BACKGROUND: Up to 50% of antibiotic prescriptions for upper respiratory infections (URIs) are inappropriate. Clinical decision support (CDS) systems to mitigate unnecessary antibiotic prescriptions have been implemented into electronic health records, but their use by providers has been limited. OBJECTIVE: As a delegation protocol, we adapted a validated electronic health record-integrated clinical prediction rule (iCPR) CDS-based intervention for registered nurses (RNs), consisting of triage to identify patients with low-acuity URI followed by CDS-guided RN visits. It was implemented in February 2022 as a randomized controlled stepped-wedge trial in 43 primary and urgent care practices within 4 academic health systems in New York, Wisconsin, and Utah. While issues were pragmatically addressed as they arose, a systematic assessment of the barriers to implementation is needed to better understand and address these barriers. METHODS: We performed a retrospective case study, collecting quantitative and qualitative data regarding clinical workflows and triage-template use from expert interviews, study surveys, routine check-ins with practice personnel, and chart reviews over the first year of implementation of the iCPR intervention. Guided by the updated CFIR (Consolidated Framework for Implementation Research), we characterized the initial barriers to implementing a URI iCPR intervention for RNs in ambulatory care. CFIR constructs were coded as missing, neutral, weak, or strong implementation factors. RESULTS: Barriers were identified within all implementation domains. The strongest barriers were found in the outer setting, with those factors trickling down to impact the inner setting. Local conditions driven by COVID-19 served as one of the strongest barriers, impacting attitudes among practice staff and ultimately contributing to a work infrastructure characterized by staff changes, RN shortages and turnover, and competing responsibilities. Policies and laws regarding scope of practice of RNs varied by state and institutional application of those laws, with some allowing more clinical autonomy for RNs. This necessitated different study procedures at each study site to meet practice requirements, increasing innovation complexity. Similarly, institutional policies led to varying levels of compatibility with existing triage, rooming, and documentation workflows. These workflow conflicts were compounded by limited available resources, as well as an implementation climate of optional participation, few participation incentives, and thus low relative priority compared to other clinical duties. CONCLUSIONS: Both between and within health care systems, significant variability existed in workflows for patient intake and triage. Even in a relatively straightforward clinical workflow, workflow and cultural differences appreciably impacted intervention adoption. Takeaways from this study can be applied to other RN delegation protocol implementations of new and innovative CDS tools within existing workflows to support integration and improve uptake. When implementing a system-wide clinical care intervention, considerations must be made for variability in culture and workflows at the state, health system, practice, and individual levels. TRIAL REGISTRATION: ClinicalTrials.gov NCT04255303; https://clinicaltrials.gov/ct2/show/NCT04255303.
Digital health implementations and investments continue to expand. As the reliance on digital health increases, it is imperative to implement technologies with inclusive and accessible approaches. A conceptual model can be used to guide equity-focused digital health implementations to improve suitability and uptake in diverse populations. The objective of this study is expand an implementation model with recommendations on the equitable implementation of new digital health technologies. The Digital Health Equity-Focused Implementation Research (DH-EquIR) conceptual model was developed based on a rigorous review of digital health implementation and health equity literature. The Equity-Focused Implementation Research for Health Programs (EquIR) model was used as a starting point and merged with digital equity and digital health implementation models. Existing theoretical frameworks and models were appraised as well as individual equity-sensitive implementation studies. Patient and program-related concepts related to digital equity, digital health implementation, and assessment of social/digital determinants of health were included. Sixty-two articles were analyzed to inform the adaption of the EquIR model for digital health. These articles included digital health equity models and frameworks, digital health implementation models and frameworks, research articles, guidelines, and concept analyses. Concepts were organized into EquIR conceptual groupings, including population health status, planning the program, designing the program, implementing the program, and equity-focused implementation outcomes. The adapted DH-EquIR conceptual model diagram was created as well as detailed tables displaying related equity concepts, evidence gaps in source articles, and analysis of existing equity-related models and tools. The DH-EquIR model serves to guide digital health developers and implementation specialists to promote the inclusion of health-equity planning in every phase of implementation. In addition, it can assist researchers and product developers to avoid repeating the mistakes that have led to inequities in the implementation of digital health across populations.
