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.     

Defining Data Migration Across Multidisciplinary Ambulatory Clinics Using Participatory Design

Objective  This study aimed to develop an institutional approach for defining data migration based on participatory design principles. Methods  We outline a collaborative approach to define data migration as part of an electronic health record (EHR) transition at an urban hospital with 20 ambulatory clinics, based on participatory design. We developed an institution-specific list of data for migration based on physician end-user feedback. In this paper, we review the project planning phases, multidisciplinary governance, and methods used. Results  Detailed data migration feedback was obtained from 90% of participants. Depending on the specialty, requests for historical laboratory values ranged from 2 to as many as 145 unique laboratory types. Lookback periods requested by physicians varied and were ultimately assigned to provide the most clinical data. This clinical information was then combined to synthesize an overall proposed data migration request on behalf of the institution. Conclusion  Institutions undergoing an EHR transition should actively involve physician end-users and key stakeholders. Physician feedback is vital for developing a clinically relevant EHR environment but is often difficult to obtain. Challenges include physician time constraints and overall knowledge about health information technology. This study demonstrates how a participatory design can serve to improve the clinical end-user''s understanding of the technical aspects of an EHR implementation, as well as enhance the outcomes of such projects.     

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.     

Optimizing Clinical Monitoring Tools to Enhance Patient Review by Pharmacists

Background  The Clinical Monitoring List (CML) is a real-time scoring system and intervention tool used by Mayo Clinic pharmacists caring for hospitalized patients. Objective  The study aimed to describe the iterative development and implementation of pharmacist clinical monitoring tools within the electronic health record at a multicampus health system enterprise. Methods  Between October 2018 and January 2019, pharmacists across the enterprise were surveyed to determine opportunities and gaps in CML functionality. Responses were received from 39% ( n  = 162) of actively staffing inpatient pharmacists. Survey responses identified three main gaps in CML functionality: (1) the desire for automated checklists of tasks, (2) additional rule logic closely aligning with clinical practice guidelines, and (3) the ability to dismiss and defer rules. The failure mode and effect analysis were used to assess risk areas within the CML. To address identified gaps, two A/B testing pilots were undertaken. The first pilot analyzed the effect of updated CML rule logic on pharmacist satisfaction in the domains of automated checklists and guideline alignment. The second pilot assessed the utility of a Clinical Monitoring Navigator (CMN) functioning in conjunction with the CML to display rules with selections to dismiss or defer rules until a user-specified date. The CMN is a workspace to guide clinical end user workflows; permitting the review and actions to be completed within one screen using EHR functionality. Results  A total of 27 pharmacists across a broad range of practice specialties were selected for two separate two-week pilot tests. Upon pilot completion, participants were surveyed to assess the effect of updates on performance gaps. Conclusion  Findings from the enterprise-wide survey and A/B pilot tests were used to inform final build decisions and planned enterprise-wide updated CML and CMN launch. This project serves as an example of the utility of end-user feedback and pilot testing to inform project decisions, optimize usability, and streamline build activities.     

Patient Cohort Identification on Time Series Data Using the OMOP Common Data Model

Background  The identification of patient cohorts for recruiting patients into clinical trials requires an evaluation of study-specific inclusion and exclusion criteria. These criteria are specified depending on corresponding clinical facts. Some of these facts may not be present in the clinical source systems and need to be calculated either in advance or at cohort query runtime (so-called feasibility query). Objectives  We use the Observational Medical Outcomes Partnership (OMOP) Common Data Model (CDM) as the repository for our clinical data. However, Atlas, the graphical user interface of OMOP, does not offer the functionality to perform calculations on facts data. Therefore, we were in search for a different approach. The objective of this study is to investigate whether the Arden Syntax can be used for feasibility queries on the OMOP CDM to enable on-the-fly calculations at query runtime, to eliminate the need to precalculate data elements that are involved with researchers'' criteria specification. Methods  We implemented a service that reads the facts from the OMOP repository and provides it in a form which an Arden Syntax Medical Logic Module (MLM) can process. Then, we implemented an MLM that applies the eligibility criteria to every patient data set and outputs the list of eligible cases (i.e., performs the feasibility query). Results  The study resulted in an MLM-based feasibility query that identifies cases of overventilation as an example of how an on-the-fly calculation can be realized. The algorithm is split into two MLMs to provide the reusability of the approach. Conclusion  We found that MLMs are a suitable technology for feasibility queries on the OMOP CDM. Our method of performing on-the-fly calculations can be employed with any OMOP instance and without touching existing infrastructure like the Extract, Transform and Load pipeline. Therefore, we think that it is a well-suited method to perform on-the-fly calculations on OMOP.     

