I2b2-etl: Python application for importing electronic health data into the informatics for integrating biology and the bedside platform.

MOTIVATION: The i2b2 platform is used at major academic health institutions and research consortia for querying for electronic health data. However, a major obstacle for wider utilization of the platform is the complexity of data loading that entails a steep curve of learning the platform's complex data schemas. To address this problem, we have developed the i2b2-etl package that simplifies the data loading process, which will facilitate wider deployment and utilization of the platform. RESULTS: We have implemented i2b2-etl as a Python application that imports ontology and patient data using simplified input file schemas and provides inbuilt record number de-identification and data validation. We describe a real-world deployment of i2b2-etl for a population-management initiative at MassGeneral Brigham. AVAILABILITY AND IMPLEMENTATION: i2b2-etl is a free, open-source application implemented in Python available under the Mozilla 2 license. The application can be downloaded as compiled docker images. A live demo is available at https://i2b2clinical.org/demo-i2b2etl/ (username: demo, password: Etl@2021). SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Vasopressin Administration Is Associated With Rising Serum Lactate Levels in Patients With Sepsis.

BACKGROUND: Vasopressin is used in conjunction with norepinephrine during treatment of patients with septic shock. Serum lactate is often used in monitoring of patients with sepsis; however, its importance as a therapeutic target is unclear. The objective of this study is to examine the relationship of vasopressin use on serum lactate levels in patients with sepsis. METHODS: This study uses electronic heath records available via the Medical Information Mart for Intensive Care III. Patients were required to have a serum lactate monitoring during the intensive care unit (ICU) stay. The treatment was the administration of vasopressin between hours 3 and 18 of the ICU stay. Analysis was performed using a matched design. RESULTS: Patients receiving vasopressin were more likely to have their serum lactate levels rise when compared to matched patients who did not receive vasopressin (odds ratio: 6.6; 95% confidence interval: 3.0-14.6, P < .001). Patients who received vasopressin had a median increase in serum lactate of 0.3 mmol/L, while patients who did not receive vasopressin had a median decrease in serum lactate of 0.7 mmol/L (P < .001). There was no statistically significant difference between the control and treated groups' lactate trajectories prior to possible administration of vasopressin (P = .15). The results did not change significantly when norepinephrine initiation was used as the index time. CONCLUSIONS: In patients with sepsis, the administration of vasopressin was associated with a statistically significant difference in lactate change over the course of 24 hours when compared to matched patients who did not receive vasopressin.

The 21st Century Cures Act: A Competitive Apps Market and the Risk of Innovation Blocking.

The 21st Century Cures Act and the recently published "final rule" define standardized methods for obtaining electronic copies of electronic health record (EHR) data through application programming interfaces. The rule is meant to create an ecosystem of reusable, substitutable apps that can be built once but run at any hospital system "without special effort." Yet, despite numerous provisions around information blocking in the final rule, there is concern that the business practices that govern EHR vendors and health care organizations in the United States could still stifle innovation. We describe potential app ecosystems that may form. We caution that misaligned incentives may result in anticompetitive behavior and purposefully limited functionality. Closed proprietary ecosystems may result, limiting the value derived from interoperability. The 21st Century Cures Act and final rule are an exciting step in the direction of improved interoperability. However, realizing the vision of a truly interoperable app ecosystem is not predetermined.

Comparing Characteristics of Patients Who Connect Their iPhones to an Electronic Health Records System Versus Patients Who Connect Without Personal Devices: Cohort Study.