OBJECTIVES: To evaluate demographic biases in diagnostic accuracy and health advice between generative artificial intelligence (AI) (ChatGPT GPT-4) and traditional symptom checkers like WebMD. MATERIALS AND METHODS: Combination symptom and demographic vignettes were developed for 27 most common symptom complaints. Standardized prompts, written from a patient perspective, with varying demographic permutations of age, sex, and race/ethnicity were entered into ChatGPT (GPT-4) between July and August 2023. In total, 3 runs of 540 ChatGPT prompts were compared to the corresponding WebMD Symptom Checker output using a mixed-methods approach. In addition to diagnostic correctness, the associated text generated by ChatGPT was analyzed for readability (using Flesch-Kincaid Grade Level) and qualitative aspects like disclaimers and demographic tailoring. RESULTS: ChatGPT matched WebMD in 91% of diagnoses, with a 24% top diagnosis match rate. Diagnostic accuracy was not significantly different across demographic groups, including age, race/ethnicity, and sex. ChatGPT's urgent care recommendations and demographic tailoring were presented significantly more to 75-year-olds versus 25-year-olds (P < .01) but were not statistically different among race/ethnicity and sex groups. The GPT text was suitable for college students, with no significant demographic variability. DISCUSSION: The use of non-health-tailored generative AI, like ChatGPT, for simple symptom-checking functions provides comparable diagnostic accuracy to commercially available symptom checkers and does not demonstrate significant demographic bias in this setting. The text accompanying differential diagnoses, however, suggests demographic tailoring that could potentially introduce bias. CONCLUSION: These results highlight the need for continued rigorous evaluation of AI-driven medical platforms, focusing on demographic biases to ensure equitable care.
The COVID-19 pandemic has boosted digital health utilization, raising concerns about increased physicians' after-hours clinical work ("work-outside-work"). The surge in patients' digital messages and additional time spent on work-outside-work by telemedicine providers underscores the need to evaluate the connection between digital health utilization and physicians' after-hours commitments. We examined the impact on physicians' workload from two types of digital demands - patients' messages requesting medical advice (PMARs) sent to physicians' inbox (inbasket), and telemedicine. Our study included 1716 ambulatory-care physicians in New York City regularly practicing between November 2022 and March 2023. Regression analyses assessed primary and interaction effects of (PMARs) and telemedicine on work-outside-work. The study revealed a significant effect of PMARs on physicians' work-outside-work and that this relationship is moderated by physicians' specialties. Non-primary care physicians or specialists experienced a more pronounced effect than their primary care peers. Analysis of their telemedicine load revealed that primary care physicians received fewer PMARs and spent less time in work-outside-work with more telemedicine. Specialists faced increased PMARs and did more work-outside-work as telemedicine visits increased which could be due to the difference in patient panels. Reducing PMAR volumes and efficient inbasket management strategies needed to reduce physicians' work-outside-work. Policymakers need to be cognizant of potential disruptions in physicians carefully balanced workload caused by the digital health services.
BACKGROUND: Overprescribing of antibiotics for acute respiratory infections (ARIs) remains a major issue in outpatient settings. Use of clinical prediction rules (CPRs) can reduce inappropriate antibiotic prescribing but they remain underutilized by physicians and advanced practice providers. A registered nurse (RN)-led model of an electronic health record-integrated CPR (iCPR) for low-acuity ARIs may be an effective alternative to address the barriers to a physician-driven model. METHODS: Following qualitative usability testing, we will conduct a stepped-wedge practice-level cluster randomized controlled trial (RCT) examining the effect of iCPR-guided RN care for low acuity patients with ARI. The primary hypothesis to be tested is: Implementation of RN-led iCPR tools will reduce antibiotic prescribing across diverse primary care settings. Specifically, this study aims to: (1) determine the impact of iCPRs on rapid strep test and chest x-ray ordering and antibiotic prescribing rates when used by RNs; (2) examine resource use patterns and cost-effectiveness of RN visits across diverse clinical settings; (3) determine the impact of iCPR-guided care on patient satisfaction; and (4) ascertain the effect of the intervention on RN and physician burnout. DISCUSSION: This study represents an innovative approach to using an iCPR model led by RNs and specifically designed to address inappropriate antibiotic prescribing. This study has the potential to provide guidance on the effectiveness of delegating care of low-acuity patients with ARIs to RNs to increase use of iCPRs and reduce antibiotic overprescribing for ARIs in outpatient settings. TRIAL REGISTRATION: ClinicalTrials.gov Identifier: NCT04255303, Registered February 5 2020, https://clinicaltrials.gov/ct2/show/NCT04255303 .
Decision Support Systems, Clinical , Respiratory Tract Infections , Humans , Anti-Bacterial Agents/therapeutic use , Nurse's Role , Respiratory Tract Infections/drug therapy , Electronic Health Records , Practice Patterns, Physicians' , Randomized Controlled Trials as Topic
Indiscriminate use of predictive models incorporating race can reinforce biases present in source data and lead to an exacerbation of health disparities. In some countries, such as the United States, there is therefore a push to remove race from prediction models; however, there are still many prediction models that use race as an input. Biomedical informaticists who are given the responsibility of using these predictive models in healthcare environments are likely to be faced with questions like how to deal with race covariates in these models. Thus, there is a need for a pragmatic framework to help model users think through how to include race in their chosen model so as to avoid inadvertently exacerbating disparities. In this paper, we use the case study of lung cancer screening to propose a simple framework to guide how model users can approach the use (or non-use) of race inputs in the predictive models they are tasked with leveraging in electronic health records and clinical workflows.