A Mobile,Electronic Health Record-Connected Application for Managing Team Workflows in Inpatient Care

Background  Clinical workflows require the ability to synthesize and act on existing and emerging patient information. While offering multiple benefits, in many circumstances electronic health records (EHRs) do not adequately support these needs. Objectives  We sought to design, build, and implement an EHR-connected rounding and handoff tool with real-time data that supports care plan organization and team-based care. This article first describes our process, from ideation and development through implementation; and second, the research findings of objective use, efficacy, and efficiency, along with qualitative assessments of user experience. Methods  Guided by user-centered design and Agile development methodologies, our interdisciplinary team designed and built Carelign as a responsive web application, accessible from any mobile or desktop device, that gathers and integrates data from a health care institution''s information systems. Implementation and iterative improvements spanned January to July 2016. We assessed acceptance via usage metrics, user observations, time–motion studies, and user surveys. Results  By July 2016, Carelign was implemented on 152 of 169 total inpatient services across three hospitals staffing 1,616 hospital beds. Acceptance was near-immediate: in July 2016, 3,275 average unique weekly users generated 26,981 average weekly access sessions; these metrics remained steady over the following 4 years. In 2016 and 2018 surveys, users positively rated Carelign''s workflow integration, support of clinical activities, and overall impact on work life. Conclusion  User-focused design, multidisciplinary development teams, and rapid iteration enabled creation, adoption, and sustained use of a patient-centered digital workflow tool that supports diverse users'' and teams'' evolving care plan organization needs.     

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.     

The Development and Piloting of the Ambulatory Electronic Health Record Evaluation Tool: Lessons Learned

Background  Substantial research has been performed about the impact of computerized physician order entry on medication safety in the inpatient setting; however, relatively little has been done in ambulatory care, where most medications are prescribed. Objective  To outline the development and piloting process of the Ambulatory Electronic Health Record (EHR) Evaluation Tool and to report the quantitative and qualitative results from the pilot. Methods  The Ambulatory EHR Evaluation Tool closely mirrors the inpatient version of the tool, which is administered by The Leapfrog Group. The tool was piloted with seven clinics in the United States, each using a different EHR. The tool consists of a medication safety test and a medication reconciliation module. For the medication test, clinics entered test patients and associated test orders into their EHR and recorded any decision support they received. An overall percentage score of unsafe orders detected, and order category scores were provided to clinics. For the medication reconciliation module, clinics demonstrated how their EHR electronically detected discrepancies between two medication lists. Results  For the medication safety test, the clinics correctly alerted on 54.6% of unsafe medication orders. Clinics scored highest in the drug allergy (100%) and drug–drug interaction (89.3%) categories. Lower scoring categories included drug age (39.3%) and therapeutic duplication (39.3%). None of the clinics alerted for the drug laboratory or drug monitoring orders. In the medication reconciliation module, three (42.8%) clinics had an EHR-based medication reconciliation function; however, only one of those clinics could demonstrate it during the pilot. Conclusion  Clinics struggled in areas of advanced decision support such as drug age, drug laboratory, and drub monitoring. Most clinics did not have an EHR-based medication reconciliation function and this process was dependent on accessing patients'' medication lists. Wider use of this tool could improve outpatient medication safety and can inform vendors about areas of improvement.     

