targeting habit formation to improve medication adherence to daily oral medications in patients with gout. BMJ Open 2021;11(11):e055930.Abstract
INTRODUCTION: Medication adherence for patients with chronic conditions such as gout, a debilitating form of arthritis that requires daily medication to prevent flares, is a costly problem. Existing interventions to improve medication adherence have only been moderately effective. Habit formation theory is a promising strategy to improve adherence. The cue-reward-repetition principle posits that habits are formed by repeatedly completing an activity after the same cue and having the action rewarded every time. Over time, cues become increasingly important whereas rewards become less salient because the action becomes automatic. Leveraging the cue-reward-repetition principle could improve adherence to daily gout medications. METHODS AND ANALYSIS: This three-arm parallel randomised controlled trial tests an adaptive intervention that leverages the repetition cue-reward principle. The trial will began recruitment in August 2021 in Boston, Massachusetts, USA. Eligible patients are adults with gout who have been prescribed a daily oral medication for gout and whose most recent uric acid is above 6 mg/dL. Participants will be randomised to one of three arms and given electronic pill bottles. In the two intervention arms, participants will select a daily activity to link to their medication-taking (cue) and a charity to which money will be donated every time they take their medication (reward). Participants in Arm 1 will receive reminder texts about their cue and their charity reward amount will be US$0.50 per day of medication taken. Arm 2 will be adaptive; participants will receive a US$0.25 per adherent-day and no reminder texts. If their adherence is <75% 6 weeks postrandomisation, their reward will increase to US$0.50 per adherent-day and they will receive reminder texts. The primary outcome is adherence to gout medications over 18 weeks. ETHICS AND DISSEMINATION: This trial has ethical approval in the USA. Results will be published in a publicly accessible peer-reviewed journal. TRIAL REGISTRATION NUMBER: NCT04776161.
Chiu N, Lauffenburger JC, Franklin JM, Choudhry NK. Prevalence, predictors, and outcomes of both true- and pseudo-resistant hypertension in the action to control cardiovascular risk in diabetes trial: a cohort study. Hypertens Res 2021;44(11):1471-1482.Abstract
Resistant hypertension (RH) has been poorly studied due to the difficulty in distinguishing it from nonadherence-the exclusion of which is necessary to accurately diagnose RH. Therefore, little is known about the prevalence, predictors, and outcomes of true RH. We evaluated 1838 patients from the standard blood pressure (BP) arm of the Action to Control Cardiovascular Risk in Diabetes Trial. We classified patients into three groups: "true RH", "pseudo-RH" (i.e., patients with BP levels that would classify them as RH but who were non-adherent), and "other" (i.e., those who could not be classified as having "true RH" or "pseudo-RH"). We examined predictors of true and pseudo-RH and the relationship between true RH and the composite outcome of nonfatal MI, nonfatal stroke, or cardiovascular death. Among 1838 participants with complete information, 489 (26.6%) met the definition of true RH, and 94 (16.1%) RH patients had "pseudo-RH" on ≥1 visit over the first 12 months. Predictors of RH included: baseline SBP ≥ 160 mmHg (OR = 8.79; 95% CI: 5.70-13.68) and baseline SBP between 140-159 (OR = 2.91; 95% CI: 2.13-4.00) compared to SBP < 140, additional baseline BP medication (OR = 3.40; 95% CI: 2.83-4.11), macroalbuminuria (OR = 2.35; 95% CI: 1.50-3.67), CKD (OR = 1.53; 95% CI: 0.99-2.33), history of stroke (OR = 1.73; 95% CI: 1.04-2.82), and black race (OR = 1.39; 95% CI: 1.02-1.88); the cross-validated C-statistic was 0.80. "True RH" patients had a 65% increased hazard in composite outcome (HR = 1.65; 95% CI: 1.13-2.42). In conclusion, the majority of patients classified as having RH had "true RH," which was more common among those who are black, have macroalbuminuria, CKD, stroke, higher baseline SBP, and are taking more baseline antihypertensives. These patients are at increased risk for cardiovascular and mortality events.