Early Detection of Cancer , Lung Neoplasms , Humans , United States , Lung Neoplasms/diagnosis , Electronic Health Records
BACKGROUND: Incorporation of pharmacy fill data into the electronic health record has enabled calculations of medication adherence, as measured by proportion of days covered (PDC), to be displayed to clinicians. Although PDC values help identify patients who may be nonadherent to their medications, it does not provide information on the reasons for medication-taking behaviors. OBJECTIVE: To characterize self-reported adherence status to antihypertensive medications among patients with low refill medication adherence. Our secondary objective was to identify the most common reasons for nonadherence and examine the patient sociodemographic characteristics associated with these barriers. METHODS: Participants were adult patients seen in primary care clinics of a large, urban health system and on antihypertensive therapy with a PDC of less than 80% based on 6-month linked electronic health record-pharmacy fill data. We administered a validated medication adherence screener and a survey assessing reasons for antihypertensive medication nonadherence. We used descriptive statistics to characterize these data and logistic and Poisson regression models to assess the relationship between sociodemographic characteristics and adherence barriers. RESULTS: The survey was completed by 242 patients (57% female; 61.2% White; 79.8% not Latino/a or Hispanic). Of these patients, 45% reported missing doses of their medications in the last 7 days. In addition, 48% endorsed having at least 1 barrier to adherence and 38.4% endorsed 2 or more barriers. The most common barriers were being busy and having difficulty remembering to take medications. Compared with White participants, Black participants (incident rate ratio = 2.49; 95% CI = 1.93-3.22) and participants of other races (incident rate ratio = 2.16; 95% CI = 1.62-2.89) experienced a greater number of barriers. CONCLUSIONS: Nearly half of patients with low PDC reported nonadherence in the prior week, suggesting PDC can be used as a screening tool. Augmenting PDC with brief self-report tools can provide insights into the reasons for nonadherence. DISCLOSURES: Dr Kharmats, Ms Martinez, Dr Belli, Ms Zhao, Dr Mann, Dr Schoenthaler, and Dr Blecker received grants from the National Institute of Health/National Heart, Lung, Blood Institute. Dr Voils holds a license by Duke University for the DOSE-Nonadherence measure and is a consultant for New York University Grossman School of Medicine. This research was supported by the NIH (R01HL156355). Dr Kharmats received a postdoctoral training grant from the National Institutes of Health (5T32HL129953-04). Dr Voils was supported by a Research Career Scientist award from the Health Services Research & Development Service of the Department of Veterans Affairs (RCS 14-443). The content of this manuscript is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health or the United States Government.
Antihypertensive Agents , Pharmaceutical Services , Adult , Humans , United States , Female , Male , Antihypertensive Agents/therapeutic use , Self Report , New York , Medication Adherence
BACKGROUND: Diabetes mellitus is a common medical complication of pregnancy, and its treatment is complex. Recent years have seen an increase in the application of mobile health tools and advanced technologies, such as remote patient monitoring, with the aim of improving care for diabetes mellitus in pregnancy. Previous studies of these technologies for the treatment of diabetes in pregnancy have been small and have not clearly shown clinical benefit with implementation. OBJECTIVE: Remote patient monitoring allows clinicians to monitor patients' health data (such as glucose values) in near real-time, between office visits, to make timely adjustments to care. Our objective was to determine if using remote patient monitoring for the management of diabetes in pregnancy leads to an improvement in maternal and neonatal outcomes. STUDY DESIGN: This was a retrospective cohort study of pregnant patients with diabetes mellitus managed by the maternal-fetal medicine practice at one academic institution between October 2019 and April 2021. This practice transitioned from paper-based blood glucose logs to remote patient monitoring in February 2020. Remote patient monitoring options included (1) device integration with Bluetooth glucometers that automatically uploaded measured glucose values to the patient's Epic MyChart application or (2) manual entry in which patients manually logged their glucose readings into their MyChart application. Values in the MyChart application directly transferred to the patient's electronic health record for review and management by clinicians. In total, 533 patients were studied. We compared 173 patients managed with paper logs to 360 patients managed with remote patient monitoring (176 device integration and 184 manual entry). Our primary outcomes were composite maternal morbidity (which included third- and fourth-degree lacerations, chorioamnionitis, postpartum hemorrhage requiring transfusion, postpartum hysterectomy, wound infection or separation, venous thromboembolism, and maternal admission to the intensive care unit) and composite neonatal morbidity (which included umbilical cord pH <7.00, 5 minute Apgar score <7, respiratory morbidity, hyperbilirubinemia, meconium aspiration, intraventricular hemorrhage, necrotizing enterocolitis, sepsis, pneumonia, seizures, hypoxic ischemic encephalopathy, shoulder dystocia, trauma, brain or body cooling, and neonatal intensive care unit admission). Secondary outcomes were measures of glycemic control and the individual components of the primary composite outcomes. We also performed a secondary analysis in which the patients who used the two different remote patient monitoring options (device integration vs manual entry) were compared. Chi-square, Fisher's exact, 2-sample t, and Mann-Whitney tests were used to compare the groups. A result was considered statistically significant at P<.05. RESULTS: Maternal baseline characteristics were not significantly different between the remote patient monitoring and paper groups aside from a slightly higher baseline rate of chronic hypertension in the remote patient monitoring group (6.1% vs 1.2%; P=.011). The primary outcomes of composite maternal and composite neonatal morbidity were not significantly different between the groups. However, remote patient monitoring patients submitted more glucose values (177 vs 146; P=.008), were more likely to achieve glycemic control in target range (79.2% vs 52.0%; P<.0001), and achieved the target range sooner (median, 3.3 vs 4.1 weeks; P=.025) than patients managed with paper logs. This was achieved without increasing in-person visits. Remote patient monitoring patients had lower rates of preeclampsia (5.8% vs 15.0%; P=.0006) and their infants had lower rates of neonatal hypoglycemia in the first 24 hours of life (29.8% vs 51.7%; P<.0001). CONCLUSION: Remote patient monitoring for the management of diabetes mellitus in pregnancy is superior to a traditional paper-based approach in achieving glycemic control and is associated with improved maternal and neonatal outcomes.