Identifying Opportunities for Workflow Automation in Health Care: Lessons Learned from Other Industries

Background  Workflow automation, which involves identifying sequences of tasks that can be streamlined by using technology and modern computing, offers opportunities to address the United States health care system''s challenges with quality, safety, and efficiency. Other industries have successfully implemented workflow automation to address these concerns, and lessons learned from those experiences may inform its application in health care. Objective  Our aim was to identify and synthesize (1) current approaches in workflow automation across industries, (2) opportunities for applying workflow automation in health care, and (3) considerations for designing and implementing workflow automation that may be relevant to health care. Methods  We conducted a targeted review of peer-reviewed and gray literature on automation approaches. We identified relevant databases and terms to conduct the searches across sources and reviewed abstracts to identify 123 relevant articles across 11 disciplines. Results  Workflow automation is used across industries such as finance, manufacturing, and travel to increase efficiency, productivity, and quality. We found automation ranged from low to full automation, and this variation was associated with task and technology characteristics. The level of automation is linked to how well a task is defined, whether a task is repetitive, the degree of human intervention and decision-making required, and the sophistication of available technology. We found that identifying automation goals and assessing whether those goals were reached was critical, and ongoing monitoring and improvement would help to ensure successful automation. Conclusion  Use of workflow automation in other industries can inform automating health care workflows by considering the critical role of people, process, and technology in design, testing, implementation, use, and ongoing monitoring of automated workflows. Insights gained from other industries will inform an interdisciplinary effort by the Office of the National Coordinator for Health Information Technology to outline priorities for advancing health care workflow automation.     

Evaluation of Electronic Health Record-Based Suicide Risk Prediction Models on Contemporary Data

Background  Suicide risk prediction models have been developed by using information from patients'' electronic health records (EHR), but the time elapsed between model development and health system implementation is often substantial. Temporal changes in health systems and EHR coding practices necessitate the evaluation of such models in more contemporary data. Objectives  A set of published suicide risk prediction models developed by using EHR data from 2009 to 2015 across seven health systems reported c-statistics of 0.85 for suicide attempt and 0.83 to 0.86 for suicide death. Our objective was to evaluate these models'' performance with contemporary data (2014–2017) from these systems. Methods  We evaluated performance using mental health visits (6,832,439 to mental health specialty providers and 3,987,078 to general medical providers) from 2014 to 2017 made by 1,799,765 patients aged 13+ across the health systems. No visits in our evaluation were used in the previous model development. Outcomes were suicide attempt (health system records) and suicide death (state death certificates) within 90 days following a visit. We assessed calibration and computed c-statistics with 95% confidence intervals (CI) and cut-point specific estimates of sensitivity, specificity, and positive/negative predictive value. Results  Models were well calibrated; 46% of suicide attempts and 35% of suicide deaths in the mental health specialty sample were preceded by a visit (within 90 days) with a risk score in the top 5%. In the general medical sample, 53% of attempts and 35% of deaths were preceded by such a visit. Among these two samples, respectively, c-statistics were 0.862 (95% CI: 0.860–0.864) and 0.864 (95% CI: 0.860–0.869) for suicide attempt, and 0.806 (95% CI: 0.790–0.822) and 0.804 (95% CI: 0.782–0.829) for suicide death. Conclusion  Performance of the risk prediction models in this contemporary sample was similar to historical estimates for suicide attempt but modestly lower for suicide death. These published models can inform clinical practice and patient care today.     

Searching the PDF Haystack: Automated Knowledge Discovery in Scanned EHR Documents

Background  Clinicians express concern that they may be unaware of important information contained in voluminous scanned and other outside documents contained in electronic health records (EHRs). An example is “unrecognized EHR risk factor information,” defined as risk factors for heritable cancer that exist within a patient''s EHR but are not known by current treating providers. In a related study using manual EHR chart review, we found that half of the women whose EHR contained risk factor information meet criteria for further genetic risk evaluation for heritable forms of breast and ovarian cancer. They were not referred for genetic counseling. Objectives  The purpose of this study was to compare the use of automated methods (optical character recognition with natural language processing) versus human review in their ability to identify risk factors for heritable breast and ovarian cancer within EHR scanned documents. Methods  We evaluated the accuracy of the chart review by comparing our criterion standard (physician chart review) versus an automated method involving Amazon''s Textract service (Amazon.com, Seattle, Washington, United States), a clinical language annotation modeling and processing toolkit (CLAMP) (Center for Computational Biomedicine at The University of Texas Health Science, Houston, Texas, United States), and a custom-written Java application. Results  We found that automated methods identified most cancer risk factor information that would otherwise require clinician manual review and therefore is at risk of being missed. Conclusion  The use of automated methods for identification of heritable risk factors within EHRs may provide an accurate yet rapid review of patients'' past medical histories. These methods could be further strengthened via improved analysis of handwritten notes, tables, and colloquial phrases.     

