Electronic Health Record Vendors Research Articles

Ready for Prime Time? Using Normalization Process Theory to Evaluate Implementation Success of Personal Health Records Designed for Decision Making

Personal health records designed for shared decision making (SDM) have the potential to engage patients and provide opportunities for positive health outcomes. Given the limited number of published interventions that become normal practice, this preimplementation evaluation of an integrated SDM personal health record system (e-PHR) was underpinned by Normalization Process Theory (NPT). The theory provides a framework to analyze cognitive and behavioral mechanisms known to influence implementation success. A mixed-methods investigation was utilized to explain the work required to implement e-PHR and its potential to integrate into practice. Patients, care providers, and electronic health record (EHR) and clinical leaders (n = 27) offered a rich explanation of the implementation work. Reliability tests of the NPT-based instrument negated the use of scores for two of the four mechanisms. Participants indicated that e-PHR made sense as explained by two qualitative themes: game-changing technology and sensibility of change. Participants appraised e-PHR as explained by two themes: reflecting on value and monitoring and adapting. The combined qualitative and quantitative results for the other two NPT mechanisms corroborated. Participants strongly agreed (score = 4.6/5) with processes requiring an investment in commitment, explained by two themes: sharing ownership of the work and enabling involvement. Weak agreement (score = 3.6/5) was observed with processes requiring an investment in effort, explained by one theme: uncovering the challenge of building collective action, and three subthemes: assessing fit, adapting to change together, and investing in the change. Finally, participants strongly agreed (score = 4.5/5) that e-PHR would positively affect engagement in self-management decision-making in two themes: care is efficient, and care is patient-centered. Overall, successful integration of e-PHR will only be attained when systemic effort is invested to enact it. Additional investigation is needed to explore the collective action gaps to inform priorities and approaches for future implementation success. This research has implications for patients, care providers, EHR vendors, and the healthcare system for improving the effectiveness and efficiency of patient-centric services. Findings confirm the usefulness of NPT for planning and understanding implementation success of PHRs.

Commercial Interests in Continuing Medical Education: Where Do Electronic Health Record Vendors Fit?

The Accreditation Council for Continuing Medical Education (ACCME) will not accredit an organization that it defines as a commercial interest, that is an entity that produces, markets, resells, or distributes health care goods or services consumed by, or used on, patients. Thus, commercial interests are not eligible to be accredited organizations offering continuing medical education (CME) credit to physicians. This decision is based on the concern that commercial interests may use CME events to market their products or services to physicians, who then might inappropriately prescribe or administer those products or services to patients. Studies have shown that CME events supported by pharmaceutical companies, for example, have influenced physicians' prescribing behaviors.Currently, however, the ACCME does not recognize electronic health record (EHR) vendors, which are part of a multi-billion-dollar business, as commercial interests, and it accredits them to provide or directly influence CME events. Like pharmaceutical company-sponsored CME events, EHR vendor activities, which inherently only focus on use of the sponsoring vendor's EHR system despite its potential intrinsic limitations, can lead to physician reciprocity. Such events also may inappropriately influence EHR system purchases, upgrades, and implementation decisions. These actions can negatively influence patient safety and care. Thus, the authors of this Perspective call on the ACCME to recognize EHR vendors as commercial interests and remove them from the list of accredited CME providers.

Impact of Electronic Health Record Interface Design on Unsafe Prescribing of Ciclosporin, Tacrolimus, and Diltiazem: Cohort Study in English National Health Service Primary Care.