BACKGROUND: While individual access to health records has traditionally been through paper and other physical media, there has been a recent push toward digitizing this process. Direct patient access to health data through application programming interfaces (APIs) is an important part of current United States policy initiatives, and Apple has created the product "Health Records on iPhone" to leverage APIs for this purpose. OBJECTIVE: The objective of this study was to examine the characteristics of patients at our institution who connected their personal iPhone devices to our electronic health records (EHRs) system through "Health Records on iPhone", as compared to patients at our institution who used our patient portal but did not connect a personal device to our system. METHODS: We examined adult patients at our institution who had authorized an iPhone device to download their health data from the Partners HealthCare EHR via APIs through "Health Records on iPhone" from February 18, 2018 (the date this feature was enabled at our health system) until February 17, 2019. We compared these patients to adult patients who used our portal at least once during this period but did not authorize an iPhone device to download their data via APIs. RESULTS: Variables associated with an increased likelihood of using "Health Records on iPhone" included male gender (adjusted OR 3.36; 95% CI 3.11-3.62; P<.001) and younger age, particularly below 50 years of age. With each decade of age over 50, people were less likely to be "Health Records on iPhone" product users. Asian patients were more likely to use the product than Caucasian patients (adjusted OR 1.32; 95% CI 1.16-1.51; P<.001), though there was no significant difference between African Americans and Caucasians (adjusted OR 1.15; 95% CI 0.94-1.41; P=.17). Patients who resided in higher ZIP code income quartiles were more likely to be users than those in the lowest quartile. CONCLUSIONS: Early results from the implementation of patient-facing APIs at a single institution suggest that there are opportunities for expanding these technologies to ensure all patients are aware of, and have access to, their health data on their personal devices. More work is needed on expanding these technologies to different patient populations.

Evidence from ClinicalTrials.gov on the growth of Digital Health Technologies in neurology trials.

Digital Health Technologies (DHTs) such as connected sensors offer particular promise for improving data collection and patient empowerment in neurology research and care. This study analyzed the recent evolution of the use of DHTs in trials registered on ClinicalTrials.gov for four chronic neurological disorders: epilepsy, multiple sclerosis, Alzheimer's, and Parkinson's disease. We document growth in the collection of both more established digital measures (e.g., motor function) and more novel digital measures (e.g., speech) over recent years, highlighting contexts of use and key trends.

Remote Cardiovascular Hypertension Program Enhanced Blood Pressure Control During the COVID-19 Pandemic.

Background The COVID-19 pandemic disrupted traditional health care; one fallout was a drastic decrease in blood pressure (BP) assessment. We analyzed the pandemic's impact on our existing remote hypertension management program's effectiveness and adaptability. Methods and Results This retrospective observational analysis evaluated BP control in an entirely remote management program before and during the pandemic. A team of pharmacists, nurse practitioners, physicians, and nonlicensed navigators used an evidence-based clinical algorithm to optimize hypertensive treatment. The algorithm was adapted during the pandemic to simplify BP control. Overall, 1256 patients (605 enrolled in the 6 months before the pandemic shutdown in March 2020 and 651 in the 6 months after) were a median age of 63 years old, 57% female, and 38.2% non-White. Among enrolled patients with sustained hypertension, 51.1% reached BP goals. Within this group, rates of achieving goal BP improved to 94.6% during the pandemic from 75.8% prepandemic (P<0.0001). Mean baseline home BP was 141.7/81.9 mm Hg during the pandemic and 139.8/82.2 prepandemic, and fell ≈16/9 mm Hg in both periods (P<0.0001). Maintenance during the pandemic was achieved earlier (median 11.8 versus 19.6 weeks, P<0.0001), with more frequent monthly calls (8.2 versus 3.1, P<0.0001) and more monthly home BP recordings per patient (32.4 versus 18.9, P<0.0001), compared with the prepandemic period. Conclusions A remote clinical management program was successfully adapted and delivered significant improvements in BP control and increased home BP monitoring despite a nationally observed disruption of traditional hypertension care. Such programs have the potential to transform hypertension management and care delivery.

An efficient landmark model for prediction of suicide attempts in multiple clinical settings.

