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Revisado por pares
Challenge of Optimizing Medical Therapy in Heart Failure: Unlocking the Potential of Digital Health and Patient Engagement
2024; Wiley; Volume: 13; Issue: 2 Linguagem: Inglês
10.1161/jaha.123.030952
ISSN2047-9980
AutoresZahra Azizi, Jessica R. Golbus, Erin M. Spaulding, Phillip H Hwang, Ana Luiza Ciminelli, Kathleen Lacar, Mario Funes Hernandez, Nisha A. Gilotra, Natasha Din, Luísa Campos Caldeira Brant, Rhoda Au, Andrea Beaton, Brahmajee K. Nallamothu, Chris T. Longenecker, Seth S. Martin, Michael P. Dorsch, Alexander T. Sandhu,
Tópico(s)Cardiac Health and Mental Health
ResumoHomeJournal of the American Heart AssociationVol. 13, No. 2Challenge of Optimizing Medical Therapy in Heart Failure: Unlocking the Potential of Digital Health and Patient Engagement Open AccessArticle CommentaryPDF/EPUBAboutView PDFView EPUBSections ToolsAdd to favoritesDownload citationsTrack citationsPermissions ShareShare onFacebookTwitterLinked InMendeleyReddit Jump toOpen AccessArticle CommentaryPDF/EPUBChallenge of Optimizing Medical Therapy in Heart Failure: Unlocking the Potential of Digital Health and Patient Engagement Zahra Azizi, Jessica R. Golbus, Erin M. Spaulding, Phillip H. Hwang, Ana L. A. Ciminelli, Kathleen Lacar, Mario Funes Hernandez, Nisha A. Gilotra, Natasha Din, Luisa C. C. Brant, Rhoda Au, Andrea Beaton, Brahmajee K. Nallamothu, Chris T. Longenecker, Seth S. Martin, Michael P. Dorsch and Alexander T. Sandhu Zahra AziziZahra Azizi * Correspondence to: Zahra Azizi, MD, MSc, Center for Digital Health, Department of Cardiovascular Medicine, Stanford University, 3180 Porter Dr, Palo Alto, CA 94304. Email: E-mail Address: [email protected] https://orcid.org/0000-0002-7897-0934 , Center for Digital Health, , Stanford University, , Stanford, , CA, , Stanford University Division of Cardiovascular Medicine and Cardiovascular Institute, Department of Medicine, , Stanford University, , Stanford, , CA, , Jessica R. GolbusJessica R. Golbus https://orcid.org/0000-0002-9538-3926 , Division of Cardiovascular Diseases, Department of Internal Medicine, , University of Michigan, , Ann Arbor, , MI, , Michigan Integrated Center for Health Analytics and Medical Prediction (MiCHAMP), , University of Michigan, , Ann Arbor, , MI, , The Center for Clinical Management and Research, , Ann Arbor VA Medical Center, , Ann Arbor, , MI, , Erin M. SpauldingErin M. Spaulding https://orcid.org/0000-0001-8390-2277 , Johns Hopkins University School of Nursing, , Baltimore, , MD, , mTECH Center, Division of Cardiology, Department of Medicine, , Johns Hopkins University School of Medicine, , Baltimore, , MD, , Phillip H. HwangPhillip H. Hwang https://orcid.org/0000-0001-6780-6808 , Department of Epidemiology, , Boston University School of Public Health, , Boston, , MA, , Ana L. A. CiminelliAna L. A. Ciminelli https://orcid.org/0000-0002-3462-7595 , School of Medicine and Hospital das Clínicas Telehealth Center, , Universidade Federal de Minas Gerais, , Belo Horizonte, , Brazil, , Kathleen LacarKathleen Lacar , Center for Digital Health, , Stanford University, , Stanford, , CA, , Stanford University Division of Cardiovascular Medicine and Cardiovascular Institute, Department of Medicine, , Stanford University, , Stanford, , CA, , Mario Funes HernandezMario Funes Hernandez https://orcid.org/0000-0002-6545-3110 , Center for Digital Health, , Stanford University, , Stanford, , CA, , Stanford University Division of Cardiovascular Medicine and Cardiovascular Institute, Department of Medicine, , Stanford University, , Stanford, , CA, , Nisha A. GilotraNisha A. Gilotra https://orcid.org/0000-0002-4511-8008 , mTECH Center, Division of Cardiology, Department of Medicine, , Johns Hopkins University School of Medicine, , Baltimore, , MD, , Natasha DinNatasha Din https://orcid.org/0000-0001-5312-4451 , Center for Digital Health, , Stanford University, , Stanford, , CA, , Veterans Affairs Palo Alto Healthcare System, , Palo Alto, , CA, , Luisa C. C. BrantLuisa C. C. Brant https://orcid.org/0000-0002-7317-1367 , School of Medicine and Hospital das Clínicas Telehealth Center, , Universidade Federal de Minas Gerais, , Belo Horizonte, , Brazil, , Rhoda AuRhoda Au https://orcid.org/0000-0001-7742-4491 , Department of Epidemiology, , Boston University School of Public Health, , Boston, , MA, , Department of Anatomy and Neurobiology, , Boston University School of Medicine, , Boston, , MA, , Andrea BeatonAndrea Beaton https://orcid.org/0000-0002-4963-355X , Department of Pediatrics, , University of Cincinnati School of Medicine, , Cincinnati, , OH, , Department of Pediatrics, , The Heart Institute at Cincinnati Children's Hospital, , Cincinnati, , OH, , Brahmajee K. NallamothuBrahmajee K. Nallamothu https://orcid.org/0000-0003-4331-6649 , Division of Cardiovascular Diseases, Department of Internal Medicine, , University of Michigan, , Ann Arbor, , MI, , Michigan Integrated Center for Health Analytics and Medical Prediction (MiCHAMP), , University of Michigan, , Ann Arbor, , MI, , The Center for