BackgroundIn England, national safety guidance recommends that ciclosporin, tacrolimus, and diltiazem are prescribed by brand name due to their narrow therapeutic windows and, in the case of tacrolimus, to reduce the chance of organ transplantation rejection. Various small studies have shown that changes to electronic health record (EHR) system interfaces can affect prescribing choices.ObjectiveOur objectives were to assess variation by EHR systems in breach of safety guidance around prescribing of ciclosporin, tacrolimus, and diltiazem, and to conduct user-interface research into the causes of such breaches.MethodsWe carried out a retrospective cohort study using prescribing data in English primary care. Participants were English general practices and their respective EHR systems. The main outcome measures were (1) the variation in ratio of safety breaches to adherent prescribing in all practices and (2) the description of observations of EHR system usage.ResultsA total of 2,575,411 prescriptions were issued in 2018 for ciclosporin, tacrolimus, and diltiazem (over 60 mg); of these, 316,119 prescriptions breached NHS guidance (12.27%). Breaches were most common among users of the EMIS EHR system (breaches in 18.81% of ciclosporin and tacrolimus prescriptions and in 17.99% of diltiazem prescriptions), but breaches were observed in all EHR systems.ConclusionsDesign choices in EHR systems strongly influence safe prescribing of ciclosporin, tacrolimus, and diltiazem, and breaches are prevalent in general practices in England. We recommend that all EHR vendors review their systems to increase safe prescribing of these medicines in line with national guidance. Almost all clinical practice is now mediated through an EHR system; further quantitative research into the effect of EHR system design on clinical practice is long overdue.

Deterrents to Integrating the Prescription Drug Monitoring Program into the Electronic Health Record

There are over 700 prescription and illicit opioid-related deaths each year in Washington State. Integrating the Prescription Drug Monitoring Program (PDMP) data into the electronic health record (EHR) allows providers seamless access to patient controlled substances prescription histories, thereby reducing inappropriate prescribing and overdoses. The project overview describes the study focus, investigation of barriers to integrating PDMP data into EHRs across care settings for providers in Washington State who prescribe controlled substances. An online survey tool was developed to inquire about barriers to PDMP integration. The article presents survey results, indicating that 81% of respondents were not integrating PMDP data into the EHR and 52% did not plan on integration. The discussion section considers common barriers that providers identified, such as EHR vendor inability to provide an update, difficulty accessing the PDMP, and prioritization. Cost was the most significant barrier. Discovering barriers to PDMP integration allows stakeholders to address these issues and prevent overprescribing of controlled substances.

Association of Electronic Health Record Use Above Meaningful Use Thresholds With Hospital Quality and Safety Outcomes

By 2018, Medicare spent more than $30 billion to incentivize the adoption of electronic health records (EHRs), based partially on the belief that EHRs would improve health care quality and safety. In a time when most hospitals are well past minimum meaningful use (MU) requirements, examining whether EHR implementation beyond the minimum threshold is associated with increased quality and safety may guide the future focus of EHR development and incentive structures. To determine whether EHR implementation above MU performance thresholds is associated with changes in hospital patient satisfaction, efficiency, and safety. This quantile regression analysis of cross-sectional data used publicly available data sets from 2362 acute care hospitals in the United States participating in both the MU and Hospital Value-Based Purchasing (HVBP) programs from January 1 to December 31, 2016. Data were analyzed from August 1, 2019, to May 22, 2020. Seven MU program performance measures, including medication and laboratory orders placed through the EHR, online health information availability and access rates, medication reconciliation through the EHR, patient-specific educational resources, and electronic health information exchange. The HVBP outcomes included patient satisfaction survey dimensions, Medicare spending per beneficiary, and 5 types of hospital-acquired infections. Among the 2362 participating hospitals, mixed associations were found between MU measures and HVBP outcomes, all varying by outcome quantile and in some cases by interaction with EHR vendor. Computerized provider order entry (CPOE) for laboratory orders was associated with decreased ratings of every patient satisfaction outcome at middle quantiles (communication with nurses: β = -0.33 [P = .04]; communication with physicians: β = -0.50 [P < .001]; responsiveness of hospital staff: β = -0.57 [P = .03]; care transition performance: β = -0.66 [P < .001]; communication about medicines: β = -0.52 [P = .002]; cleanliness and quietness: β = -0.58 [P = .007]; discharge information: β = -0.48 [P < .001]; and overall rating: β = -0.95 [P < .001]). However, at middle quantiles, CPOE for medication orders was associated with increased ratings for communication with physicians (τ = 0.5; β = 0.54; P = .009), care transition (τ = 0.5; β = 1.24; P < .001), discharge information (τ = 0.5; β = 0.41; P = .01), and overall hospital ratings (τ = 0.5; β = 0.97; P = .02). At high quantiles, electronic health information exchange was associated with improved ratings of communication with nurses (τ = 0.9; β = 0.23; P = .03). Medication reconciliation had positive associations with increased communication with nursing at low quantiles (τ = 0.1; β = 0.60; P < .001), increased discharge information at middle quantiles (τ = 0.5; β = 0.28; P = .03), and responsiveness of hospital staff at middle (τ = 0.5; β = 0.77; P = .001) and high (τ = 0.9; β = 0.84; P = .001) quantiles. Patients accessing their health information online was not associated with any outcomes. Increased use of patient-specific educational resources identified through the EHR was associated with increased ratings of communication with physicians at high quantiles (τ = 0.9; β = 0.20; P = .02) and with decreased spending at low-spending hospitals (τ = 0.1; β = -0.40; P = .008). Increasing EHR implementation, as measured by MU criteria, was not straightforwardly associated with increased HVBP measures of patient satisfaction, spending, and safety in this study. These results call for a critical evaluation of the criteria by which EHR implementation is measured and increased attention to how different EHR products may lead to differential outcomes.

