Patients Are Knowledge Workers in the Clinical Information Space

Background  Limited research exists on patient knowledge/cognition or “getting inside patients'' heads.” Because patients possess unique and privileged knowledge, clinicians need this information to make patient-centered and coordinated treatment planning decisions. To achieve patient-centered care, we characterize patient knowledge and contributions to the clinical information space. Methods and Objectives  In a theoretical overview, we explore the relevance of patient knowledge to care provision, apply historical perspectives of knowledge acquisition to patient knowledge, propose a representation of patient knowledge types across the continuum of care, and include illustrative vignettes about Mr. Jones. We highlight how the field of human factors (a core competency of health informatics) provides a perspective and methods for eliciting and characterizing patient knowledge. Conclusion  Patients play a vital role in the clinical information space by possessing and sharing unique knowledge relevant to the clinical picture. Without a patient''s contributions, the clinical picture of the patient is incomplete. A human factors perspective informs patient-centered care and health information technology solutions to support clinical information sharing.     

Privacy versus Convenience: A Historical Perspective,Analysis of Risks,and an Informatics Call to Action

Background  The pace of technological change dwarfs the pace of social and policy change. This mismatch allows for individual harm from lack of recognition of changes in societal context. The value of privacy has not kept pace with changes in technology over time; individuals seem to discount how loss of privacy can lead to directed personal harm. Objective  The authors examined individuals sharing personal data with mobile health applications (mHealth apps) and compared the current digital context to the historical context of harm. The authors make recommendations to informatics professionals to support consumers who wish to use mHealth apps in a manner that balances convenience with personal privacy to reduce the risk of harm. Methods  A literature search focused by a historical perspective of risk of harm was performed throughout the development of this paper. Two case studies highlight questions a consumer might ask to assess the risk of harm posed by mobile health applications. Results  A historical review provides the context for the collective human experience of harm. We then encapsulate current perceptions and views of privacy and list potential risks created by insufficient attention to privacy management. Discussion  The results provide a historical context for individuals to view the risk of harm and shed light on potential emotional, reputational, economic, and physical harms that can result from naïve use of mHealth apps. We formulate implications for clinical informaticists. Conclusion  Concepts of both harm and privacy have changed substantially over the past 20 years. Technology provides methods to invade privacy and cause harm unimaginable a few decades ago. Only recently have the consequences become clearer. The current regulatory framework is extremely limited. Given the risks of harm and limited awareness, we call upon informatics professionals to support more privacy education and protections and increase mHealth transparency about data usage.     

What Industry Wants: An Empirical Analysis of Health Informatics Job Postings

Objectives  To describe the education, experience, skills, and knowledge required for health informatics jobs in the United States. Methods  Health informatics job postings ( n  = 206) from Indeed.com on April 14, 2020 were analyzed in an empirical analysis, with the abstraction of attributes relating to requirements for average years and types of experience, minimum and desired education, licensure, certification, and informatics skills. Results  A large percentage (76.2%) of posts were for clinical informaticians, with 62.1% of posts requiring a minimum of a bachelor''s education. Registered nurse (RN) licensure was required for 40.8% of posts, and only 7.3% required formal education in health informatics. The average experience overall was 1.6 years (standard deviation = 2.2), with bachelor''s and master''s education levels increasing mean experience to 3.5 and 5.8 years, respectively. Electronic health record support, training, and other clinical systems were the most sought-after skills. Conclusion  This cross-sectional study revealed the importance of a clinical background as an entree into health informatics positions, with RN licensure and clinical experience as common requirements. The finding that informatics-specific graduate education was rarely required may indicate that there is a lack of alignment between academia and industry, with practical experience preferred over specific curricular components. Clarity and shared understanding of terms across academia and industry are needed for defining and advancing the preparation for and practice of health informatics.     