Clinical Management and Research, , Ann Arbor VA Medical Center, , Ann Arbor, , MI, , Chris T. LongeneckerChris T. Longenecker https://orcid.org/0000-0002-9468-0179 , Division of Cardiology and Department of Global Health, , University of Washington, , Seattle, , WA, , Seth S. MartinSeth S. Martin https://orcid.org/0000-0002-7021-7622 , mTECH Center, Division of Cardiology, Department of Medicine, , Johns Hopkins University School of Medicine, , Baltimore, , MD, , Michael P. DorschMichael P. Dorsch https://orcid.org/0000-0003-2910-1879 , College of Pharmacy, , University of Michigan, , Ann Arbor, , MI, and Alexander T. SandhuAlexander T. Sandhu https://orcid.org/0000-0003-3208-1143 , Center for Digital Health, , Stanford University, , Stanford, , CA, , Stanford University Division of Cardiovascular Medicine and Cardiovascular Institute, Department of Medicine, , Stanford University, , Stanford, , CA, , Veterans Affairs Palo Alto Healthcare System, , Palo Alto, , CA, Originally published16 Jan 2024https://doi.org/10.1161/JAHA.123.030952Journal of the American Heart Association. 2024;13:e030952Nonstandard Abbreviations and AcronymsGDMTguideline‐directed medical therapyHFrEFheart failure with reduced ejection fractionHeart failure (HF) is responsible for substantial morbidity among the estimated 6 million affected adults in the United States.1 For HF with reduced ejection fraction (HFrEF), optimal guideline‐directed medical therapy (GDMT) is estimated to reduce mortality by >70% in addition to improving quality of life.1 However, GDMT remains underused.2, 3 Therefore, there is a critical need to identify strategies to improve implementation of GDMT for patients with HF.HF remote monitoring programs have traditionally focused on lifestyle management, monitoring for signs of decompensation and diuretic adjustment rather than optimization of GDMT. The impact of such programs on clinical outcomes has been inconsistent. Given the clinical benefit of GDMT, focusing digital health interventions on medication optimization may improve their overall impact.4 In addition, interventions that have focused on improving HF medical therapy rates have traditionally focused on clinician or health system–level interventions, such as clinical decision support to encourage medication uptitration, implementation of pharmacist‐ or nurse‐led titration protocols, and outpatient audit‐and‐feedback interventions.5, 6 Many of these interventions have demonstrated effectiveness, but the magnitude of improvement in GDMT initiation and optimization has often been modest.2For optimal HF management, patients ideally need to attend clinic visits, take and manage medications frequently, monitor vital signs and weight, adjust lifestyle, cope with HF stress, and participate in cardiac rehabilitation. However, there is little prior research on enhancing patient engagement to improve the use of GDMT. The EPIC‐HF (Electronically Delivered, Patient‐Activation Tool for Intensification of Medications for Chronic Heart Failure with Reduced Ejection Fraction) trial illustrated the potential of this paradigm.7 The trial randomized 306 outpatients with HFrEF to usual care or to receive patient activation tools, including a 3‐minute video on the importance of GDMT and a 1‐page paper checklist on GDMT goals. There was nearly a 20% absolute increase in GDMT initiation or intensification among the patient activation arm at 30 days. This illustrates that improving patient knowledge and motivation can substantially improve GDMT rates.Patient engagement is essential for virtual HF programs designed to optimize GDMT.8 Digital health has the potential to expand such programs to focus on activating patients to advocate for optimal care. On the basis of prior literature and findings from patient, caregiver, and clinician panels in the American Heart Association Health Technology and Innovation Strategically Focused Research Network, this viewpoint identifies current barriers in the outpatient management of HFrEF and explores approaches by which digital technology–enabled patient engagement can enhance quality of care and improve outcomes among patients with HFrEF.GAPS IN OUTPATIENT HF CAREEfforts to improve quality of care among patients with HFrEF have primarily focused on the inpatient setting.9 The benefits to inpatient GDMT initiation and titration include readily available safety and tolerability data (vital signs and laboratory values), multidisciplinary care team resources (social work, pharmacy, and nutrition), and patient and caregivers' heightened focus on HF. However, the pressure to reduce hospital length of stay and the complexity of uptitrating several classes of GDMT medications during an acute HF exacerbation prohibit full GDMT optimization by the time of discharge. The need for rapid outpatient uptitration is further strengthened by the safety, tolerability and efficacy of rapid optimization, helped by NT‐proBNP testinG, of heart failure therapies trial,10 in which early optimization of medical therapy during and following a HF hospitalization