National Trends in Daily Ambulatory Electronic Health Record Use by Otolaryngologists.

Since their development in the 1970s electronic health records (EHRs) are now nearly ubiquitous. This study aims to characterize the daily interactions of otolaryngology providers with EHRs. This study was a cross-sectional review of provider efficiency profile (PEP) data, as collected by a major EHR vendor. Participating institutions had 6 months of de-identified PEP data reviewed starting January 1, 2019. PEP data is generated for providers with scheduled patients, both attendings and advanced practice providers (APPs). Time metrics are recorded when a provider is interacting with the EHR including a 5-second time-out for inactivity. Data on 269 otolaryngologists and 29 APPs from 10 institutions were evaluated. On scheduled ambulatory clinic days attendings spent 70 ± 36 (mean ± standard deviation) min interacting in the EHR versus 108 ± 46 min for APPs. Of the daily EHR time, mean time in notes, clinical review, in basket, orders, and schedule were 30.1 ± 19.4, 9.6 ± 6.1, 7.3 ± 5.8, and 5.8 ± 7.6 min, respectively. Per patient visit, median (interquartile range) time in notes, clinical review, and orders were 3.19 (2.2-4.9), 1.14 (0.63-1.8), and 0.70 (0.47-1.05) min, respectively. Mean progress note length was 4638 ± 2143 characters. Otolaryngology providers spend a meaningful portion of their clinic day interacting with the EHR. PEP data may provide means to target interventions and a metric to measure the impact of those interventions on provider EHR efficiency. 3 Laryngoscope, 131:975-981, 2021.

Clinical Decision Support for Immunization Uptake and Use in Immunization Health Information Systems.

In the United States, immunization recommendations and their associated schedules are developed by the Advisory Committee on Immunization Practices (ACIP). To assist with the translation process and better harmonize the outcomes of existing clinical decision support tools, the Centers for Disease Control and Prevention (CDC) created clinical decision support for immunization (CDSi) resources for each set of ACIP recommendations. These resources are continually updated and refined as new vaccine recommendations and clarifications become available and will be available to health information systems for a coronavirus disease 2019 (COVID-19) vaccine when one becomes available for use in the United States Objectives: To assess awareness of CDSi resources, whether CDSi resources were being used by immunization-related health information systems, and perceived impact of CDSi resources on stakeholders' work Design: Online surveys conducted from 2015-2019 including qualitative and quantitative questions Participants: The main and technical contact from each of the 64 CDC-funded immunization information system (IIS) awardees, IIS vendors, and electronic health record vendors Results: Awareness of at least one resource increased from 75% of respondents in 2015 to 100% in 2019. Use of at least one CDSi resource also increased from 47% in 2015 to 78% in 2019. About 80% or more of users of CDSi are somewhat or very highly satisfied with the resources and report a somewhat or very positive impact from using them Conclusion: As awareness and use of CDSi resources increases, the likelihood that patients receive recommended immunizations at the right time will also increase. Rapid and precise integration of vaccine recommendations into health information systems will be particularly important when a COVID-19 vaccine becomes available to help facilitate vaccine implementation.