Improving ACGME Compliance for Obstetric Anesthesiology Fellows Using an Automated Email Notification System

Background  The Accreditation Council for Graduate Medical Education establishes minimum case requirements for trainees. In the subspecialty of obstetric anesthesiology, requirements for fellow participation in nonobstetric antenatal procedures pose a particular challenge due to the physical location remote from labor and delivery and frequent last-minute scheduling. Objectives  In response to this challenge, we implemented an informatics-based notification system, with the aim of increasing fellow participation in nonobstetric antenatal surgeries. Methods  In December 2014 an automated email notification system to inform obstetric anesthesiology fellows of scheduled nonobstetric surgeries in pregnant patients was initiated. Cases were identified via daily automated query of the preoperative evaluation database looking for structured documentation of current pregnancy. Information on flagged cases including patient medical record number, operating room location, and date and time of procedure were communicated to fellows via automated email daily. Median fellow participation in nonobstetric antenatal procedures per quarter before and after implementation were compared using an exact Wilcoxon-Mann-Whitney test due to low baseline absolute counts. The fraction of antenatal cases representing nonobstetric procedures completed by fellows before and after implementation was compared using a Fisher''s exact test. Results  The number of nonobstetric antenatal cases logged by fellows per quarter increased significantly following implementation, from median 0[0,1] to 3[1,6] cases/quarter ( p  = 0.007). Additionally, nonobstetric antenatal cases completed by fellows as a percentage of total antenatal cases completed increased from 14% in preimplementation years to 52% in postimplementation years ( p  < 0.001). Conclusion  Through an automated email system to identify nonobstetric antenatal procedures in pregnant patients, we were able to increase the number of these cases completed by fellows during 3 years following implementation.     

Nothing for Me or About Me,Without Me: Codesign of Clinical Decision Support

Background  Partnerships among patients, families, caregivers, and clinicians are critical to helping patients lead their best lives given their specific genetics, conditions, circumstances, and the environments in which they live, work, and play. These partnerships extend to the development of health information technology, including clinical decision support (CDS). Design of these technologies, however, often occurs without a profound understanding of the true needs, wants, and concerns of patients and family members. Patient perspective is important not only for patient-facing applications but for provider-facing applications, especially those intended to support shared decision-making. Objectives  Our objective is to describe models for effectively engaging patients and caregivers during CDS development and implementation and to inspire CDS developers to partner with patients and caregivers to improve the potential impact of CDS. Methods  This article serves as a case study of how two patient activists successfully implemented models for engaging patients and caregivers in a federal program designed to increase the uptake of research evidence into clinical practice through CDS. Models included virtual focus groups, social media, agile software development, and attention to privacy and cybersecurity. Results  Impact on the federal program has been substantial and has resulted in improved CDS training materials, new prototype CDS applications, prioritization of new functionality and features, and increased engagement of patient and caregiver communities in ongoing projects. Among these opportunities is a group of developers and patient activists dedicated and committed to exploring strategic and operational opportunities to codesign CDS applications. Conclusion  Codesign and implementation of CDS can occur as a partnership among developers, implementers, patients, cybersecurity and privacy activists, and caregivers. Several approaches are viable, and an iterative process is most promising. Additional work is needed to investigate scalability of the approaches explored by this case study and to identify measures of meaningful inclusion of patients/caregivers in CDS projects.     

