Electronic Medical Record Users Research Articles

Usability assessment of an open-source simulated electronic medical record in pharmacy education

Background and PurposeThe growth of electronic medical records (EMRs) has been a critical component of evolving pharmacy practice, catalyzed by foundational initiatives such as the HITECH Act in 2009. The objective of this study was to evaluate the usability and student perceptions of a novel, open-source, educational EMR (EdEMR) for integration in the PharmD program at the University of British Columbia (UBC). Educational Activity and SettingParticipants were PharmD students at UBC and had to complete a series of survey questions and EdEMR tasks including a Systems Usability Scale (SUS) evaluation. Task completion time, mouse clicks, SUS score, and 5-point Likert scale rankings of student opinions on the use of the EdEMR were collected. FindingsSeven students successfully completed the study. Participants self-ranked themselves as novice EMR users who used EMRs a few times a week. The mean time to complete the tasks was 6 min, 42 seconds and was approximately double that of benchmark times (mean benchmark = 2 min, 20 seconds). The EdEMR scored within the upper quartile of SUS scores (mean = 83), indicating good usability. Participants identified that the EMR would most optimally be used for distance learning and case-based learning. SummaryThe EdEMR showed good usability as demonstrated by the successfully completed tasks and SUS score. Participants were receptive to the EdEMR's application as an online learning tool and for case-based learning. The EdEMR is an enticing, open-source tool currently available to all pharmacy programs and could further support online and remote teaching.

Electronic medical records and primary care quality: Evidence from Manitoba

Improvements in quality of care through supporting decision-making processes and increased efficiency have prompted widespread implementation of electronic medical records (EMRs) in Canada. Using a set of indicators of preventive care, chronic disease management, and hospitalizations due to ambulatory care sensitive conditions (ACSC), this study measures the effect of EMR adoption on quality of primary care measures. Population-based data for the Canadian province of Manitoba are used in a difference-in-differences approach with patient- and time-fixed effects. Evidence of changes in the selected quality-of-care indicators is weak, with preventive care, management of asthma, and hospitalizations showing no significant change due to EMR adoption. A statistically significant increase in the quality of diabetes care was found for EMR users, changes being larger for late EMR adopters which is possibly explained by a network effect. This research demonstrates that measuring whether EMRs prompt changes in the quality of care confronts serious challenges. The rapid evolution and gradual adoption of EMR technology, the inevitable learning/acceptance process by individual health practitioners, and its potential reflection on different patient populations create unmeasurable variables that confound EMRs' impact. This study also underscores the importance of data development to support the economic value of EMRs.

Modeling physician variability to prioritize relevant medical record information.

ObjectivePatient information can be retrieved more efficiently in electronic medical record (EMR) systems by using machine learning models that predict which information a physician will seek in a clinical context. However, information-seeking behavior varies across EMR users. To explicitly account for this variability, we derived hierarchical models and compared their performance to nonhierarchical models in identifying relevant patient information in intensive care unit (ICU) cases.Materials and methodsCritical care physicians reviewed ICU patient cases and selected data items relevant for presenting at morning rounds. Using patient EMR data as predictors, we derived hierarchical logistic regression (HLR) and standard logistic regression (LR) models to predict their relevance.ResultsIn 73 pairs of HLR and LR models, the HLR models achieved an area under the receiver operating characteristic curve of 0.81, 95% confidence interval (CI) [0.80–0.82], which was statistically significantly higher than that of LR models (0.75, 95% CI [0.74–0.76]). Further, the HLR models achieved statistically significantly lower expected calibration error (0.07, 95% CI [0.06–0.08]) than LR models (0.16, 95% CI [0.14–0.17]).DiscussionThe physician reviewers demonstrated variability in selecting relevant data. Our results show that HLR models perform significantly better than LR models with respect to both discrimination and calibration. This is likely due to explicitly modeling physician-related variability.ConclusionHierarchical models can yield better performance when there is physician-related variability as in the case of identifying relevant information in the EMR.

The Impact of Nurse Experience and Sequential Organ Failure Assessment on Nurses’ Workflow in an Intensive Care Unit

The purpose of this study is to understand the impact of Nurse Experience (NE) and Sequential Organ Failure Assessment (SOFA) on nurses’ workflow and process time in an Intensive Care Unit (ICU). In this study, a combination of data from the time-motion study observations, ICU nurses’ Real-Time Location System (RTLS), and the Real-Time Measurement System (RTMS) data were used to develop multiple Hierarchical Task Analysis (HTA) charts. HTA chart helped us to identify the ICU nurses’ workflow patterns. The results showed that ICU nurses spent over 47% of their time on performing primary care (in-room activities) and 25% on out-of-room activities. Furthermore, NE levels and SOFA scores had significant impacts on the average process time of nurse handoff, in-room Electronic Medical Record (EMR) charting, and the workflow patterns of out-of-room activities and out-of-room EMR usage. The outcomes of this study will provide a detailed workflow of ICU nurses and might help nurse managers to conduct a better strategy for improving ICU nurse’s workflow.