The human resource costs of implementing autopend clinical decision support to improve health maintenance.

Sutter Health developed a novel autopend, or automated laboratory test ordering, clinical decision support (CDS) tool to coordinate the patient and physician process of completing preventive services. This study estimated the costs of developing and implementing the autopend functionality within an existing electronic health maintenance (HM) reminder system. Human resource time was measured by triangulating in-depth key informant interviews with Microsoft Outlook Calendar metadata (meetings attended) for managers and hourly data from a time-based project management tool (Project Web App) for Epic programmers. Employee time spent was multiplied by the Bureau of Labor Statistics California state hourly wages. Sutter Health is an integrated health care delivery network with more than 12,000 physicians across 100 communities serving 3 million patients. Activity-based costing methodology was used to divide the implementation into activities and the human resources required to complete them. Developing and implementing the autopend CDS took more than 3 years, involved 6 managers and 3 Epic programmers, and cost $201,500 (2013 US$) (2670 total hours), which excluded the costs of implementing the initial HM reminder system. Managers spent 90.5% of the total costs (86.6% of total hours) integrating autopend into the health system compared with 9.5% of the total costs (13.4% of total hours) spent programming the functionality. The autopend CDS might be similarly costly for other organizations to implement if their managers need to complete comparable activities. However, electronic health record vendors could include autopend as a standard package to reduce development costs and improve the uptake of this promising CDS tool.

How can we partner with electronic health record vendors on the complex journey to safer health care?

The Office of the National Coordinator for Health Information Technology released the Safety Assurance Factors for EHR Resilience (SAFER) guides in 2014. Our group developed these guides covering key facets of both electronic health record (EHR) infrastructure (eg, system configuration, contingency planning for downtime, and system-to-system interfaces) and clinical processes (eg, computer-based provider order entry with clinical decision support, test result reporting, patient identification, and clinician-to-clinician communication). The SAFER guides encourage healthy relationships between EHR vendors and users. We conducted a qualitative study over 12 months. We visited 9 health care organizations ranging in size from 1-doctor outpatient clinics to large, multisite, multihospital integrated delivery networks. We interviewed and observed clinicians, IT professionals, and administrators. From the interview transcripts and observation field notes, we identified overarching themes: technical functionality, usability, standards, testing, workflow processes, personnel to support implementation and use, infrastructure, and clinical content. In addition, we identified health care organization-EHR vendor working relationships: marine drill sergeant, mentor, development partner, seller, and parasite. We encourage health care organizations and EHR vendors to develop healthy working relationships to help address the tasks required to design, develop, implement, and maintain EHRs required to achieve safer and higher quality health care.

Pilot Implementation of Clinical Genomic Data into the Native Electronic Health Record: Challenges of Scalability

Background Recently, electronic health record (EHR) vendors have allowed for the storage of discrete genomic data within the EHR. With this new capability there remain many challenges related to adoption and implementation. Geisinger has genomic data available for approximately 145,000 patients. With the goal of integrating genetic data into the EHR for the entire sequenced population, we performed a pilot study on a subpopulation to assess the plausibility of large-scale deployment. Objectives Methods Discrete genomic variants were imported into the EHR. Maintenance guidelines and information resources were developed for each genetic condition and best practice alerts were built for pharmacogenomic variants. Weekly calls were held to address barriers to implementation. A list of challenges was maintained throughout the process. Solutions were proposed and categorized into short-term and long-term solutions. Results Challenges were identified and solutions proposed for five discrete areas: Conclusion Scaling the use of genomic data in the EHR requires the engagement of hospitals, open standards communities, laboratories, EHR vendors, and genomic information resources.