Moving Faster than the COVID-19 Pandemic: The Rapid,Digital Transformation of a Public Health System

Background  Queensland, Australia has been successful in containing the COVID-19 pandemic. Underpinning that response has been a highly effective virus containment strategy which relies on identification, isolation, and contact tracing of cases. The dramatic emergence of the COVID-19 pandemic rendered traditional paper-based systems for managing contact tracing no longer fit for purpose. A rapid digital transformation of the public health contact tracing system occurred to support this effort. Objectives  The objectives of the digital transformation were to shift legacy systems (paper or standalone electronic systems) to a digitally enabled public health system, where data are centered around the consumer rather than isolated databases. The objective of this paper is to outline this case study and detail the lessons learnt to inform and give confidence to others contemplating digitization of public health systems in response to the COVID-19 pandemic. Methods  This case study is set in Queensland, Australia. Universal health care is available. A multidisciplinary team was established consisting of clinical informaticians, developers, data strategists, and health information managers. An agile “pair-programming” approach was undertaken to application development and extensive change efforts were made to maximize adoption of the new digital workflows. Data governance and flows were changed to support rapid management of the pandemic. Results  The digital coronavirus application (DCOVA) is a web-based application that securely captures information about people required to quarantine and creates a multiagency secure database to support a successful containment strategy. Conclusion  Most of the literature surrounding digital transformation allows time for significant consultation, which was simply not possible under crisis conditions. Our observation is that staff was willing to adopt new digital systems because the reason for change (the COVID-19 pandemic) was clearly pressing. This case study highlights just how critical a unified purpose, is to successful, rapid digital transformation.     

Sexual Orientation Demographic Data in a Clinical Cohort of Transgender Patients

Background  There are specific issues regarding sexual orientation (SO) collection and analysis among transgender and nonbinary patients. A limitation to meaningful SO and gender identity (GI) data collection is their consideration as a fixed trait or demographic data point. Methods  A de-identified patient database from a single electronic health record (EHR) that allows for searching any discrete data point in the EHR was used to query demographic data (sex assigned at birth and current GI) for transgender individuals from January 2011 to March 2020 at a large urban tertiary care academic health center. Results  A cohort of transgender individuals were identified by using EHR data from a two-step demographic question. Almost half of male identified (46.70%, n  = 85) and female identified (47.51%, n  = 86) individuals had “heterosexual/straight” input for SO. Overall, male and female identified (i.e., binary) GI aggregate categories had similar SO responses. Assigned male at birth (AMAB) nonbinary individuals ( n  = 6) had “homosexual/gay” SO data input. Assigned female at birth (AFAB) nonbinary individuals ( n  = 56) had almost half “something else” SO data input (41.67%, n  = 15). Individuals with “choose not to disclose” for GI ( n  = 249) almost all had “choose not to disclose” SO data (96.27%, n  = 232). Conclusion  Current SO categories do not fully capture transgender individuals'' identities and experiences, and limit the clinical and epidemiological utility of collecting this data in the current form. Anatomical assumptions based on SO should be seen as a potential shortcoming in over-reliance on SO as an indicator of screening needs and risk factors.     

Transformation of Electronic Health Records and Questionnaire Data to OMOP CDM: A Feasibility Study Using SG_T2DM Dataset

Background  Diabetes mellitus (DM) is an important public health concern in Singapore and places a massive burden on health care spending. Tackling chronic diseases such as DM requires innovative strategies to integrate patients'' data from diverse sources and use scientific discovery to inform clinical practice that can help better manage the disease. The Observational Medical Outcomes Partnership (OMOP) Common Data Model (CDM) was chosen as the framework for integrating data with disparate formats. Objective  The study aimed to evaluate the feasibility of converting Singapore based data source, comprising of electronic health records (EHR), cognitive and depression assessment questionnaire data to OMOP CDM standard. Additionally, we also validate whether our OMOP CDM instance is fit for the purpose of research by executing a simple treatment pathways study using Atlas, a graphical user interface tool to conduct analysis on OMOP CDM data as a proof of concept. Methods  We used de-identified EHR, cognitive, and depression assessment questionnaires data from a tertiary care hospital in Singapore to convert it to version 5.3.1 of OMOP CDM standard. We evaluate the OMOP CDM conversion by (1) assessing the mapping coverage (that is the percentage of source terms mapped to OMOP CDM standard); (2) local raw dataset versus CDM dataset analysis; and (3) Implementing Harmonized Intrinsic Data Quality Framework using an open-source R package called Data Quality Dashboard. Results  The content coverage of OMOP CDM vocabularies is more than 90% for clinical data, but only around 11% for questionnaire data. The comparison of characteristics between source and target data returned consistent results and our transformed data did not pass 38 (1.4%) out of 2,622 quality checks. Conclusion  Adoption of OMOP CDM at our site demonstrated that EHR data are feasible for standardization with minimal information loss, whereas challenges remain for standardizing cognitive and depression assessment questionnaire data that requires further work.     