Growing evidence has shown that applying machine learning models to large clinical data sources may exceed clinician performance in suicide risk stratification. However, many existing prediction models either suffer from "temporal bias" (a bias that stems from using case-control sampling) or require training on all available patient visit data. Here, we adopt a "landmark model" framework that aligns with clinical practice for prediction of suicide-related behaviors (SRBs) using a large electronic health record database. Using the landmark approach, we developed models for SRB prediction (regularized Cox regression and random survival forest) that establish a time-point (e.g., clinical visit) from which predictions are made over user-specified prediction windows using historical information up to that point. We applied this approach to cohorts from three clinical settings: general outpatient, psychiatric emergency department, and psychiatric inpatients, for varying prediction windows and lengths of historical data. Models achieved high discriminative performance (area under the Receiver Operating Characteristic curve 0.74-0.93 for the Cox model) across different prediction windows and settings, even with relatively short periods of historical data. In short, we developed accurate, dynamic SRB risk prediction models with the landmark approach that reduce bias and enhance the reliability and portability of suicide risk prediction models.

Results of a Remotely Delivered Hypertension and Lipid Program in More Than 10 000 Patients Across a Diverse Health Care Network.

Importance: Blood pressure (BP) and cholesterol control remain challenging. Remote care can deliver more effective care outside of traditional clinician-patient settings but scaling and ensuring access to care among diverse populations remains elusive. Objective: To implement and evaluate a remote hypertension and cholesterol management program across a diverse health care network. Design, Setting, and Participants: Between January 2018 and July 2021, 20 454 patients in a large integrated health network were screened; 18 444 were approached, and 10 803 were enrolled in a comprehensive remote hypertension and cholesterol program (3658 patients with hypertension, 8103 patients with cholesterol, and 958 patients with both). A total of 1266 patients requested education only without medication titration. Enrolled patients received education, home BP device integration, and medication titration. Nonlicensed navigators and pharmacists, supported by cardiovascular clinicians, coordinated care using standardized algorithms, task management and automation software, and omnichannel communication. BP and laboratory test results were actively monitored. Main Outcomes and Measures: Changes in BP and low-density lipoprotein cholesterol (LDL-C). Results: The mean (SD) age among 10 803 patients was 65 (11.4) years; 6009 participants (56%) were female; 1321 (12%) identified as Black, 1190 (11%) as Hispanic, 7758 (72%) as White, and 1727 (16%) as another or multiple races (including American Indian or Alaska Native, Asian, Native Hawaiian or Other Pacific Islander, unknown, other, and declined to respond; consolidated owing to small numbers); and 142 (11%) reported a preferred language other than English. A total of 424 482 BP readings and 139 263 laboratory reports were collected. In the hypertension program, the mean (SD) office BP prior to enrollment was 150/83 (18/10) mm Hg, and the mean (SD) home BP was 145/83 (20/12) mm Hg. For those engaged in remote medication management, the mean (SD) clinic BP 6 and 12 months after enrollment decreased by 8.7/3.8 (21.4/12.4) and 9.7/5.2 (22.2/12.6) mm Hg, respectively. In the education-only cohort, BP changed by a mean (SD) -1.5/-0.7 (23.0/11.1) and by +0.2/-1.9 (30.3/11.2) mm Hg, respectively (P < .001 for between cohort difference). In the lipids program, patients in remote medication management experienced a reduction in LDL-C by a mean (SD) 35.4 (43.1) and 37.5 (43.9) mg/dL at 6 and 12 months, respectively, while the education-only cohort experienced a mean (SD) reduction in LDL-C of 9.3 (34.3) and 10.2 (35.5) mg/dL at 6 and 12 months, respectively (P < .001). Similar rates of enrollment and reductions in BP and lipids were observed across different racial, ethnic, and primary language groups. Conclusions and Relevance: The results of this study indicate that a standardized remote BP and cholesterol management program may help optimize guideline-directed therapy at scale, reduce cardiovascular risk, and minimize the need for in-person visits among diverse populations.

Why APIs? Anticipated value, barriers, and opportunities for standards-based application programming interfaces in healthcare: perspectives of US thought leaders.