Barriers to Implementing an Artificial Intelligence Model for Unplanned Readmissions

Abstract Background Electronic health record (EHR) vendors now offer “off-the-shelf” artificial intelligence (AI) models to client organizations. Our health system faced difficulties in promoting end-user utilization of a new AI model for predicting readmissions embedded in the EHR. Objectives The aim is to conduct a case study centered on identifying barriers to uptake/utilization. Methods A qualitative study was conducted using interviews with stakeholders. The interviews were used to identify relevant stakeholders, understand current workflows, identify implementation barriers, and formulate future strategies. Results We discovered substantial variation in existing workflows around readmissions. Some stakeholders did not perform any formal readmissions risk assessment. Others accustomed to using existing risk scores such as LACE+ had concerns about transitioning to a new model. Some stakeholders had existing workflows in place that could accommodate the new model, but they were not previously aware that the new model was in production. Concerns expressed by end-users included: whether the model's predictors were relevant to their work, need for adoption of additional workflow processes, need for training and change management, and potential for unintended consequences (e.g., increased health care resource utilization due to potentially over-referring discharged patients to home health services). Conclusion AI models for risk stratification, even if “off-the-shelf” by design, are unlikely to be “plug-and-play” in health care settings. Seeking out key stakeholders and defining clear use cases early in the implementation process can better facilitate utilization of these models.

Empirical study of Data Completeness in Electronic Health Records in China

Abstract Background: As a dimension of data quality in electronic health records (EHR), data completeness plays an important role in improving quality of care. Although many studies of data management focus on constructing the factors that influence data quality for the purpose of quality improvement, the constructs that are developed for interpreting factors influencing data completeness in the EHR context have received limited attention. Methods: Based on related studies, we constructed the factors influencing EHR data completeness in a conceptual model. We then examined the proposed model by surveying clinical practitioners in China. Results: Our results show that the data quality management literature can serve as a starting point to derive a conceptual model of factors influencing data completeness in the EHR context. This study also demonstrates that “resources” should be added as a factor that influences data completeness in EHR. Conclusion: Our resulting conceptual model shows a substantial explanation of data completeness in EHR assessed in this study. Although the proposed relationships between the included factors were previously supported in the literature, our work provides the beginning empirical evidence that some relationships may not be always significantly supported. The possible explanation of these differences has been discussed in the present research. This study thus benefits decision makers and EHR program managers in implementing EHR as well as EHR vendors in the EHR integration by addressing data completeness issues. Available at: https://aisel.aisnet.org/pajais/vol12/iss2/4/ Recommended Citation Liu, Caihua; Zowghi, Didar; Talaei-Khoei, Amir; and Jin, Zhi (2020) "Empirical study of Data Completeness in Electronic Health Records in China," Pacific Asia Journal of the Association for Information Systems: Vol. 12: Iss. 2, Article 4. DOI: 10.17705/1pais.12204 Available at: https://aisel.aisnet.org/pajais/vol12/iss2/4

Abstract 06: Streamlining data management for clinical trials

Abstract The purpose of this study is to answer the FDA’s call to action for new research paradigms that rely on real-world data (RWD) to increase the efficiency of clinical trials and to conduct research using an approach that is less disruptive to the patient-provider clinical care setting. With the expansion of electronic health record (EHR) vendor structured data models, clinical study data can be directly captured using the patient’s EHR data as documented in the health care organization’s (HCOs) electronic medical record (EMR) system(s) and other electronic data sources (eSources). In partnership with HCOs, the Optum Digital Research Network is conducting a pilot to enable effective RWD extraction, normalization, and transcription from EMRs and other eSource to a read-only electronic data capture (EDC) system, effectively eliminating the need for duplicative data entry during the study. Optum is developing custom database queries to extract structured EMR data (including demographics, medications, labs, vitals, and encounters) from the respective domains of each EMR into a staging database. The raw data are then curated, normalized, and mapped into a common data model study database. In this case, the Observational Medical Outcomes Partnership (OMOP) data standard is employed as the clinical data management (CDM) model. The curated data are then mapped to Clinical Data Acquisition Standards Harmonization (CDASH) variables and transmitted to an EDC system using the EDC vendor’s application programming interface (API). The entire process is scheduled on a daily cadence (removing any duplicate data) to capture any updated visit information, medications, labs, or vitals. The EDC form fields are read-only. If there is a discrepancy or query, sites are notified requesting to confirm or correct their EHR data, which may result in an update to the respective read-only EDC form fields. Through this pilot project, Optum’s successful transfer of structured EMR data to a read-only EDC system has substantial implications for clinical research. Using data and technology, this methodology is projected to improve the efficiency (and lower costs) of clinical trials while minimizing the burden on HCOs. Citation Format: Brook Norris, Lauren Neighbors, Lucas Wale. Streamlining data management for clinical trials [abstract]. In: Proceedings of the AACR Special Conference on Advancing Precision Medicine Drug Development: Incorporation of Real-World Data and Other Novel Strategies; Jan 9-12, 2020; San Diego, CA. Philadelphia (PA): AACR; Clin Cancer Res 2020;26(12_Suppl_1):Abstract nr 06.