Lauffenburger JC, Haff N, McDonnell ME, Solomon DH, Antman EM, Glynn RJ, Choudhry NK. Exploring patient experiences coping with using multiple medications: a qualitative interview study. BMJ Open 2021;11(11):e046860.Abstract
OBJECTIVE: Long-term adherence to evidence-based medications in cardiometabolic diseases remains poor, despite extensive efforts to develop and test interventions and deploy clinician performance incentives. The limited success of interventions may be due to ignored factors such as patients' experience of medication-taking. Despite being potentially addressable by clinicians, these factors have not been sufficiently explored, which is particularly important as patients use increasing numbers of medications. The aim is to explore patient perspectives on medication-taking, medication properties that are barriers to adherence, and coping strategies for their medication regimen. DESIGN: Individual, in-person, semistructured qualitative interviews. SETTING: Urban healthcare system. PARTICIPANTS: Twenty-six adults taking ≥2 oral medications for diabetes, hypertension or hyperlipidaemia with non-adherence. Interviews were digitally recorded and transcribed. Data were analysed using developed codes to generate themes. Representative quotations were selected to illustrate themes. RESULTS: Participants' mean age was 55 years, 46% were female and 39% were non-white. Six key themes were identified: (1) medication-taking viewed as a highly inconvenient action (that patients struggle to remember to do); (2) negative implications because of inconvenience or illness perceptions; (3) actual medication regimens can deviate substantially from prescribed regimens; (4) certain medication properties (especially size and similar appearance with others) may contribute to adherence deviations; (5) development of numerous coping strategies to overcome barriers and (6) suggestions to make medication-taking easier (including reducing drug costs, simplifying regimen or dosing frequency and creating more palatable medications). CONCLUSION: Patients with poor adherence often find taking prescription medications to be undesirable and take them differently than prescribed in part due to properties of the medications themselves and coping strategies they have developed to overcome medication-taking challenges. Interventions that reduce the inconvenience of medication use and tailor medications to individual needs may be a welcome development.
Choudhry NK, Fontanet CP, Ghazinouri R, Fifer S, Archer KR, Haff N, Butterworth SW, Deogun H, Block S, Cooper A, Sears E, Goyal P, Coronado RA, Schneider BJ, Hsu E, Milstein A. Design of the Spine Pain Intervention to Enhance Care Quality And Reduce Expenditure Trial (SPINE CARE) study: Methods and lessons from a multi-site pragmatic cluster randomized controlled trial. Contemp Clin Trials 2021;111:106602.Abstract
BACKGROUND: Low back and neck pain (together, spine pain) are among the leading causes of medical visits, lost productivity, and disability. For most people, episodes of spine pain are self-limited; nevertheless, healthcare spending for this condition is extremely high. Focusing care on individuals at high-risk of progressing from acute to chronic pain may improve efficiency. Alternatively, postural therapies, which are frequently used by patients, may prevent the overuse of high-cost interventions while delivering equivalent outcomes. METHODS: The SPINE CARE (Spine Pain Intervention to Enhance Care Quality And Reduce Expenditure) trial is a cluster-randomized multi-center pragmatic clinical trial designed to evaluate the clinical effectiveness and healthcare utilization of two interventions for primary care patients with acute and subacute spine pain. The study is being conducted at 33 primary care clinics in geographically distinct regions of the United States. Individuals ≥18 years presenting to primary care with neck and/or back pain of ≤3 months' duration were randomized at the clinic-level to 1) usual care, 2) a risk-stratified, multidisciplinary approach called the Identify, Coordinate, and Enhance (ICE) care model, or 3) Individualized Postural Therapy (IPT), a standardized postural therapy method of care. The trial's two primary outcomes are change in function at 3 months and spine-related spending at one year. 2971 individuals were enrolled between June 2017 and March 2020. Follow-up was completed on March 31, 2021. DISCUSSION: The SPINE CARE trial will determine the impact on clinical outcomes and healthcare costs of two interventions for patients with spine pain presenting to primary care. TRIAL REGISTRATION NUMBER: NCT03083886.