Linking a Consortium-Wide Data Quality Assessment Tool with the MIRACUM Metadata Repository

Background  Many research initiatives aim at using data from electronic health records (EHRs) in observational studies. Participating sites of the German Medical Informatics Initiative (MII) established data integration centers to integrate EHR data within research data repositories to support local and federated analyses. To address concerns regarding possible data quality (DQ) issues of hospital routine data compared with data specifically collected for scientific purposes, we have previously presented a data quality assessment (DQA) tool providing a standardized approach to assess DQ of the research data repositories at the MIRACUM consortium''s partner sites. Objectives  Major limitations of the former approach included manual interpretation of the results and hard coding of analyses, making their expansion to new data elements and databases time-consuming and error prone. We here present an enhanced version of the DQA tool by linking it to common data element definitions stored in a metadata repository (MDR), adopting the harmonized DQA framework from Kahn et al and its application within the MIRACUM consortium. Methods  Data quality checks were consequently aligned to a harmonized DQA terminology. Database-specific information were systematically identified and represented in an MDR. Furthermore, a structured representation of logical relations between data elements was developed to model plausibility-statements in the MDR. Results  The MIRACUM DQA tool was linked to data element definitions stored in a consortium-wide MDR. Additional databases used within MIRACUM were linked to the DQ checks by extending the respective data elements in the MDR with the required information. The evaluation of DQ checks was automated. An adaptable software implementation is provided with the R package DQAstats . Conclusion  The enhancements of the DQA tool facilitate the future integration of new data elements and make the tool scalable to other databases and data models. It has been provided to all ten MIRACUM partners and was successfully deployed and integrated into their respective data integration center infrastructure.     

The My Diabetes Care Patient Portal Intervention: Usability and Pre-Post Assessment

Background  My Diabetes Care (MDC) is a novel, multifaceted patient portal intervention designed to help patients better understand their diabetes health data and support self-management. MDC uses infographics to visualize and summarize patients'' diabetes health data, incorporates motivational strategies, and provides literacy level–appropriate educational resources. Objectives  We aimed to assess the usability, acceptability, perceptions, and potential impact of MDC. Methods  We recruited 69 participants from four clinics affiliated with Vanderbilt University Medical Center. Participants were given 1 month of access to MDC and completed pre- and post-questionnaires including validated measures of usability and patient activation, and questions about user experience. Results  Sixty participants completed the study. Participants'' mean age was 58, 55% were females, 68% were Caucasians, and 48% had limited health literacy (HL). Most participants (80%) visited MDC three or more times and 50% spent a total of ≥15 minutes on MDC. Participants'' median System Usability Scale (SUS) score was 78.8 [Q1, Q3: 72.5, 87.5] and significantly greater than the threshold value of 68 indicative of “above average” usability ( p  < 0.001). The median SUS score of patients with limited HL was similar to those with adequate HL (77.5 [72.5, 85.0] vs. 82.5 [72.5, 92.5]; p  = 0.41). Participants most commonly reported the literacy level–appropriate educational links and health data infographics as features that helped them better understand their diabetes health data (65%). All participants (100%) intended to continue to use MDC. Median Patient Activation Measure® scores increased postintervention (64.3 [55.6, 72.5] vs. 67.8 [60.6, 75.0]; p  = 0.01). Conclusion  Participants, including those with limited HL, rated the usability of MDC above average, anticipated continued use, and identified key features that improved their understanding of diabetes health data. Patient activation improved over the study period. Our findings suggest MDC may be a beneficial addition to existing patient portals.     