Objective: Improving health data interoperability through application programming interfaces (APIs) is a focus of US policy initiatives and could have tremendous impact on many aspects of care delivery, such as innovation, operational efficiency, and patient-centered care. To better understand the landscape of API use cases, we interviewed US thought leaders involved in developing and implementing standard-based APIs. Materials and Methods: We conducted semi-structured virtual interviews with US subject matter experts (SMEs) on APIs. SMEs were asked to describe API use cases along with value and barriers for each use case. Written summaries were checked by the SME and analyzed by the study team to identify findings and themes. Results: We interviewed 12 SMEs representing diverse sectors of the US healthcare system, including academia, industry, public health agencies, electronic health record vendors, government, and standards organizations. Use cases for standards-based APIs fell into six categories: patient-facing, clinician-facing, population health and value-based care, public health, administrative, and social services. The value across use cases was viewed as unrealized to date, and barriers to the use of APIs varied by use case. Conclusions: SMEs identified a diverse set of API use cases where standard-based APIs had the potential to generate value. As policy efforts seek to increase API adoption, our work provides an early look at the landscape of API use cases, value propositions, and barriers. Additional effort is needed to better understand the barriers and how to overcome them to create value, such as through demonstration projects and rigorous evaluations for specific use cases.

Deploying digital health tools within large, complex health systems: key considerations for adoption and implementation.

In recent years, the number of digital health tools with the potential to significantly improve delivery of healthcare services has grown tremendously. However, the use of these tools in large, complex health systems remains comparatively limited. The adoption and implementation of digital health tools at an enterprise level is a challenge; few strategies exist to help tools cross the chasm from clinical validation to integration within the workflows of a large health system. Many previously proposed frameworks for digital health implementation are difficult to operationalize in these dynamic organizations. In this piece, we put forth nine dimensions along which clinically validated digital health tools should be examined by health systems prior to adoption, and propose strategies for selecting digital health tools and planning for implementation in this setting. By evaluating prospective tools along these dimensions, health systems can evaluate which existing digital health solutions are worthy of adoption, ensure they have sufficient resources for deployment and long-term use, and devise a strategic plan for implementation.

Use of automatic SQL generation interface to enhance transparency and validity of health-data analysis.

Analysis of health data typically requires development of queries using structured query language (SQL) by a data-analyst. As the SQL queries are manually created, they are prone to errors. In addition, accurate implementation of the queries depends on effective communication with clinical experts, that further makes the analysis error prone. As a potential resolution, we explore an alternative approach wherein a graphical interface that automatically generates the SQL queries is used to perform the analysis. The latter allows clinical experts to directly perform complex queries on the data, despite their unfamiliarity with SQL syntax. The interface provides an intuitive understanding of the query logic which makes the analysis transparent and comprehensible to the clinical study-staff, thereby enhancing the transparency and validity of the analysis. This study demonstrates the feasibility of using a user-friendly interface that automatically generate SQL for analysis of health data. It outlines challenges that will be useful for designing user-friendly tools to improve transparency and reproducibility of data analysis.

Use of connected digital products in clinical research following the COVID-19 pandemic: a comprehensive analysis of clinical trials.

OBJECTIVES: In an effort to mitigate COVID-19 related challenges for clinical research, the US Food and Drug Administration (FDA) issued new guidance for the conduct of 'virtual' clinical trials in late March 2020. This study documents trends in the use of connected digital products (CDPs), tools that enable remote patient monitoring and telehealth consultation, in clinical trials both before and after the onset of the pandemic. DESIGN: We applied a comprehensive text search algorithm to clinical trial registry data to identify trials that use CDPs for remote monitoring or telehealth. We compared CDP use in the months before and after the issuance of FDA guidance facilitating virtual clinical trials. SETTING: All trials registered on ClinicalTrials.gov with start dates from May 2019 through February 2021. OUTCOME MEASURES: The primary outcome measure was the overall percentage of CDP use in clinical trials started in the 10 months prior to the pandemic onset (May 2019-February 2020) compared with the 10 months following (May 2020-February 2021). Secondary outcome measures included CDP usage by trial type (interventional, observational), funder type (industry, non-industry) and diagnoses (COVID-19 or non-COVID-19 participants). RESULTS: CDP usage in clinical trials increased by only 1.65 percentage points, from 14.19% (n=23 473) of all trials initiated in the 10 months prior to the pandemic onset to 15.84% (n=26 009) of those started in the 10 months following (p<0.01). The increase occurred primarily in observational studies and non-industry funded trials and was driven entirely by CDP usage in trials for COVID-19. CONCLUSIONS: These findings suggest that in the short-term, new options created by regulatory guidance to stimulate telehealth and remote monitoring were not widely incorporated into clinical research. In the months immediately following the pandemic onset, CDP adoption increased primarily in observational and non-industry funded studies where virtual protocols are likely medically necessary due to the participants' COVID-19 diagnosis.