Bhatt AS, Choudhry NK. Evidence-Based Prescribing and Polypharmacy for Patients With Heart Failure. Ann Intern Med 2021;174(8):1165-1166. PDF
Haff N, Choudhry NK. Helping Patients Manage Their Own Blood Pressures: A Strategy to Address Hypertension Control in the United States. Circ Cardiovasc Qual Outcomes 2021;14(8) PDF
Piña IL, Di Palo KE, Brown MT, Choudhry NK, Cvengros J, Whalen D, Whitsel LP, Johnson J. Medication adherence: Importance, issues and policy: A policy statement from the American Heart Association. Prog Cardiovasc Dis 2021;64:111-120.Abstract
Medications do not work in patients who do not take them. This true statement highlights the importance of medication adherence. Providers are often frustrated by the lack of consistent medication adherence in the patients they care for. Today with the time constraints that providers face, it becomes difficult to discover the extent of non-adherence. There are certainly many challenges in medication adherence not only at the patient-provider level but also within a healthy system and finally in insurers and payment systems. In a cross-sectional survey of unintentional nonadherence in over 24,000 adults with chronic illness, including hypertension, diabetes and hyperlipidemia, 62% forgot to take medications and 37% had run out of their medications within a year. These sobering data necessitate immediate policy and systems solutions to support patients in adherence. Medication adherence for cardiovascular diseases (CVD) has the potential to change outcomes, such as blood pressure control and subsequent events. The American Heart Association (AHA)/American Stroke Association (ASA) has a goal of improving medication adherence in CVD and stroke prevention and treatment. This paper will explore medication adherence with all its inherent issues and suggest policy and structural changes that must happen in order to transform medication adherence levels in the U.S. and achieve the AHA/ASA's health impact goals.
Lauffenburger JC, Barlev RA, Sears ES, Keller PA, McDonnell ME, Yom-Tov E, Fontanet CP, Hanken K, Haff N, Choudhry NK. Preferences for mHealth Technology and Text Messaging Communication in Patients With Type 2 Diabetes: Qualitative Interview Study. J Med Internet Res 2021;23:e25958.Abstract
BACKGROUND: Individuals with diabetes need regular support to help them manage their diabetes on their own, ideally delivered via mechanisms that they already use, such as their mobile phones. One reason for the modest effectiveness of prior technology-based interventions may be that the patient perspective has been insufficiently incorporated. OBJECTIVE: This study aims to understand patients' preferences for mobile health (mHealth) technology and how that technology can be integrated into patients' routines, especially with regard to medication use. METHODS: We conducted semistructured qualitative individual interviews with patients with type 2 diabetes from an urban health care system to elicit and explore their perspectives on diabetes medication-taking behaviors, daily patterns of using mobile technology, use of mHealth technology for diabetes care, acceptability of text messages to support medication adherence, and preferred framing of information within text messages to support diabetes care. The interviews were digitally recorded and transcribed. The data were analyzed using codes developed by the study team to generate themes, with representative quotations selected as illustrations. RESULTS: We conducted interviews with 20 participants, of whom 12 (60%) were female and 9 (45%) were White; in addition, the participants' mean glycated hemoglobin A(1c) control was 7.8 (SD 1.1). Overall, 5 key themes were identified: patients try to incorporate cues into their routines to help them with consistent medication taking; many patients leverage some form of technology as a cue to support adherence to medication taking and diabetes self-management behaviors; patients value simplicity and integration of technology solutions used for diabetes care, managing medications, and communicating with health care providers; some patients express reluctance to rely on mobile technology for these diabetes care behaviors; and patients believe they prefer positively framed communication, but communication preferences are highly individualized. CONCLUSIONS: The participants expressed some hesitation about using mobile technology in supporting diabetes self-management but have largely incorporated it or are open to incorporating it as a cue to make medication taking more automatic and less burdensome. When using technology to support diabetes self-management, participants exhibited individualized preferences, but overall, they preferred simple and positively framed communication. mHealth interventions may be improved by focusing on integrating them easily into daily routines and increasing the customization of content.