Diabetes, Gestational , Infant, Newborn, Diseases , Meconium Aspiration Syndrome , Pregnancy , Infant , Female , Humans , Infant, Newborn , Retrospective Studies , Diabetes, Gestational/drug therapy , Blood Glucose , Infant, Newborn, Diseases/therapy , Monitoring, Physiologic , Pregnancy Outcome
Importance: Clinical decision support (CDS) algorithms are increasingly being implemented in health care systems to identify patients for specialty care. However, systematic differences in missingness of electronic health record (EHR) data may lead to disparities in identification by CDS algorithms. Objective: To examine the availability and comprehensiveness of cancer family history information (FHI) in patients' EHRs by sex, race, Hispanic or Latino ethnicity, and language preference in 2 large health care systems in 2021. Design, Setting, and Participants: This retrospective EHR quality improvement study used EHR data from 2 health care systems: University of Utah Health (UHealth) and NYU Langone Health (NYULH). Participants included patients aged 25 to 60 years who had a primary care appointment in the previous 3 years. Data were collected or abstracted from the EHR from December 10, 2020, to October 31, 2021, and analyzed from June 15 to October 31, 2021. Exposures: Prior collection of cancer FHI in primary care settings. Main Outcomes and Measures: Availability was defined as having any FHI and any cancer FHI in the EHR and was examined at the patient level. Comprehensiveness was defined as whether a cancer family history observation in the EHR specified the type of cancer diagnosed in a family member, the relationship of the family member to the patient, and the age at onset for the family member and was examined at the observation level. Results: Among 144â¯484 patients in the UHealth system, 53.6% were women; 74.4% were non-Hispanic or non-Latino and 67.6% were White; and 83.0% had an English language preference. Among 377â¯621 patients in the NYULH system, 55.3% were women; 63.2% were non-Hispanic or non-Latino, and 55.3% were White; and 89.9% had an English language preference. Patients from historically medically undeserved groups-specifically, Black vs White patients (UHealth: 17.3% [95% CI, 16.1%-18.6%] vs 42.8% [95% CI, 42.5%-43.1%]; NYULH: 24.4% [95% CI, 24.0%-24.8%] vs 33.8% [95% CI, 33.6%-34.0%]), Hispanic or Latino vs non-Hispanic or non-Latino patients (UHealth: 27.2% [95% CI, 26.5%-27.8%] vs 40.2% [95% CI, 39.9%-40.5%]; NYULH: 24.4% [95% CI, 24.1%-24.7%] vs 31.6% [95% CI, 31.4%-31.8%]), Spanish-speaking vs English-speaking patients (UHealth: 18.4% [95% CI, 17.2%-19.1%] vs 40.0% [95% CI, 39.7%-40.3%]; NYULH: 15.1% [95% CI, 14.6%-15.6%] vs 31.1% [95% CI, 30.9%-31.2%), and men vs women (UHealth: 30.8% [95% CI, 30.4%-31.2%] vs 43.0% [95% CI, 42.6%-43.3%]; NYULH: 23.1% [95% CI, 22.9%-23.3%] vs 34.9% [95% CI, 34.7%-35.1%])-had significantly lower availability and comprehensiveness of cancer FHI (P < .001). Conclusions and Relevance: These findings suggest that systematic differences in the availability and comprehensiveness of FHI in the EHR may introduce informative presence bias as inputs to CDS algorithms. The observed differences may also exacerbate disparities for medically underserved groups. System-, clinician-, and patient-level efforts are needed to improve the collection of FHI.