Coronary Artery Disease Phenotype Detection in an Academic Hospital System Setting

Background  The United States, and especially West Virginia, have a tremendous burden of coronary artery disease (CAD). Undiagnosed familial hypercholesterolemia (FH) is an important factor for CAD in the U.S. Identification of a CAD phenotype is an initial step to find families with FH. Objective  We hypothesized that a CAD phenotype detection algorithm that uses discrete data elements from electronic health records (EHRs) can be validated from EHR information housed in a data repository. Methods  We developed an algorithm to detect a CAD phenotype which searched through discrete data elements, such as diagnosis, problem lists, medical history, billing, and procedure (International Classification of Diseases [ICD]-9/10 and Current Procedural Terminology [CPT]) codes. The algorithm was applied to two cohorts of 500 patients, each with varying characteristics. The second (younger) cohort consisted of parents from a school child screening program. We then determined which patients had CAD by systematic, blinded review of EHRs. Following this, we revised the algorithm by refining the acceptable diagnoses and procedures. We ran the second algorithm on the same cohorts and determined the accuracy of the modification. Results  CAD phenotype Algorithm I was 89.6% accurate, 94.6% sensitive, and 85.6% specific for group 1. After revising the algorithm (denoted CAD Algorithm II) and applying it to the same groups 1 and 2, sensitivity 98.2%, specificity 87.8%, and accuracy 92.4; accuracy 93% for group 2. Group 1 F1 score was 92.4%. Specific ICD-10 and CPT codes such as “coronary angiography through a vein graft” were more useful than generic terms. Conclusion  We have created an algorithm, CAD Algorithm II, that detects CAD on a large scale with high accuracy and sensitivity (recall). It has proven useful among varied patient populations. Use of this algorithm can extend to monitor a registry of patients in an EHR and/or to identify a group such as those with likely FH.     

Early Detection of Prediabetes and T2DM Using Wearable Sensors and Internet-of-Things-Based Monitoring Applications

Background  Prediabetes and type 2 diabetes mellitus (T2DM) are one of the major long-term health conditions affecting global healthcare delivery. One of the few effective approaches is to actively manage diabetes via a healthy and active lifestyle. Objectives  This research is focused on early detection of prediabetes and T2DM using wearable technology and Internet-of-Things-based monitoring applications. Methods  We developed an artificial intelligence model based on adaptive neuro-fuzzy inference to detect prediabetes and T2DM via individualized monitoring. The key contributing factors to the proposed model include heart rate, heart rate variability, breathing rate, breathing volume, and activity data (steps, cadence, and calories). The data was collected using an advanced wearable body vest and combined with manual recordings of blood glucose, height, weight, age, and sex. The model analyzed the data alongside a clinical knowledgebase. Fuzzy rules were used to establish baseline values via existing interventions, clinical guidelines, and protocols. Results  The proposed model was tested and validated using Kappa analysis and achieved an overall agreement of 91%. Conclusion  We also present a 2-year follow-up observation from the prediction results of the original model. Moreover, the diabetic profile of a participant using M-health applications and a wearable vest (smart shirt) improved when compared to the traditional/routine practice.     

Assessing Data Adequacy for High Blood Pressure Clinical Decision Support: A Quantitative Analysis

Objective  This study examines guideline-based high blood pressure (HBP) and hypertension recommendations and evaluates the suitability and adequacy of the data and logic required for a Fast Healthcare Interoperable Resources (FHIR)-based, patient-facing clinical decision support (CDS) HBP application. HBP is a major predictor of adverse health events, including stroke, myocardial infarction, and kidney disease. Multiple guidelines recommend interventions to lower blood pressure, but implementation requires patient-centered approaches, including patient-facing CDS tools. Methods  We defined concept sets needed to measure adherence to 71 recommendations drawn from eight HBP guidelines. We measured data quality for these concepts for two cohorts (HBP screening and HBP diagnosed) from electronic health record (EHR) data, including four use cases (screening, nonpharmacologic interventions, pharmacologic interventions, and adverse events) for CDS. Results  We identified 102,443 people with diagnosed and 58,990 with undiagnosed HBP. We found that 21/35 (60%) of required concept sets were unused or inaccurate, with only 259 (25.3%) of 1,101 codes used. Use cases showed high inclusion (0.9–11.2%), low exclusion (0–0.1%), and missing patient-specific context (up to 65.6%), leading to data in 2/4 use cases being insufficient for accurate alerting. Discussion  Data quality from the EHR required to implement recommendations for HBP is highly inconsistent, reflecting a fragmented health care system and incomplete implementation of standard terminologies and workflows. Although imperfect, data were deemed adequate for two test use cases. Conclusion  Current data quality allows for further development of patient-facing FHIR HBP tools, but extensive validation and testing is required to assure precision and avoid unintended consequences.     