Bhatt AS, Varshney AS, Nekoui M, Moscone A, Cunningham JW, Jering KS, Patel PN, Sinnenberg LE, Bernier TD, Buckley LF, Cook B, Dempsey J, Kelly J, Knowles DM, Lupi K, Malloy R, Matta LS, Rhoten MN, Sharma K, Snyder CA, Ting C, McElrath EE, Amato MG, Alobaidly M, Ulbricht CE, Choudhry NK, Adler DS, Vaduganathan M. Virtual Optimization of Guideline-Directed Medical Therapy in Hospitalized Patients with Heart Failure with Reduced Ejection Fraction: the IMPLEMENT-HF Pilot Study. Eur J Heart Fail 2021;Abstract
AIMS: HFrEF GDMT implementation remains incomplete. Non-cardiovascular hospitalization may present opportunities for GDMT optimization. We assessed the efficacy and durability of a virtual, multidisciplinary "GDMT Team" on medical therapy prescription for HFrEF. METHODS AND RESULTS: Consecutive hospitalizations in patients with HFrEF≤40% were prospectively identified from February 3 to March 1, 2020 (usual care group) and March 2 to August 28, 2020 (intervention group). Patients with critical illness, de-novo HF, and SBP<90mmHg were excluded. In the intervention group, a pharmacist-physician GDMT Team provided optimization suggestions to treating teams based on an evidence-based algorithm. The primary outcome was a GDMT optimization score, the net of positive (+1 for new initiations or up-titrations) & negative therapeutic changes (-1 for discontinuations or down-titrations) at hospital discharge. Serious in-hospital safety events were assessed. Among 278 consecutive encounters with HFrEF, 118 met eligibility criteria; 29 (25%) received usual care and 89 (75%) received the GDMT Team intervention. Among usual care encounters, there were no changes in GDMT prescription during hospitalization. In the intervention group, β-blocker (72% to 88%; P=0.01), ARNI (6% to 17%; P=0.03), MRA (16% to 29%; P=0.05), and triple therapy (9% to 26%; P<0.01) prescriptions increased during hospitalization. After adjustment, the GDMT Team was associated with an increase in GDMT optimization score (+0.58; 95% CI: +0.09 to +1.07; P=0.02). There were no serious in-hospital adverse events. CONCLUSIONS: Non-cardiovascular hospitalizations are a potentially safe and effective setting for GDMT optimization. A virtual GDMT Team was associated with improved HF therapeutic optimization. This implementation strategy warrants testing in a prospective randomized controlled trial. This article is protected by copyright. All rights reserved.
Lauffenburger JC, Isaac T, Trippa L, Keller P, Robertson T, Glynn RJ, Sequist TD, Kim DH, Fontanet CP, Castonguay EWB, Haff N, Barlev RA, Mahesri M, Gopalakrishnan C, Choudhry NK. Rationale and design of the Novel Uses of adaptive Designs to Guide provider Engagement in Electronic Health Records (NUDGE-EHR) pragmatic adaptive randomized trial: a trial protocol. Implementation Science 2021;16:9.Abstract
The prescribing of high-risk medications to older adults remains extremely common and results in potentially avoidable health consequences. Efforts to reduce prescribing have had limited success, in part because they have been sub-optimally timed, poorly designed, or not provided actionable information. Electronic health record (EHR)-based tools are commonly used but have had limited application in facilitating deprescribing in older adults. The objective is to determine whether designing EHR tools using behavioral science principles reduces inappropriate prescribing and clinical outcomes in older adults.
Ashrafzadeh S, Metlay JP, Choudhry NK, Emmons KM, Asgari MM. Using Implementation Science to Optimize the Uptake of Evidence-Based Medicine into Dermatology Practice. J Invest Dermatol 2020;140:952-958.Abstract
An estimated 17-year lag exists between evidence generation and its integration into routine clinical care. The field of implementation science has emerged to close this gap by applying rigorous methods to systematically study the obstacles and facilitators of the uptake of evidence-based practices. However, implementation science has not gained wide traction in dermatology. In this narrative review, we use literature and expert input to introduce implementation science and key frameworks for implementing interventions and evaluating their uptake. We then highlight opportunities for dermatology-specific interventions at the patient-, provider-, system-, and population-levels, and advocate for the field's expansion into dermatology.