Electronic Health Records , Neoplasms , Delivery of Health Care , Female , Hispanic or Latino , Humans , Language , Male , Retrospective Studies
Rationale & Objective: To design and implement clinical decision support incorporating a validated risk prediction estimate of kidney failure in primary care clinics and to evaluate the impact on stage-appropriate monitoring and referral. Study Design: Block-randomized, pragmatic clinical trial. Setting & Participants: Ten primary care clinics in the greater Boston area. Patients with stage 3-5 chronic kidney disease (CKD) were included. Patients were randomized within each primary care physician panel through a block randomization approach. The trial occurred between December 4, 2015, and December 3, 2016. Intervention: Point-of-care noninterruptive clinical decision support that delivered the 5-year kidney failure risk equation as well as recommendations for stage-appropriate monitoring and referral to nephrology. Outcomes: The primary outcome was as follows: Urine and serum laboratory monitoring test findings measured at one timepoint 6 months after the initial primary care visit and analyzed only in patients who had not undergone the recommended monitoring test in the preceding 12 months. The secondary outcome was nephrology referral in patients with a calculated kidney failure risk equation value of >10% measured at one timepoint 6 months after the initial primary care visit. Results: The clinical decision support application requested and processed 569,533 Continuity of Care Documents during the study period. Of these, 41,842 (7.3%) documents led to a diagnosis of stage 3, 4, or 5 CKD by the clinical decision support application. A total of 5,590 patients with stage 3, 4, or 5 CKD were randomized and included in the study. The link to the clinical decision support application was clicked 122 times by 57 primary care physicians. There was no association between the clinical decision support intervention and the primary outcome. There was a small but statistically significant difference in nephrology referral, with a higher rate of referral in the control arm. Limitations: Contamination within provider and clinic may have attenuated the impact of the intervention and may have biased the result toward null. Conclusions: The noninterruptive design of the clinical decision support was selected to prevent cognitive overload; however, the design led to a very low rate of use and ultimately did not improve stage-appropriate monitoring. Funding: Research reported in this publication was supported by the National Institute of Diabetes and Digestive and Kidney Diseases of the National Institutes of Health under award K23DK097187. Trial Registration: ClinicalTrials.gov Identifier: NCT02990897.
The COVID-19 pandemic accelerated the adoption of remote patient monitoring technology, which offers exciting opportunities for expanded connected care at a distance. However, while the mode of clinicians' interactions with patients and their health data has transformed, the larger framework of how we deliver care is still driven by a model of episodic care that does not facilitate this new frontier. Fully realizing a transformation to a system of continuous connected care augmented by remote monitoring technology will require a shift in clinicians' and health systems' approach to care delivery technology and its associated data volume and complexity. In this article, we present a solution that organizes and optimizes the interaction of automated technologies with human oversight, allowing for the maximal use of data-rich tools while preserving the pieces of medical care considered uniquely human. We review implications of this "augmented continuous connected care" model of remote patient monitoring for clinical practice and offer human-centered design-informed next steps to encourage innovation around these important issues.
COVID-19 , Telemedicine , Delivery of Health Care , Government Programs , Humans , Pandemics
BACKGROUND: Cancer genetic testing to assess an individual's cancer risk and to enable genomics-informed cancer treatment has grown exponentially in the past decade. Because of this continued growth and a shortage of health care workers, there is a need for automated strategies that provide high-quality genetics services to patients to reduce the clinical demand for genetics providers. Conversational agents have shown promise in managing mental health, pain, and other chronic conditions and are increasingly being used in cancer genetic services. However, research on how patients interact with these agents to satisfy their information needs is limited. OBJECTIVE: Our primary aim is to assess user interactions with a conversational agent for pretest genetics education. METHODS: We conducted a feasibility study of user interactions with a conversational agent who delivers pretest genetics education to primary care patients without cancer who are eligible for cancer genetic evaluation. The conversational agent provided scripted content similar to that delivered in a pretest genetic counseling visit for cancer genetic testing. Outside of a core set of information delivered to all patients, users were able to navigate within the chat to request additional content in their areas of interest. An artificial intelligence-based preprogrammed library was also established to allow users to ask open-ended questions to the conversational agent. Transcripts of the interactions were recorded. Here, we describe the information selected, time spent to complete the chat, and use of the open-ended question feature. Descriptive statistics were used for quantitative measures, and thematic analyses were used for qualitative responses. RESULTS: We invited 103 patients to participate, of which 88.3% (91/103) were offered access to the conversational agent, 39% (36/91) started the chat, and 32% (30/91) completed the chat. Most users who completed the chat indicated that they wanted to continue with genetic testing (21/30, 70%), few were unsure (9/30, 30%), and no patient declined to move forward with testing. Those who decided to test spent an average of 10 (SD 2.57) minutes on the chat, selected an average of 1.87 (SD 1.2) additional pieces of information, and generally did not ask open-ended questions. Those who were unsure spent 4 more minutes on average (mean 14.1, SD 7.41; P=.03) on the chat, selected an average of 3.67 (SD 2.9) additional pieces of information, and asked at least one open-ended question. CONCLUSIONS: The pretest chat provided enough information for most patients to decide on cancer genetic testing, as indicated by the small number of open-ended questions. A subset of participants were still unsure about receiving genetic testing and may require additional education or interpersonal support before making a testing decision. Conversational agents have the potential to become a scalable alternative for pretest genetics education, reducing the clinical demand on genetics providers.