reduced the composite of HF rehospitalization and all‐cause death by 34% (risk ratio, 0.66 [95% CI, 0.50–0.86]). In addition, many patients are diagnosed with HF in the ambulatory setting and may avoid hospitalization with timely initiation and optimization of GDMT. Therefore, effective strategies to improve outpatient optimization of GDMT for HFrEF are critical.There is suboptimal uptitration of GDMT for HFrEF in outpatient settings. The change the management of patients with heart failure registry demonstrated that outpatients are rarely initiated on additional therapies or have the dose of existing therapies uptitrated. Only 20% of patients with HFrEF were on target β‐blocker doses, and only 10% experienced any uptitration of their medical therapies over a 12‐month period.2 Hence, in the following sections, we will discuss the barriers to improve outpatient HF care and explore how promoting patient engagement via digital health technology could help overcome these challenges.CLINICIAN AND HEALTH SYSTEM BARRIERSMultiple factors impact clinician‐level decision‐making on GDMT optimization and contribute to therapeutic inertia. First, HF management is a rapidly evolving field, and knowledge gaps contribute to suboptimal treatment. Clinicians may underestimate the risk faced by outpatients with mild symptoms despite their substantially higher risk of hospitalization or death compared with patients receiving primary or secondary prevention for atherosclerotic cardiovascular disease.1 Second, clinicians manage multiple concurrent conditions and have limited time to dedicate to any given medical problem or medication adjustment.There are also critical structural system‐level barriers that contribute to suboptimal GDMT implementation and intensification. The limited capacity of outpatient clinics renders in‐office uptitration every 1 to 2 weeks challenging. Gaps in care are further exacerbated for patients with limited access to cardiovascular specialists, because of full‐time jobs or transportation limitations, especially in rural settings.11 In the Veterans Health Administration, patients living with HF who lived further from specialty care were less likely to be on ≥50% of the target dose of β‐blockers or angiotensin‐converting enzyme inhibitors and angiotensin II receptor blockers.12 Finally, there is a substantial financial burden to GDMT optimization that is driven by both the out‐of‐pocket costs of brand‐name medications and the direct and indirect costs of outpatient care, including travel time and lost work.Digital health can help address these barriers by facilitating protocolized uptitration of therapy outside of traditional office visits.5, 13 Multiple studies have demonstrated that remote management programs led by nurses or pharmacists have successfully increased GDMT via protocolized uptitration.5, 13 Such programs can address financial and time costs in addition to leveraging the expansion and acceptance of telemedicine to provide virtual medication optimization with less frequent in‐person visits. Digital health solutions, including smartphone applications and paired monitoring devices, can (1) enable capture, transmission, and summation of data, such as weight and vital signs; and (2) be used to semiautomate medication initiation and uptitration via protocols. Furthermore, medication titration can be rapidly and electronically transmitted back to patients following care team confirmation. Such an approach can facilitate virtual management. Wong and colleagues successfully applied this approach in a pilot study of 20 patients with HFrEF using the Biofourmis platform that adjusted GDMT based on home vitals and a titration algorithm.14Digital health interventions must align with clinician workflows to enhance better clinical outcomes. For example, the OptiLink HF trial15 evaluated the impact of impedance‐based remote monitoring by implantable cardioverter‐defibrillator and cardiac resynchronization therapy with defibrillator devices among 1002 patients with advanced HF. They found only 55.5% of remote monitoring alerts led to contact by the clinical team consistent with the study protocol. Among patients in whom alerts led to protocolized contact, remote monitoring was associated with a reduction in the composite of cardiovascular death or HF hospitalization compared with usual care. Among patients in whom alerts did not lead to protocolized contact, there was no significant difference in outcomes compared with usual care. This study illustrates how successful interventions require alignment with clinician workflows.Implementing standardized best practices counters gaps in knowledge and the hesitance of uptitration when a patient is presumably stable. However, the effectiveness of such approaches remains reliant on patient self‐monitoring and patient‐clinician agreement on recommendations. For these reasons, there are often still large gaps in GDMT after the use of protocolized uptitration.5 As described in the following section, digital health tools can be used to empower patients to advocate for