Fanaroff AC, Peterson ED, Kaltenbach LA, Anstrom KJ, Fonarow GC, Henry TD, Cannon CP, Choudhry NK, Cohen DJ, Atreja N, Bhalla N, Eudicone JM, Wang TY. Copayment Reduction Voucher Utilization and Associations With Medication Persistence and Clinical Outcomes: Findings From the ARTEMIS Trial. Circ Cardiovasc Qual Outcomes 2020;13:e006182.Abstract
BACKGROUND: Cost is frequently cited as a barrier to optimal medication use, but the extent to which copayment assistance interventions are used when available, and their impact on evidence-based medication persistence and major adverse cardiovascular events is unknown. METHODS AND RESULTS: The ARTEMIS trial (Affordability and Real-World Antiplatelet Treatment Effectiveness After Myocardial Infarction Study) randomized 301 hospitals to usual care versus the ability to provide patients with vouchers that offset copayment costs when filling P2Y(12) inhibitors in the 1 year post-myocardial infarction. In the intervention group, we used multivariable logistic regression to identify patient and medication cost characteristics associated with voucher use. We then used this model to stratify both intervention and usual care patients by likelihood of voucher use, and examined the impact of the voucher intervention on 1-year P2Y(12) inhibitor persistence (no gap in pharmacy supply >30 days) and major adverse cardiovascular events (all-cause death, myocardial infarction, or stroke). Among 10 102 enrolled patients, 6135 patients were treated at hospitals randomized to the copayment intervention. Of these, 1742 (28.4%) never used the voucher, although 1729 (99.2%) voucher never-users filled at least one P2Y(12) inhibitor prescription in the 1 year post-myocardial infarction. Characteristics most associated with voucher use included: discharge on ticagrelor, planned 1-year course of P2Y(12) inhibitor treatment, white race, commercial insurance, and higher out-of-pocket medication costs (c-statistic 0.74). Applying this propensity model to stratify all enrolled patients by likelihood of voucher use, the intervention improved medication persistence the most in patients with high likelihood of voucher use (adjusted interaction P=0.03, odds ratio, 1.86 [95% CI, 1.48-2.33]). The intervention did not significantly reduce major adverse cardiovascular events in any voucher use likelihood group, although the odds ratio was lowest (0.86 [95% CI, 0.56-1.16]) among patients with high likelihood of voucher use (adjusted interaction P=0.04). CONCLUSIONS: Among patients discharged after myocardial infarction, those with higher copayments and greater out-of-pocket medication costs were more likely to use a copayment assistance voucher, but some classes of patients were less likely to use a copayment assistance voucher. Patients at low likelihood of voucher use benefitted least from copayment assistance, and other interventions may be needed to improve medication-taking behaviors and clinical outcomes in these patients. Registration: URL: Unique identifier: NCT02406677.
Lauffenburger JC, Mahesri M, Choudhry NK. Use of Data-Driven Methods to Predict Long-term Patterns of Health Care Spending for Medicare Patients. JAMA network open 2020;3:e2020291-e2020291.Abstract
IMPORTANCE: Current approaches to predicting health care costs generally rely on a single composite value of spending and focus on short time horizons. By contrast, examining patients' spending patterns using dynamic measures applied over longer periods may better identify patients with different spending and help target interventions to those with the greatest need. OBJECTIVE: To classify patients by their long-term, dynamic health care spending patterns using a data-driven approach and assess the ability to predict spending patterns, particularly using characteristics that are potentially modifiable through intervention. DESIGN, SETTING, AND PARTICIPANTS: This cohort study used a retrospective cohort design from a random nationwide sample of Medicare fee-for-service administrative claims data to identify beneficiaries aged 65 years or older with continuous eligibility from 2011 to 2013. Statistical analysis was performed from August 2018 to December 2019. MAIN OUTCOMES AND MEASURES: Group-based trajectory modeling was applied to the claims data to classify the Medicare beneficiaries by their total health care spending patterns over a 2-year period. The ability to predict membership in each trajectory spending group was assessed using generalized boosted regression, a data mining approach to model building and prediction, with split-sample validation. Models were estimated using (1) prior-year predictors and (2) prior-year predictors potentially modifiable through intervention measured in the claims data. These models were evaluated using validated C-statistics. The relative influence of individual predictors in the models was evaluated. RESULTS: Among the 329 476 beneficiaries, the mean (SD) age was 76.0 (7.2) years and 190 346 (57.8%) were female. This final 5-group model included a minimal-user group (group 1, 37 572 individuals [11.4%]), a low-cost group (group 2, 48 575 individuals [14.7%]), a rising-cost group (group 3, 24 736 individuals [7.5%]), a moderate-cost group (group 4, 83 338 individuals [25.3%]), and a high-cost group (group 5, 135 255 individuals [41.2%]). Potentially modifiable characteristics strongly predicted these patterns (C-statistics range: 0.68-0.94). For groups with progressively increasing spending in particular, the most influential factors were number of medications (relative influence: 29.2), number of office visits (relative influence: 30.3), and mean medication adherence (relative influence: 33.6). CONCLUSIONS AND RELEVANCE: Using a data-driven approach, distinct spending patterns were identified with high accuracy. The potentially modifiable predictors of membership in the rising-cost group represent important levers for early interventions that may prevent later spending increases. This approach could be adapted by organizations to target quality improvement interventions, particularly because numerous health care organizations are increasingly using these routinely collected data.