Patient-led data sharing for clinical bioinformatics research: USCDI and beyond.

The 21st Century Cures Act, passed in 2016, and the Final Rules it called for create a roadmap for enabling patient access to their electronic health information. The set of data to be made available, as determined by the Office of the National Coordinator for Health IT through the US Core Data for Interoperability expansion process, will impact the value creation of this improved data liquidity. In this commentary, we look at the potential for significant value creation from USCDI in the context of clinical bioinformatics research and advocate for the research community's involvement in the USCDI process to propel this value creation forward. We also describe 1 mechanism-using existing required APIs for full data export capabilities-that could pragmatically enable this value creation at minimal additional technical lift beyond the current regulatory requirements.

Development of a Perioperative Medication-Related Clinical Decision Support Tool to Prevent Medication Errors: An Analysis of User Feedback.

OBJECTIVES: Medication use in the perioperative setting presents many patient safety challenges that may be improved with electronic clinical decision support (CDS). The objective of this paper is to describe the development and analysis of user feedback for a robust, real-time medication-related CDS application designed to provide patient-specific dosing information and alerts to warn of medication errors in the operating room (OR). METHODS: We designed a novel perioperative medication-related CDS application in four phases: (1) identification of need, (2) alert algorithm development, (3) system design, and (4) user interface design. We conducted group and individual design feedback sessions with front-line clinician leaders and subject matter experts to gather feedback about user requirements for alert content and system usability. Participants were clinicians who provide anesthesia (attending anesthesiologists, nurse anesthetists, and house staff), OR pharmacists, and nurses. RESULTS: We performed two group and eight individual design feedback sessions, with a total of 35 participants. We identified 20 feedback themes, corresponding to 19 system changes. Key requirements for user acceptance were: Use hard stops only when necessary; provide as much information as feasible about the rationale behind alerts and patient/clinical context; and allow users to edit fields such as units, time, and baseline values (e.g., baseline blood pressure). CONCLUSION: We incorporated user-centered design principles to build a perioperative medication-related CDS application that uses real-time patient data to provide patient-specific dosing information and alerts. Emphasis on early user involvement to elicit user requirements, workflow considerations, and preferences during application development can result in time and money efficiencies and a safer and more usable system.

Workflow Automation for a Virtual Hypertension Management Program.

OBJECTIVES: Hypertension is a modifiable risk factor for numerous comorbidities and treating hypertension can greatly improve health outcomes. We sought to increase the efficiency of a virtual hypertension management program through workflow automation processes. METHODS: We developed a customer relationship management (CRM) solution at our institution for the purpose of improving processes and workflow for a virtual hypertension management program and describe here the development, implementation, and initial experience of this CRM system. RESULTS: Notable system features include task automation, patient data capture, multi-channel communication, integration with our electronic health record (EHR), and device integration (for blood pressure cuffs). In the five stages of our program (intake and eligibility screening, enrollment, device configuration/setup, medication titration, and maintenance), we describe some of the key process improvements and workflow automations that are enabled using our CRM platform, like automatic reminders to capture blood pressure data and present these data to our clinical team when ready for clinical decision making. We also describe key limitations of CRM, like balancing out-of-the-box functionality with development flexibility. Among our first group of referred patients, 76% (39/51) preferred email as their communication method, 26/51 (51%) were able to enroll electronically, and 63% of those enrolled (32/51) were able to transmit blood pressure data without phone support. CONCLUSION: A CRM platform could improve clinical processes through multiple pathways, including workflow automation, multi-channel communication, and device integration. Future work will examine the operational improvements of this health information technology solution as well as assess clinical outcomes.

Coronary risk assessment by point-based vs. equation-based Framingham models: significant implications for clinical care.