GDMT optimization. If the patient is aligned with the goal of HF GDMT uptitration and is equipped to conduct self‐monitoring, a virtual optimization program will likely have larger effects.PATIENT‐LEVEL BARRIERSMultiple patient‐level barriers contribute to limited GDMT optimization. First, there may be gaps in patients' understanding of the role of GDMT. HF education has traditionally emphasized salt and fluid restriction to reduce the risk of HF exacerbation, rather than the need for GDMT. Although an incident HF hospitalization is an ideal setting to introduce the importance of GDMT and the plan for continued uptitration, most patients will need outpatient reinforcement of these concepts and not all patients with HF are hospitalized at the time of diagnosis. The EPIC‐HF trial illustrated the impact of an animated previsit video to explain the importance of GDMT.7 Such educational content can be delivered longitudinally online or via smartphone applications. Delivering this information in manageable modules personalized to where a patient is in his/her journey may further increase its effectiveness.Beyond understanding the importance of GDMT, patients would likely benefit from a concrete understanding of how their medication regimen compares with optimal treatment. The EPIC‐HF trial used a 1‐page checklist that compared their medication regimen with target GDMT doses to illustrate gaps in their care.7 Smartphone applications can be used to help patients longitudinally track how their medication regimen compares with optimal GDMT. Furthermore, integrating the checklist with remote vital sign monitoring can provide actionable insights into how their regimen could be improved. Such data may empower patients to discuss uptitration with their care team.A second patient‐level barrier is the challenge of conceptualizing the benefits of therapy. Patients often feel better after decongestion; this symptomatic improvement reduces their urgency to optimize medical treatment. Educational content should emphasize the benefits of HF GDMT independent of baseline symptoms. In addition, most HFrEF therapies improve patient‐reported health status.1 Therefore, mobile health tools could be used to monitor the improvement in health status that occurs over time with GDMT optimization (eg, via the Kansas City Cardiomyopathy Questionnaire). Such tracking can provide biofeedback, reinforcing patient commitment to adhere to therapy and continue uptitration.16 A systematic review found applications providing biofeedback (ie, weight monitoring, blood pressure, or medication adherence) resulted in reduced hospitalizations and improved HF knowledge.16 Combining data on weight and medication adherence with structured assessments of patient‐reported health status may accentuate the effect of biofeedback.17A third potential barrier to GDMT optimization is patient concerns about the safety of rapid, virtual uptitration.18 Systematically monitoring laboratory values, vital signs, and symptoms via wireless blood pressure monitors/scales and application‐recorded patient‐reported health status may reassure patients that their response to medication changes is being monitored by their clinical team. Knowing the data are connected to the clinical team may itself improve patient adherence to monitoring.A fourth barrier to GDMT optimization is the high prevalence of cognitive impairment found in patients with HF,19 ranging from 25% to 75%, depending on the criteria used. Cognitive impairment can significantly impair a patient's ability to perform self‐monitoring, which is critical for GDMT optimization. Mobile health technologies can be used to identify patients with cognitive impairment via novel digital biomarkers, such as patient voice20; after identifying these patients, mobile health technology can facilitate family and caregivers being more involved in their care.While delivering guideline‐recommended care, clinicians also need to align care with patient priorities. This process of shared decision‐making is a challenging balance as patients living with HF have competing comorbidities, in addition to non–health‐related priorities. An average patient with HFrEF takes 6.8 prescription medications per day21 and has >5 noncardiac comorbidities.22 Patient education can help patients and their caregivers understand the potential benefits of GDMT in terms of both survival and quality of life. This will help patients make informed decisions about GDMT optimization that align with their priorities.A critical challenge for digital health solutions is being accessible to the broad range of patients experiencing HF. In addition, using digital tools can be challenging for patients with lower technology and health literacy. Ensuring digital interventions improve, rather than worsen, health equity requires such tools to be designed and tested across a broad range of patients. Health systems must both facilitate access to such interventions and provide human support to facilitate patients with lower technologic literacy using such tools.Remote HF