Novel Nonproprietary Measures of Ambulatory Electronic Health Record Use Associated with Physician Work Exhaustion

Background  Accumulating evidence indicates an association between physician electronic health record (EHR) use after work hours and occupational distress including burnout. These studies are based on either physician perception of time spent in EHR through surveys which may be prone to bias or by utilizing vendor-defined EHR use measures which often rely on proprietary algorithms that may not take into account variation in physician''s schedules which may underestimate time spent on the EHR outside of scheduled clinic time. The Stanford team developed and refined a nonproprietary EHR use algorithm to track the number of hours a physician spends logged into the EHR and calculates the Clinician Logged-in Outside Clinic (CLOC) time, the number of hours spent by a physician on the EHR outside of allocated time for patient care. Objective  The objective of our study was to measure the association between CLOC metrics and validated measures of physician burnout and professional fulfillment. Methods  Physicians from adult outpatient Internal Medicine, Neurology, Dermatology, Hematology, Oncology, Rheumatology, and Endocrinology departments who logged more than 8 hours of scheduled clinic time per week and answered the annual wellness survey administered in Spring 2019 were included in the analysis. Results  We observed a statistically significant positive correlation between CLOC ratio (defined as the ratio of CLOC time to allocated time for patient care) and work exhaustion (Pearson''s r  = 0.14; p  = 0.04), but not interpersonal disengagement, burnout, or professional fulfillment. Conclusion  The CLOC metrics are potential objective EHR activity-based markers associated with physician work exhaustion. Our results suggest that the impact of time spent on EHR, while associated with exhaustion, does not appear to be a dominant factor driving the high rates of occupational burnout in physicians.     

The Impact of Longer Consultation Time: A Simulation-Based Approach

Background  Maintaining a sufficient consultation length in primary health care (PHC) is a fundamental part of providing quality care that results in patient safety and satisfaction. Many facilities have limited capacity and increasing consultation time could result in a longer waiting time for patients and longer working hours for physicians. The use of simulation can be practical for quantifying the impact of workflow scenarios and guide the decision-making. Objective  To examine the impact of increasing consultation time on patient waiting time and physician working hours. Methods  Using discrete events simulation, we modeled the existing workflow and tested five different scenarios with a longer consultation time. In each scenario, we examined the impact of consultation time on patient waiting time, physician hours, and rate of staff utilization. Results  At baseline scenarios (5-minute consultation time), the average waiting time was 9.87 minutes and gradually increased to 89.93 minutes in scenario five (10 minutes consultation time). However, the impact of increasing consultation time on patients waiting time did not impact all patients evenly where patients who arrive later tend to wait longer. Scenarios with a longer consultation time were more sensitive to the patients'' order of arrival than those with a shorter consultation time. Conclusion  By using simulation, we assessed the impact of increasing the consultation time in a risk-free environment. The increase in patients waiting time was somewhat gradual, and patients who arrive later in the day are more likely to wait longer than those who arrive earlier in the day. Increasing consultation time was more sensitive to the patients'' order of arrival than those with a shorter consultation time.     

Agile,Easily Applicable,and Useful eHealth Usability Evaluations: Systematic Review and Expert-Validation

Background  Electronic health (eHealth) usability evaluations of rapidly developed eHealth systems are difficult to accomplish because traditional usability evaluation methods require substantial time in preparation and implementation. This illustrates the growing need for fast, flexible, and cost-effective methods to evaluate the usability of eHealth systems. To address this demand, the present study systematically identified and expert-validated rapidly deployable eHealth usability evaluation methods. Objective  Identification and prioritization of eHealth usability evaluation methods suitable for agile, easily applicable, and useful eHealth usability evaluations. Methods  The study design comprised a systematic iterative approach in which expert knowledge was contrasted with findings from literature. Forty-three eHealth usability evaluation methods were systematically identified and assessed regarding their ease of applicability and usefulness through semi-structured expert interviews with 10 European usability experts and systematic literature research. The most appropriate eHealth usability evaluation methods were selected stepwise based on the experts'' judgements of their ease of applicability and usefulness. Results  Of these 43 eHealth usability evaluation methods identified as suitable for agile, easily applicable, and useful eHealth usability evaluations, 10 were recommended by the experts based on their usefulness for rapid eHealth usability evaluations. The three most frequently recommended eHealth usability evaluation methods were Remote User Testing, Expert Review, and Rapid Iterative Test and Evaluation Method. Eleven usability evaluation methods, such as Retrospective Testing, were not recommended for use in rapid eHealth usability evaluations. Conclusion  We conducted a systematic review and expert-validation to identify rapidly deployable eHealth usability evaluation methods. The comprehensive and evidence-based prioritization of eHealth usability evaluation methods supports faster usability evaluations, and so contributes to the ease-of-use of emerging eHealth systems.