China

Abstract Background: As a dimension of data quality in electronic health records (EHR), data completeness plays an important role in improving quality of care. Although many studies of data management focus on constructing the factors that influence data quality for the purpose of quality improvement, the constructs that are developed for interpreting factors influencing data completeness in the EHR context have received limited attention. Methods: Based on related studies, we constructed the factors influencing EHR data completeness in a conceptual model. We then examined the proposed model by surveying clinical practitioners in China. Results: Our results show that the data quality management literature can serve as a starting point to derive a conceptual model of factors influencing data completeness in the EHR context. This study also demonstrates that “resources” should be added as a factor that influences data completeness in EHR. Conclusion: Our resulting conceptual model shows a substantial explanation of data completeness in EHR assessed in this study. Although the proposed relationships between the included factors were previously supported in the literature, our work provides the beginning empirical evidence that some relationships may not be always significantly supported. The possible explanation of these differences has been discussed in the present research. This study thus benefits decision makers and EHR program managers in implementing EHR as well as EHR vendors in the EHR integration by addressing data completeness issues.

National Trends in the Safety Performance of Electronic Health Record Systems From 2009 to 2018

Despite the broad adoption of electronic health record (EHR) systems across the continuum of care, safety problems persist. To measure the safety performance of operational EHRs in hospitals across the country during a 10-year period. This case series included all US adult hospitals nationwide that used the National Quality Forum Health IT Safety Measure EHR computerized physician order entry safety test administered by the Leapfrog Group between 2009 and 2018. Data were analyzed from July 1, 2018 to December 1, 2019. The Health IT Safety Measure test, which uses simulated medication orders that have either injured or killed patients previously to evaluate how well hospital EHRs could identify medication errors with potential for patient harm. Descriptive statistics for performance on the assessment test over time were calculated at the overall test score level, type of decision support category level, and EHR vendor level. Among 8657 hospital-years observed during the study, mean (SD) scores on the overall test increased from 53.9% (18.3%) in 2009 to 65.6% (15.4%) in 2018. Mean (SD) hospital score for the categories representing basic clinical decision support increased from 69.8% (20.8%) in 2009 to 85.6% (14.9%) in 2018. For the categories representing advanced clinical decision support, the mean (SD) score increased from 29.6% (22.4%) in 2009 to 46.1% (21.6%) in 2018. There was considerable variation in test performance by EHR. These findings suggest that despite broad adoption and optimization of EHR systems in hospitals, wide variation in the safety performance of operational EHR systems remains across a large sample of hospitals and EHR vendors. Hospitals using some EHR vendors had significantly higher test scores. Overall, substantial safety risk persists in current hospital EHR systems.

Categorization of Third-Party Apps in Electronic Health Record App Marketplaces: Systematic Search and Analysis.