management has focused on capturing information from patients and sharing it with clinicians to enable remote monitoring for decompensation and, more recently, to facilitate GDMT optimization. We believe there is a critical opportunity to test how these data can be shared with patients and caregivers to help them be their own best advocates for GDMT optimization.THE PATH FORWARDLeveraging digital health technologies can enhance the quality of care by not only reducing clinician burden, but also empowering patients and caregivers as active partners. The path forward starts with developing tools based on our understanding of barriers, patient motivation, and implementation science and then rigorously evaluating them in clinical trials. Then, we must focus on adapting effective tools to fit the unique needs of different populations and settings to improve not only overall quality of care but also health equity.23The American Heart Association Health Technology and Innovation Strategically Focused Research Network has identified multiple goals for digital health technology to substantially improve GDMT initiation and target dose achievement based on human‐centered design sessions with patients, caregivers, and clinicians. A digital intervention toolkit must effectively address the barriers discussed above by (a) facilitating timely GDMT initiation and optimization for clinicians, (b) empowering patients with education on the health outcomes' impact of GDMT and when it should be optimized, and (c) reducing barriers for patients and clinicians alike in adjusting GDMT by leveraging virtual rather than in‐person settings (Figure). Not achieving these goals may substantially undermine the impact of a digital health intervention on GDMT use and clinical outcomes.Download figureDownload PowerPointFigure . Conceptual framework for leveraging digital health to facilitate patient engagement in heart failure (HF) management.This figure highlights the barriers that exist at the patient, clinician, and health system levels in optimizing guideline‐directed medical therapy (GDMT) for patients with HF. Digital health technologies can overcome these barriers by allowing for regular GDMT adjustments, enabling virtual medication changes without the need for office visits, and emphasizing the importance of GDMT uptitration to patients. To move forward, a digital toolkit must be developed that focuses on patient education, including visualization, gamification, goal setting, and biofeedback. This toolkit can empower patients and caregivers as active partners and advocates, and its effectiveness can be rigorously evaluated in clinical trials using our understanding of barriers, patient motivation, and implementation science. By increasing access to care, improving GDMT, promoting healthy lifestyle decisions, and improving adherence, this toolkit can ultimately enhance quality of life, decrease hospitalization and cost of care, and increase survival. Created with BioRender.com.An HF digital health intervention should provide recommendations for GDMT optimization that integrate patient‐specific factors and clinician preferences. The intervention should be consistent with guideline‐based recommendations but can be flexible. There is not a single correct GDMT therapy order. Therefore, clinicians with experience in HF management may prefer an intervention that allows customization within the bounds of evidence. In this case, the focus would be helping clinicians implement their plan efficiently rather than telling them the right next step. For others with less expertise in HF management, a more prescriptive approach may be preferable. But most important, the intervention should include recommendations at frequent, regular intervals to optimize GDMT based on the prespecified protocol to counteract the inertia that is prevalent throughout chronic disease management.Several strategies can be used to promote patients and caregivers becoming their own most powerful advocates for safe, timely GDMT optimization. First, education should begin early in the treatment process, such as during an initial HF admission or after outpatient HF diagnosis, when patients are highly engaged in learning about their diagnosis. The education should focus on the benefits of timely optimization of GDMT. This should include an overview of the longitudinal treatment plan; patients may accept multiple titration steps if explained upfront rather than occurring as seemingly unexpected changes at later visits. Education should be available longitudinally to reinforce and expand prior material. When a new therapy is recommended, patients should have access to materials that explain the expected benefits and risks. Second, novel approaches to motivate patients to advocate for therapy optimization should be tested. This may include medication checklists, visualizations of expected GDMT benefits, gamification of GDMT achievement, or biofeedback. Patient empowerment is not only a valuable tool for improving GDMT, it is also consistent with the principles of shared