A Perioperative Care Display for Understanding High Acuity Patients

Background  The data visualization literature asserts that the details of the optimal data display must be tailored to the specific task, the background of the user, and the characteristics of the data. The general organizing principle of a concept-oriented display is known to be useful for many tasks and data types. Objectives  In this project, we used general principles of data visualization and a co-design process to produce a clinical display tailored to a specific cognitive task, chosen from the anesthesia domain, but with clear generalizability to other clinical tasks. To support the work of the anesthesia-in-charge (AIC) our task was, for a given day, to depict the acuity level and complexity of each patient in the collection of those that will be operated on the following day. The AIC uses this information to optimally allocate anesthesia staff and providers across operating rooms. Methods  We used a co-design process to collaborate with participants who work in the AIC role. We conducted two in-depth interviews with AICs and engaged them in subsequent input on iterative design solutions. Results  Through a co-design process, we found (1) the need to carefully match the level of detail in the display to the level required by the clinical task, (2) the impedance caused by irrelevant information on the screen such as icons relevant only to other tasks, and (3) the desire for a specific but optional trajectory of increasingly detailed textual summaries. Conclusion  This study reports a real-world clinical informatics development project that engaged users as co-designers. Our process led to the user-preferred design of a single binary flag to identify the subset of patients needing further investigation, and then a trajectory of increasingly detailed, text-based abstractions for each patient that can be displayed when more information is needed.     

Health Intelligence Atlas: A Core Tool for Public Health Intelligence

Background  The dramatic increase in complexity and volume of health data has challenged traditional health systems to deliver useful information to their users. The novel coronavirus disease 2019 (COVID-19) pandemic has further exacerbated this problem and demonstrated the critical need for the 21st century approach. This approach needs to ingest relevant, diverse data sources, analyze them, and generate appropriate health intelligence products that enable users to take more effective and efficient actions for their specific challenges. Objectives  This article characterizes the Health Intelligence Atlas (HI-Atlas) development and implementation to produce Public Health Intelligence (PHI) that supports identifying and prioritizing high-risk communities by public health authorities. The HI-Atlas moves from post hoc observations to a proactive model-based approach for preplanning COVID-19 vaccine preparedness, distribution, and assessing the effectiveness of those plans. Results  Details are presented on how the HI-Atlas merged traditional surveillance data with social intelligence multidimensional data streams to produce the next level of health intelligence. Two-model use cases in a large county demonstrate how the HI-Atlas produced relevant PHI to inform public health decision makers to (1) support identification and prioritization of vulnerable communities at risk for COVID-19 spread and vaccine hesitancy, and (2) support the implementation of a generic model for planning equitable COVID-19 vaccine preparedness and distribution. Conclusion  The scalable models of data sources, analyses, and smart hybrid data layer visualizations implemented in the HI-Atlas are the Health Intelligence tools designed to support real-time proactive planning and monitoring for COVID-19 vaccine preparedness and distribution in counties and states.     

Personalized Massive Open Online Course for Childhood Cancer Survivors: Behind the Scenes

Background  Today, in France, it is estimated that 1 in 850 people aged between 20 and 45 years has been treated for childhood cancer, which equals 40,000 to 50,000 people. As late effects of the cancer and its treatment affect a large number of childhood cancer survivors (CCS) and only 30% of them benefit from an efficient long-term follow-up care for prevention, early detection, and treatment of late effects, health education of CCS represents a challenge of public health. Objectives  Massive open online courses (MOOCs) are a recent innovative addition to the online learning landscape. This entertaining and practical tool could easily allow a deployment at a national level and make reliable information available for all the CCS in the country, wherever they live. Methods  The MOOC team brings together a large range of specialists involved in the long-term follow-up care, but also associations of CCS, video producers, a communication consultant, a pedagogical designer, a cartoonist and a musician. We have designed three modules addressing transversal issues (lifestyle, importance of psychological support, risks of fertility problems) and eight modules covering organ-specific problems. Detailed data on childhood cancer treatments received were used to allocate the specific modules to each participant. Results  This paper presents the design of the MOOC entitled “Childhood Cancer, Living Well, After,” and how its feasibility and its impact on CCS knowledge will be measured. The MOOC about long-term follow-up after childhood cancer, divided into 11 modules, involved 130 participants in its process, and resulted in a 170-minute film. The feasibility study included 98 CCS (31 males vs. 67 females; p  < 0.0001). Conclusion  Such personalized, free, and online courses with an online forum and a possible psychologist consultation based on unique characteristics and needs of each survivor population could improve adherence to long-term follow-up without alarming them unnecessarily.     