BACKGROUND: US cholesterol guidelines use original and simplified versions of the Framingham model to estimate future coronary risk and thereby classify patients into risk groups with different treatment strategies. We sought to compare risk estimates and risk group classification generated by the original, complex Framingham model and the simplified, point-based version. METHODS: We assessed 2,543 subjects age 20-79 from the 2001-2006 National Health and Nutrition Examination Surveys (NHANES) for whom Adult Treatment Panel III (ATP-III) guidelines recommend formal risk stratification. For each subject, we calculated the 10-year risk of major coronary events using the original and point-based Framingham models, and then compared differences in these risk estimates and whether these differences would place subjects into different ATP-III risk groups (<10% risk, 10-20% risk, or >20% risk). Using standard procedures, all analyses were adjusted for survey weights, clustering, and stratification to make our results nationally representative. RESULTS: Among 39 million eligible adults, the original Framingham model categorized 71% of subjects as having "moderate" risk (<10% risk of a major coronary event in the next 10 years), 22% as having "moderately high" (10-20%) risk, and 7% as having "high" (>20%) risk. Estimates of coronary risk by the original and point-based models often differed substantially. The point-based system classified 15% of adults (5.7 million) into different risk groups than the original model, with 10% (3.9 million) misclassified into higher risk groups and 5% (1.8 million) into lower risk groups, for a net impact of classifying 2.1 million adults into higher risk groups. These risk group misclassifications would impact guideline-recommended drug treatment strategies for 25-46% of affected subjects. Patterns of misclassifications varied significantly by gender, age, and underlying CHD risk. CONCLUSIONS: Compared to the original Framingham model, the point-based version misclassifies millions of Americans into risk groups for which guidelines recommend different treatment strategies.

Beyond validation: getting health apps into clinical practice.

Fueled by advances in technology, increased access to smartphones, and capital investment, the number of available health "apps" has exploded in recent years. Patients use their smartphones for many things, but not as much as they might for health, especially for managing their chronic conditions. Moreover, while significant work is ongoing to develop, validate, and evaluate these apps, it is less clear how to effectively disseminate apps into routine clinical practice. We propose a framework for prescribing apps and outline the key issues that need to be addressed to enable app dissemination in clinical care. This includes: education and awareness, creating digital formularies, workflow and EHR integration, payment models, and patient/provider support. As work in digital health continues to expand, integrating health apps into clinical care delivery will be critical if digital health is to achieve its potential.

Remote Patient Monitoring Program for Hospital Discharged COVID-19 Patients.

OBJECTIVE: We deployed a Remote Patient Monitoring (RPM) program to monitor patients with coronavirus disease 2019 (COVID-19) upon hospital discharge. We describe the patient characteristics, program characteristics, and clinical outcomes of patients in our RPM program. METHODS: We enrolled COVID-19 patients being discharged home from the hospital. Enrolled patients had an app, and were provided with a pulse oximeter and thermometer. Patients self-reported symptoms, O2 saturation, and temperature daily. Abnormal symptoms or vital signs were flagged and assessed by a pool of nurses. Descriptive statistics were used to describe patient and program characteristics. A mixed-effects logistic regression model was used to determine the odds of a combined endpoint of emergency department (ED) or hospital readmission. RESULTS: A total of 295 patients were referred for RPM from five participating hospitals, and 225 patients were enrolled. A majority of enrolled patients (66%) completed the monitoring period without triggering an abnormal alert. Enrollment was associated with a decreased odds of ED or hospital readmission (adjusted odds ratio: 0.54; 95% confidence interval: 0.3-0.97; p = 0.039). Referral without enrollment was not associated with a reduced odds of ED or hospital readmission. CONCLUSION: RPM for COVID-19 provides a mechanism to monitor patients in their home environment and reduce hospital utilization. Our work suggests that RPM reduces readmissions for patients with COVID-19 and provides scalable remote monitoring capabilities upon hospital discharge. RPM for postdischarge patients with COVID-19 was associated with a decreased risk of readmission to the ED or hospital, and provided a scalable mechanism to monitor patients in their home environment.