decision‐making. Patients should have control and knowledge about their health. Patient activation through electronic reminders can motivate conversations with providers, and this approach was shown to improve use of GDMT when given before an upcoming clinic visit in the EPIC‐HF trial.7 Digital health tools that provide access to personal health data in a manner that is understandable and actionable may provide such empowerment.Finally, a digital intervention should simplify therapy optimization for care teams and patients. Integrating ambulatory measurements, including laboratory values, vital signs, and symptoms, with electronic health record data and preestablished protocols should make remote monitoring more efficient for clinicians. Facilitating uptitration with fewer office visits should reduce patient financial and time burden and create clinic availability for patients with worsening HF. Ideally, interventions should also meet remote patient monitoring reimbursement standards. All of these advantages could tackle the extra burdens of providing GDMT in rural communities and those with limited numbers of providers. Having a viable financial model in both the fee‐for‐service system and aligning with the transition to value‐based payment will be critical for health system support and uptake.Digital health technologies can be impactful if they promote care with proven clinical benefit, including GDMT in patients with HF. We believe our proposed toolkit can enhance HF outcomes through GDMT optimization. The current literature has not yet demonstrated the efficacy and utility of digital health in this regard. Until clinical benefit is proven in clinical trials, implementation will be difficult. Future research should leverage pragmatic methods that allow efficient evaluation of digital health tools embedded within clinical workflows. Such approaches will also allow researchers to better understand the clinical context in which therapies are effective. Even then, widespread implementation will need to overcome multiple important barriers, including data challenges (access, ownership, security, and EMR integration), privacy and financial concern, digital literacy, access disparities, and user retention. Overcoming these barriers will require bringing together experts in clinical care, informatics, health system design and implementation, and patient engagement. Once there is evidence for effective therapies, implementation science will be critical to guide how barriers can be overcome to scale such technologies in a broad and equitable manner.With the rapid expansion of digital health interventions, the systematic evaluation of different approaches will be critical. Systematic reviews that comprehensively catalogue different tools, evaluate their impact on outcomes, and identify their limitations will improve the future design and evaluation of digital health interventions.CONCLUSIONSDigital health technologies can change how we care for patients with chronic diseases, such as HF. The monitoring, education, and communication that were previously confined to brief visits in a clinician office can now be expanded to the time and space that works for the patient. Such expansion not only amplifies the data we can incorporate into clinical decision‐making but also allows us to transform how we engage patients and caregivers in driving their care. For HF, successfully breaking through long‐standing outpatient therapeutic inertia would have a substantial impact on morbidity and mortality for millions.Sources of FundingThis work was supported by the American Heart Association Health Technology and Innovation Strategically Focused Research Network.DisclosuresDrs Azizi, Spaulding, Gilotra, Martin, Golbus, and Dorsch were/are funded by American Heart Association Health Technology and Innovation Strategically Focused Research Network. Dr Sandhu is supported by the National Heart, Lung, and Blood Institute (1K23HL151672‐03). Dr Golbus is supported by the National, Heart, Lung, and Blood Institute (1K23HL168220‐01 and L30HL143700). Dr Nallamothu is a principal investigator or coinvestigator on research grants from the National Institutes of Health, Veterans Affairs Health Services Research and Development Service, the American Heart Association, Janssen, and Apple, Inc. He also receives compensation as Editor‐in‐Chief of Circulation: Cardiovascular Quality & Outcomes, a journal of the American Heart Association. Finally, he is a coinventor on US utility patent number US15/356,012 (US20170148158A1) entitled "Automated Analysis of Vasculature in Coronary Angiograms" that uses software technology with signal processing and machine learning to automate the reading of coronary angiograms, held by the University of Michigan. The patent is licensed to AngioInsight, Inc, in which Dr Nallamothu holds ownership shares and receives consultancy fees. Dr Martin: under a license agreement between Corrie Health and the Johns Hopkins University, the university owns equity in Corrie Health,
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