Artificial Intelligence , Communication , Chronic Disease , Genetic Counseling , Humans , Mental Health
BACKGROUND: Recent linkages between electronic health records (EHRs) and pharmacy data hold opportunity for up-to-date assessment of medication adherence at the point of care. OBJECTIVE: To validate linked EHR-pharmacy data, which can be used for point-of-care interventions for concordance with insurance claims data for patients in a large health care delivery system. METHODS: We performed a retrospective cohort study of adult patients with an active antihypertensive medication order and seen as outpatients between August 25, 2019, and August 31, 2019. Pharmacy fill information was obtained from the EHR via linkages with Surescripts pharmacy and pharmacy benefit manager data, as well as from insurance claims available at our institution. We matched antihypertensive medication fills observed in the linked EHR-pharmacy database with available fills in the insurance claims database and calculated the percentage of medication fills that were available in each database. We estimated medication adherence using proportion of days covered in the linked EHR-pharmacy database and in the insurance claims database. RESULTS: Of 26,679 patients with hypertension, 23,348 (87.5%) had at least 1 antihypertensive medication fill recorded in the linked EHR-pharmacy database. Of 1,501 patients matched with the insurance database and with a documented medication fill, a fill was present for 1,484 (98.9%) and 1,259 (83.9%) patients in the linked EHR-pharmacy and insurance databases, respectively. Of 12,109 medication fills recorded in the insurance data, we found an overlap of 11,060 (91.3%) fills with the linked EHR-pharmacy database. The linked EHR-pharmacy database also contained 18,232 of 19,281 (94.6%) medication fills present in either database. Measured medication adherence was higher for patients when based on linked EHR-pharmacy data compared with insurance claims data (42% vs 30%, P < 0.001). CONCLUSIONS: Linked EHR-pharmacy data captured medication fills for the vast majority of patients and resulted in higher estimates of adherence than insurance claims. Our results suggest that pharmacy fill data available in the EHR have sufficient reliability to be used for point-of-care assessment of medication adherence. DISCLOSURES: This study was supported by grant R01HL155149 from the National Heart, Lung, and Blood Institute. Allen Thorpe provided funding for the NYU Langone Health Learning Health System Program, which helped fund this project. The authors have nothing to disclose.
Electronic Health Records/standards , Information Storage and Retrieval/standards , Pharmacy , Practice Patterns, Physicians' , Databases, Factual , Medication Adherence , New York City , Retrospective Studies
BACKGROUND: The integration of behavioral economics (BE) principles and electronic health records (EHRs) using clinical decision support (CDS) tools is a novel approach to improving health outcomes. Meanwhile, the American Geriatrics Society has created the Choosing Wisely (CW) initiative to promote less aggressive glycemic targets and reduction in pharmacologic therapy in older adults with type 2 diabetes mellitus. To date, few studies have shown the effectiveness of combined BE and EHR approaches for managing chronic conditions, and none have addressed guideline-driven deprescribing specifically in type 2 diabetes. We previously conducted a pilot study aimed at promoting appropriate CW guideline adherence using BE nudges and EHRs embedded within CDS tools at 5 clinics within the New York University Langone Health (NYULH) system. The BE-EHR module intervention was tested for usability, adoption, and early effectiveness. Preliminary results suggested a modest improvement of 5.1% in CW compliance. OBJECTIVE: This paper presents the protocol for a study that will investigate the effectiveness of a BE-EHR module intervention that leverages BE nudges with EHR technology and CDS tools to reduce overtreatment of type 2 diabetes in adults aged 76 years and older, per the CW guideline. METHODS: A pragmatic, investigator-blind, cluster randomized controlled trial was designed to evaluate the BE-EHR module. A total of 66 NYULH clinics will be randomized 1:1 to receive for 18 months either (1) a 6-component BE-EHR module intervention + standard care within the NYULH EHR, or (2) standard care only. The intervention will be administered to clinicians during any patient encounter (eg, in person, telemedicine, medication refill, etc). The primary outcome will be patient-level CW compliance. Secondary outcomes will measure the frequency of intervention component firings within the NYULH EHR, and provider utilization and interaction with the BE-EHR module components. RESULTS: Study recruitment commenced on December 7, 2020, with the activation of all 6 BE-EHR components in the NYULH EHR. CONCLUSIONS: This study will test the effectiveness of a previously developed, iteratively refined, user-tested, and pilot-tested BE-EHR module aimed at providing appropriate diabetes care to elderly adults, compared to usual care via a cluster randomized controlled trial. This innovative research will be the first pragmatic randomized controlled trial to use BE principles embedded within the EHR and delivered using CDS tools to specifically promote CW guideline adherence in type 2 diabetes. The study will also collect valuable information on clinician workflow and interaction with the BE-EHR module, guiding future research in optimizing the timely delivery of BE nudges within CDS tools. This work will address the effectiveness of BE-inspired interventions in diabetes and chronic disease management. TRIAL REGISTRATION: ClinicalTrials.gov NCT04181307; https://clinicaltrials.gov/ct2/show/NCT04181307. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): DERR1-10.2196/28723.