BackgroundThird-party electronic health record (EHR) apps allow health care organizations to extend the capabilities and features of their EHR system. Given the widespread utilization of EHRs and the emergence of third-party apps in EHR marketplaces, it has become necessary to conduct a systematic review and analysis of apps in EHR app marketplaces.ObjectiveThe goal of this review is to organize, categorize, and characterize the availability of third-party apps in EHR marketplaces.MethodsTwo informaticists (authors JR and BW) used grounded theory principles to review and categorize EHR apps listed in top EHR vendors’ public-facing marketplaces.ResultsWe categorized a total of 471 EHR apps into a taxonomy consisting of 3 primary categories, 15 secondary categories, and 55 tertiary categories. The three primary categories were administrative (n=203, 43.1%), provider support (n=159, 33.8%), and patient care (n=109, 23.1%). Within administrative apps, we split the apps into four secondary categories: front office (n=77, 37.9%), financial (n=53, 26.1%), office administration (n=49, 24.1%), and office device integration (n=17, 8.4%). Within the provider support primary classification, we split the apps into eight secondary categories: documentation (n=34, 21.3%), records management (n=27, 17.0%), care coordination (n=23, 14.4%), population health (n=18, 11.3%), EHR efficiency (n=16, 10.1%), ordering and prescribing (n=15, 9.4%), medical device integration (n=13, 8.2%), and specialty EHR (n=12, 7.5%). Within the patient care primary classification, we split the apps into three secondary categories: patient engagement (n=50, 45.9%), clinical decision support (n=40, 36.7%), and remote care (n=18, 16.5%). Total app counts varied substantially across EHR vendors. Overall, the distribution of apps across primary categories were relatively similar, with a few exceptions.ConclusionsWe characterized and organized a diverse and rich set of third-party EHR apps. This work provides an important reference for developers, researchers, and EHR customers to more easily search, review, and compare apps in EHR app marketplaces.

Development of a Nursing Assignment Tool Using Workload Acuity Scores.

To determine a just and consistent practice for creating nursing assignments. Traditional methods of assigning patients to nurses may lead to unbalanced nursing workload. This article describes the ongoing, hospital-wide effort to evaluate and implement a nursing assignment tool based on electronic health record (EHR) functionality and auto-calculated nursing workload scores. EHR records of individual patient workload scores from all hospital units were collected from August 2017 to June 2018. A nurse-specific total workload score was summed for each staff. Then, each hospital unit's mean nurse workload score and standard deviation, along with the unit's nurse-to-patient ratio, were used to calculate levels of high, medium, and low nursing workload measurement (NWM). Mean patient-specific workload scores varied greatly across hospital units. Unit-specific nurse-to-patient ratios were factored into NWM scores to create ranges for assignments that were relatively consistent across the institution. The use of objective, electronically generated nursing workload scores, combined with traditional nurse-to-patient ratios, provides accurate real-time nurse staffing needs that can inform best practice in staffing. The confirmation of individual patient workload scores and an appreciation for the complexity of EHR vendor rules are necessary for successful implementation. Automation ensures patient safety, staff satisfaction, and optimal resource allocation.

Security and Privacy Risks Associated With Adult Patient Portal Accounts in US Hospitals

Patient portals can help caregivers better manage care for patients, but how caregivers access the patient portal could threaten patient security and privacy. To identify the proportions of hospitals that provide proxy accounts to caregivers of adult patients, endorse password sharing with caregivers, and enable patients to restrict the types of information seen by their caregivers. This national cross-sectional study included a telephone survey and was conducted from May 21, 2018, to December 20, 2018. The randomly selected sample comprised 1 independent hospital and 1 health system-affiliated general medical hospital from every US state and the District of Columbia. Specialty hospitals and those that did not have a patient portal in place were excluded. An interviewer posing as the daughter of an older adult patient called each hospital to ask about the hospital's patient portal practices. The interviewer used a structured questionnaire to obtain information on proxy account availability, password sharing, and patient control of their own information. The primary outcome was the proportion of hospitals that provided proxy accounts to caregivers of adult patients. Secondary outcomes were the proportion of hospitals with personnel who endorsed password sharing and the proportion that allowed adult patients to limit the types of information available to caregivers. After exclusions, a total of 102 (51 health system-affiliated and 51 independent) hospitals were included in the study. Of these hospitals, 69 (68%) provided proxy accounts to caregivers of adult patients and 26 (25%) did not. In 7 of 102 hospitals (7%), the surveyed personnel did not know if proxy accounts were available. In the 94 hospitals asked about password sharing between the patient and caregiver, personnel in 42 hospitals (45%) endorsed the practice. Among hospitals that provided proxy accounts, only 13 of the 69 hospitals (19%) offered controls that enabled patients to restrict the types of information their proxies could see. This study found that almost half of surveyed hospital personnel recommended password sharing and that few hospitals enabled patients to limit the types of information seen by those with proxy access. These findings suggest that hospitals and electronic health record (HER) vendors need to improve the availability and setup process of proxy accounts in a way that allows caregivers to care for patients without violating their privacy.