Leveraging Real-World Data for the Selection of Relevant Eligibility Criteria for the Implementation of Electronic Recruitment Support in Clinical Trials

Background  Even though clinical trials are indispensable for medical research, they are frequently impaired by delayed or incomplete patient recruitment, resulting in cost overruns or aborted studies. Study protocols based on real-world data with precisely expressed eligibility criteria and realistic cohort estimations are crucial for successful study execution. The increasing availability of routine clinical data in electronic health records (EHRs) provides the opportunity to also support patient recruitment during the prescreening phase. While solutions for electronic recruitment support have been published, to our knowledge, no method for the prioritization of eligibility criteria in this context has been explored. Methods  In the context of the Electronic Health Records for Clinical Research (EHR4CR) project, we examined the eligibility criteria of the KATHERINE trial. Criteria were extracted from the study protocol, deduplicated, and decomposed. A paper chart review and data warehouse query were executed to retrieve clinical data for the resulting set of simplified criteria separately from both sources. Criteria were scored according to disease specificity, data availability, and discriminatory power based on their content and the clinical dataset. Results  The study protocol contained 35 eligibility criteria, which after simplification yielded 70 atomic criteria. For a cohort of 106 patients with breast cancer and neoadjuvant treatment, 47.9% of data elements were captured through paper chart review, with the data warehouse query yielding 26.9% of data elements. Score application resulted in a prioritized subset of 17 criteria, which yielded a sensitivity of 1.00 and specificity 0.57 on EHR data (paper charts, 1.00 and 0.80) compared with actual recruitment in the trial. Conclusion  It is possible to prioritize clinical trial eligibility criteria based on real-world data to optimize prescreening of patients on a selected subset of relevant and available criteria and reduce implementation efforts for recruitment support. The performance could be further improved by increasing EHR data coverage.     

Rapid Implementation of a Complex,Multimodal Technology Response to COVID-19 at an Integrated Community-Based Health Care System

Background  The rapid spread of severe acute respiratory syndrome coronavirus-2 or SARS-CoV-2 necessitated a scaled treatment response to the novel coronavirus disease 2019 (COVID-19). Objective  This study aimed to characterize the design and rapid implementation of a complex, multimodal, technology response to COVID-19 led by the Intermountain Healthcare''s (Intermountain''s) Care Transformation Information Systems (CTIS) organization to build pandemic surge capacity. Methods  Intermountain has active community-spread cases of COVID-19 that are increasing. We used the Centers for Disease Control and Prevention Pandemic Intervals Framework (the Framework) to characterize CTIS leadership''s multimodal technology response to COVID-19 at Intermountain. We provide results on implementation feasibility and sustainability of health information technology (HIT) interventions as of June 30, 2020, characterize lessons learned and identify persistent barriers to sustained deployment. Results  We characterize the CTIS organization''s multimodal technology response to COVID-19 in five relevant areas of the Framework enabling (1) incident management, (2) surveillance, (3) laboratory testing, (4) community mitigation, and (5) medical care and countermeasures. We are seeing increased use of traditionally slow-to-adopt technologies that create additional surge capacity while sustaining patient safety and care quality. CTIS leadership recognized early that a multimodal technology intervention could enable additional surge capacity for health care delivery systems with a broad geographic and service scope. A statewide central tracking system to coordinate capacity planning and management response is needed. Order interoperability between health care systems remains a barrier to an integrated response. Conclusion  The rate of future pandemics is estimated to increase. The pandemic response of health care systems, like Intermountain, offers a blueprint for the leadership role that HIT organizations can play in mainstream care delivery, enabling a nimbler, virtual health care delivery system that is more responsive to current and future needs.     