BACKGROUND: Telemedicine in obstetrics has mostly been described in the rural areas that have limited access to subspecialties. During the COVID-19 pandemic, health systems rapidly expanded telemedicine services for urgent and nonurgent healthcare delivery, even in urban settings. The New York University health system implemented a prompt systemwide expansion of video-enabled telemedicine visits, increasing telemedicine to >8000 visits daily within 6 weeks of the beginning of the pandemic. There are limited studies that explore patient and provider satisfaction of telemedicine visits in obstetrical patients during the COVID-19 epidemic, particularly in the United States. OBJECTIVE: This study aimed to evaluate both the patients' and the providers' satisfaction with the administration of maternal-fetal medicine services through telemedicine and to identify the factors that drive the patients' desire for future obstetrical telemedicine services. STUDY DESIGN: A cross-sectional survey was administered to patients who completed a telemedicine video visit with the Division of Maternal-Fetal Medicine at the New York University Langone Hospital-Long Island from March 19, 2020, to May 26, 2020. A 10-question survey assessing the patients' digital experience and desire for future use was either administered by telephone or self-administered by the patients via a link after obtaining verbal consent. The survey responses were scored from 1-strongly disagree to 5-strongly agree. We analyzed the demographics and survey responses of the patients who agreed to vs those who answered neutral or disagree to the question "I would like telehealth to be an option for future obstetric visits." The providers also answered a similar 10-question survey. The median scores were compared using appropriate tests. A P value of <.05 was considered significant. RESULTS: A total of 253 patients participated in 433 telemedicine visits, and 165 patients completed the survey, resulting in a 65% survey response rate. Overall, there were high rates of patient satisfaction in all areas assessed. Those who desired future telemedicine had significantly greater agreeability that they were able to see and hear their provider easily (5 [4.5, 5] vs 5 [4, 5]; P=.014) and that the lack of physical activity was not an issue (5 [4, 5] vs 5 [4, 5]; P=.032). They were also more likely to agree that the telemedicine visits were as good as in-person visits (4 [3, 5] vs 3 [2, 3]; P<.001) and that telehealth made it easier for them to see doctors or specialists (5 [4, 5] vs 3 [2, 3]; P<.001). The patients seeking consults for poor obstetrical history were more likely to desire future telemedicine compared with other visit types (19 (90%) vs 2 (10%); P=.05). Provider survey responses also demonstrated high levels of satisfaction, with 83% agreeing that they would like telemedicine to be an option for future obstetrical visits. CONCLUSION: We demonstrated that maternal-fetal medicine obstetrical patients and providers were highly satisfied with the implementation of telemedicine during the initial wave of the COVID-19 pandemic and a majority of them desire telemedicine as an option for future visits. A patient's desire for future telemedicine visits was significantly affected by their digital experience, the perception of a lack of need for physical contact, perceived time saved on travel, and access to healthcare providers. Health systems need to continue to improve healthcare delivery and invest in innovative solutions to conduct physical examinations remotely.
COVID-19 , Telemedicine , Cross-Sectional Studies , Female , Humans , Pandemics , Perinatology , Pregnancy , SARS-CoV-2 , United States
BACKGROUND: Advances in genetics and sequencing technologies are enabling the identification of more individuals with inherited cancer susceptibility who could benefit from tailored screening and prevention recommendations. While cancer family history information is used in primary care settings to identify unaffected patients who could benefit from a cancer genetics evaluation, this information is underutilized. System-level population health management strategies are needed to assist health care systems in identifying patients who may benefit from genetic services. In addition, because of the limited number of trained genetics specialists and increasing patient volume, the development of innovative and sustainable approaches to delivering cancer genetic services is essential. METHODS: We are conducting a randomized controlled trial, entitled Broadening the Reach, Impact, and Delivery of Genetic Services (BRIDGE), to address these needs. The trial is comparing uptake of genetic counseling, uptake of genetic testing, and patient adherence to management recommendations for automated, patient-directed versus enhanced standard of care cancer genetics services delivery models. An algorithm-based system that utilizes structured cancer family history data available in the electronic health record (EHR) is used to identify unaffected patients who receive primary care at the study sites and meet current guidelines for cancer genetic testing. We are enrolling eligible patients at two healthcare systems (University of Utah Health and New York University Langone Health) through outreach to a randomly selected sample of 2780 eligible patients in the two sites, with 1:1 randomization to the genetic services delivery arms within sites. Study outcomes are assessed through genetics clinic records, EHR, and two follow-up questionnaires at 4 weeks and 12 months after last genetic counseling contactpre-test genetic counseling. DISCUSSION: BRIDGE is being conducted in two healthcare systems with different clinical structures and patient populations. Innovative aspects of the trial include a randomized comparison of a chatbot-based genetic services delivery model to standard of care, as well as identification of at-risk individuals through a sustainable EHR-based system. The findings from the BRIDGE trial will advance the state of the science in identification of unaffected patients with inherited cancer susceptibility and delivery of genetic services to those patients. TRIAL REGISTRATION: BRIDGE is registered as NCT03985852 . The trial was registered on June 6, 2019 at clinicaltrials.gov .