Impact of the European General Data Protection Regulation (GDPR) on Health Data Management in a European Union Candidate Country: A Case Study of Serbia

As of May 2018, all relevant institutions within member countries of the European Economic Area are required to comply with the European General Data Protection Regulation (GDPR) or face significant fines. This regulation has also had a notable effect on the European Union (EU) candidate countries, which are undergoing the process of harmonizing their legislature with the EU as part of the accession process. The Republic of Serbia is an example of such a candidate country, and its 2018 Personal Data Protection Act mirrors the majority of provisions in the GDPR. This paper presents the impact of the GDPR on health data management and Serbia’s capability to conduct international health data research projects. Data protection incidents reported in Serbia are explored to identify common underlying causes using a novel taxonomy of contributing factors across aspects and health system levels. The GDPR has an extraterritorial application for the non-EU data controllers who process the data of EU citizens and residents, which mainly affects private practices used by medical tourists from the EU, public health care institutions frequented by foreigners, as well as expatriates, dual citizens, tourists, and other visitors. Serbia generally does not have well-established procedures to support international research collaborations around its health data. For smaller projects, contractual arrangements can be made with health data providers and their ethics committees. Even then, organizations that have not previously participated in similar ventures may require approval or support from health authorities. Extensive studies that involve multisite data typically require the support of central health system institutions and relevant research data aggregators or electronic health record vendors. The lack of a framework for preparation, anonymization, and assurance of privacy preservation forces researchers to rely heavily on local expertise and support. Given the current limitation and potential issues with the legislation, it remains to be seen whether the move toward the GDPR will be beneficial for the Serbian health system, medical research, protection of personal data and privacy rights, and research capacity. Although significant progress has been made so far, a strategic approach is needed at the national level to address insufficient resources in the area of data protection and develop the personal data protection environment further. This will also require a targeted educational effort among health workers and decision makers, aiming to improve awareness and develop skills and knowledge necessary for the workforce.

Deploying Patient-Facing Application Programming Interfaces: Thematic Analysis of Health System Experiences.

BackgroundHealth systems have recently started to activate patient-facing application programming interfaces (APIs) to facilitate patient access to health data and other interactions.ObjectiveThis study sought to ascertain health systems’ understanding, strategies, governance, and organizational infrastructure around patient-facing APIs, as well as their business drivers and barriers, to facilitate national learning, policy, and progress toward adoption.MethodsWe performed a content analysis of semistructured interviews with a convenience sample of 10 health systems known to be leading adopters of health technology, having either implemented or planning to implement patient-facing APIs.ResultsOf the 10 health systems, eight had operational patient-facing APIs, with organizational strategy driven most by federal policy, the emergence of Health Records on iPhone, and feelings of ethical obligation. The two priority use cases identified were enablement of a patient’s longitudinal health record and digital interactions with the health system. The themes most frequently cited as barriers to the increased use of patient-facing APIs were security concerns, an immature app ecosystem that does not currently offer superior functionality compared with widely adopted electronic health record (EHR)–tethered portals, a lack of business drivers, EHR vendor hesitation toward data sharing, and immature technology and standards.ConclusionsOur findings reveal heterogeneity in health system understanding and approaches to the implementation and use of patient-facing APIs. Ongoing study, targeted policy interventions, and sharing of best practices appear necessary to achieve successful national implementation.