Patient Portals: Useful for Whom and for What? A Cross-Sectional Analysis of National Survey Data

Background  Patients who use patient portals may be more engaged and empowered in their care; however, differences in who accesses patient portals remain. The characteristics of who uses patient portals more frequently and who perceives them as useful may also differ, as well as which functions people use. Objective  We assessed the characteristics of patient portal users to examine who uses them more frequently and who perceives them as useful. In addition, we wanted to see if those who use them more frequently or perceive them to be more useful use different functions or more functions of patient portals. Methods  Pooled cross-sectional data from 2017 to 2018 Health Information National Trends Survey (HINTS) were used. Ordinal regression models were developed to assess frequency of use and perceived usefulness by demographics, and multivariable logistic regression models were used to examine the association between the use of 10 patient portal functions and frequency of use and perceived usefulness of patient portals. Results  The odds of using patient portals more frequently were higher among those with Bachelor''s degrees, incomes between $35,000 and $75,000, and those with two or more chronic conditions. Respondents with three or more chronic conditions had higher odds of rating patient portals as useful. Those who used their patient portal 10 or more times in the past year had higher odds of using all functions except for viewing test results compared with those who used their patient portal one to two times per year. Those who rated patient portals as “very useful” had higher odds of using seven of the functions compared with those who rated them “not very”/“not at all useful.” Conclusion  It is important to continue to assess usefulness, frequency of use, and overall patient portal function use to identify opportunities to increase patient engagement with patient portals.     

A Framework for Systematic Assessment of Clinical Trial Population Representativeness Using Electronic Health Records Data

Background  Clinical trials are the gold standard for generating robust medical evidence, but clinical trial results often raise generalizability concerns, which can be attributed to the lack of population representativeness. The electronic health records (EHRs) data are useful for estimating the population representativeness of clinical trial study population. Objectives  This research aims to estimate the population representativeness of clinical trials systematically using EHR data during the early design stage. Methods  We present an end-to-end analytical framework for transforming free-text clinical trial eligibility criteria into executable database queries conformant with the Observational Medical Outcomes Partnership Common Data Model and for systematically quantifying the population representativeness for each clinical trial. Results  We calculated the population representativeness of 782 novel coronavirus disease 2019 (COVID-19) trials and 3,827 type 2 diabetes mellitus (T2DM) trials in the United States respectively using this framework. With the use of overly restrictive eligibility criteria, 85.7% of the COVID-19 trials and 30.1% of T2DM trials had poor population representativeness. Conclusion  This research demonstrates the potential of using the EHR data to assess the clinical trials population representativeness, providing data-driven metrics to inform the selection and optimization of eligibility criteria.     

Designing and Implementing an IT Architecture for a Digital Multicenter Dementia Registry: digiDEM Bayern

Background  Registries are an essential research tool to investigate the long-term course of diseases and their impact on the affected. The project digiDEM Bayern will set up a prospective dementia registry to collect long-term data of people with dementia and their caregivers in Bavaria (Germany) supported by more than 300 research partners. Objective  The objective of this article is to outline an information technology (IT) architecture for the integration of a registry and comprehensive participant management in a dementia study. Measures to ensure high data quality, study governance, along with data privacy, and security are to be included in the architecture. Methods  The architecture was developed based on an iterative, stakeholder-oriented process. The development was inspired by the Twin Peaks Model that focuses on the codevelopment of requirements and architecture. We gradually moved from a general to a detailed understanding of both the requirements and design through a series of iterations. The experience learned from the pilot phase was integrated into a further iterative process of continuous improvement of the architecture. Results  The infrastructure provides a standardized workflow to support the electronic data collection and trace each participant''s study process. Therefore, the implementation consists of three systems: (1) electronic data capture system for Web-based or offline app-based data collection; (2) participant management system for the administration of the identity data of participants and research partners as well as of the overall study governance process; and (3) videoconferencing software for conducting interviews online. First experiences in the pilot phase have proven the feasibility of the framework. Conclusion  This article outlines an IT architecture to integrate a registry and participant management in a dementia research project. The framework was discussed and developed with the involvement of numerous stakeholders. Due to its adaptability of used software systems, a transfer to other projects should be easily possible.