Genetic Counseling , Neoplasms , Child , Female , Genetic Testing , Humans , Infant, Newborn , Neoplasms/genetics , Neoplasms/therapy , New York , Pregnancy , Primary Health Care
BACKGROUND: The primary prevention of cardiovascular (CV) events is often less intense in persons at higher CV risk and vice versa. Clinical practice guidelines recommend that clinicians and patients use shared decision making (SDM) to arrive at an effective and feasible prevention plan that is congruent with each person's CV risk and informed preferences. However, SDM does not routinely happen in practice. This study aims to integrate into routine care an SDM decision tool (CV PREVENTION CHOICE) at three diverse healthcare systems in the USA and study strategies that foster its adoption and routine use. METHODS: This is a mixed method, hybrid type III stepped wedge cluster randomized study to estimate (a) the effectiveness of implementation strategies on SDM uptake and utilization and (b) the extent to which SDM results in prevention plans that are risk-congruent. Formative evaluation methods, including clinician and stakeholder interviews and surveys, will identify factors likely to impact feasibility, acceptability, and adoption of CV PREVENTION CHOICE as well as normalization of CV PREVENTION CHOICE in routine care. Implementation facilitation will be used to tailor implementation strategies to local needs, and implementation strategies will be systematically adjusted and tracked for assessment and refinement. Electronic health record data will be used to assess implementation and effectiveness outcomes, including CV PREVENTION CHOICE reach, adoption, implementation, maintenance, and effectiveness (measured as risk-concordant care plans). A sample of video-recorded clinical encounters and patient surveys will be used to assess fidelity. The study employs three theoretical approaches: a determinant framework that calls attention to categories of factors that may foster or inhibit implementation outcomes (the Consolidated Framework for Implementation Research), an implementation theory that guides explanation or understanding of causal influences on implementation outcomes (Normalization Process Theory), and an evaluation framework (RE-AIM). DISCUSSION: By the project's end, we expect to have (a) identified the most effective implementation strategies to embed SDM in routine practice and (b) estimated the effectiveness of SDM to achieve feasible and risk-concordant CV prevention in primary care. TRIAL REGISTRATION: ClinicalTrials.gov, NCT04450914 . Posted June 30, 2020 TRIAL STATUS: This study received ethics approval on April 17, 2020. The current trial protocol is version 2 (approved February 17, 2021). The first subject had not yet been enrolled at the time of submission.
BACKGROUND: Clinical decision support (CDS) is a valuable feature of electronic health records (EHRs) designed to improve quality and safety. However, due to the complexities of system design and inconsistent results, CDS tools may inadvertently increase alert fatigue and contribute to physician burnout. A/B testing, or rapid-cycle randomized tests, is a useful method that can be applied to the EHR in order to rapidly understand and iteratively improve design choices embedded within CDS tools. OBJECTIVE: This paper describes how rapid randomized controlled trials (RCTs) embedded within EHRs can be used to quickly ascertain the superiority of potential CDS design changes to improve their usability, reduce alert fatigue, and promote quality of care. METHODS: A multistep process combining tools from user-centered design, A/B testing, and implementation science was used to understand, ideate, prototype, test, analyze, and improve each candidate CDS. CDS engagement metrics (alert views, acceptance rates) were used to evaluate which CDS version is superior. RESULTS: To demonstrate the impact of the process, 2 experiments are highlighted. First, after multiple rounds of usability testing, a revised CDS influenza alert was tested against usual care CDS in a rapid (~6 weeks) RCT. The new alert text resulted in minimal impact on reducing firings per patients per day, but this failure triggered another round of review that identified key technical improvements (ie, removal of dismissal button and firings in procedural areas) that led to a dramatic decrease in firings per patient per day (23.1 to 7.3). In the second experiment, the process was used to test 3 versions (financial, quality, regulatory) of text supporting tobacco cessation alerts as well as 3 supporting images. Based on 3 rounds of RCTs, there was no significant difference in acceptance rates based on the framing of the messages or addition of images. CONCLUSIONS: These experiments support the potential for this new process to rapidly develop, deploy, and rigorously evaluate CDS within an EHR. We also identified important considerations in applying these methods. This approach may be an important tool for improving the impact of and experience with CDS. TRIAL REGISTRATION: Flu alert trial: ClinicalTrials.gov NCT03415425; https://clinicaltrials.gov/ct2/show/NCT03415425. Tobacco alert trial: ClinicalTrials.gov NCT03714191; https://clinicaltrials.gov/ct2/show/NCT03714191.
Decision Support Systems, Clinical , Electronic Health Records , Humans , Randomized Controlled Trials as Topic , Software