Abstract 4166A: An artificial intelligence based meta-analysis of publicly available single cell RNA-seq datasets for hematopoietic and lymphoid malignancies identifies repurposable cancer drug targets

Abstract Background: Hematopoietic malignancies represent a broad category of diseases that originate in blood or lymphoid tissues and will account for roughly 10 percent of the estimated 1.7 million new cancer diagnoses in 2019. Traditional treatment options include chemotherapy, radiation therapy, and bone marrow transplant. With the recent explosion in -omics level data platforms and increased patient opt-ins for electronic medical record usage, we have entered a new phase of drug development that focuses on utilizing data-driven approaches to identify targeted therapeutics. Here we apply artificial intelligence and machine learning principles to publicly available datasets to identify candidates for drug repurposing. Methods: A meta-analysis was performed using more than 500 publicly available single cell RNA-seq (scRNA-seq) datasets spanning multiple species, platforms, and capture techniques. Fifteen unique sets of study data examining human or mouse-modeled malignancies of hematopoietic and lymphoid origin were identified. Data were downloaded using the most upstream format available (ie fastq > bam > mtx > h5/rdata). Mappings from fastqbam format were performed using STAR v2.5.3a, mapping from bam files to mtx were performed using Cell Ranger v3.0, and mappings from mtx files to h5 were performed using Scanpy v 1.4. Data were denoised using variational inference to account for study, species, disease, tissue, and subject-specific batch effects using scVI v0.4.1. Meta data from each study was extracted and binned by inspection into classification of ‘severe', ‘moderate', ‘mild', or ‘no disease'. Disease severity signatures were constructed accordingly using EdgeR v3.9, and paired with chemical perturbation signatures for more than 10,000 drug compounds, to identify and score therapeutic candidates. Results: A total of 588 datasets were evaluated using curations provided in Svensson et al 2019, yielding fifteen relevant human (n = 9) and mouse (n = 6) studies published in pubmed-indexed journals. These spanned 11 unique journals, the most common of which was Nature Immunology (n=3). Datasets were obtained from GEO (n = 10), ArrayExpress (n = 2), or laboratory/group-specific web servers (n = 3). Studies evaluated between 63 and 10,260 individual cells (mean 1763, median 362), across six different sequencing platforms. From the 12,442 distinct compounds profiled across 71 cell lines in the LINCS database, we identified 20 top candidates with perturbation profiles most correlated with the decreasing disease severity signatures generated through our scRNA-Seq meta-analysis. Conclusion: This represents a novel method for cancer drug discovery and repurposing using exclusively publicly available datasets. Citation Format: Bei Jiang, Michael Januszyk. An artificial intelligence based meta-analysis of publicly available single cell RNA-seq datasets for hematopoietic and lymphoid malignancies identifies repurposable cancer drug targets [abstract]. In: Proceedings of the Annual Meeting of the American Association for Cancer Research 2020; 2020 Apr 27-28 and Jun 22-24. Philadelphia (PA): AACR; Cancer Res 2020;80(16 Suppl):Abstract nr 4166A.

Frequent Mobile Electronic Medical Records Users Respond More Quickly to Emergency Department Consultation Requests: Retrospective Quantitative Study.

BackgroundSpecialty consultation is a critical aspect of emergency department (ED) practice, and a delay in providing consultation might have a significant clinical effect and worsen ED overcrowding. Although mobile electronic medical records (EMR) are being increasingly used and are known to improve the workflow of health care providers, limited studies have evaluated their effectiveness in real-life clinical scenarios.ObjectiveFor this study, we aimed to determine the association between response duration to an ED specialty consultation request and the frequency of mobile EMR use.MethodsThis retrospective study was conducted in an academic ED in Seoul, South Korea. We analyzed EMR and mobile EMR data from May 2018 to December 2018. Timestamps of ED consultation requests were retrieved from a PC-based EMR, and the response interval was calculated. Doctors’ log frequencies were obtained from the mobile EMR, and we merged data using doctors’ deidentification numbers. Pearson’s product-moment correlation was performed to identify this association. The primary outcome was the relationship between the frequency of mobile EMR usage and the time interval from ED request to consultation completion by specialty doctors. The secondary outcome was the relationship between the frequency of specialty doctors’ mobile EMR usage and the response time to consultation requests.ResultsA total of 25,454 consultations requests were made for 15,555 patients, and 252 specialty doctors provided ED specialty consultations. Of the 742 doctors who used the mobile EMR, 208 doctors used it for the specialty consultation process. After excluding the cases lacking essential information, 21,885 consultations with 208 doctors were included for analysis. According to the mobile EMR usage pattern, the average usage frequency of all users was 13.3 logs/day, and the average duration of the completion of the specialty consultation was 51.7 minutes. There was a significant inverse relationship between the frequency of mobile EMR usage and time interval from ED request to consultation completion by specialty doctors (coefficient=–0.19; 95% CI –0.32 to –0.06; P=.005). Secondary analysis with the response time was done. There was also a significant inverse relationship between the frequency of specialty doctors’ mobile EMR usage and the response time to consultation requests (coefficient=–0.18; 95% CI –0.30 to –0.04; P=.009).ConclusionsOur findings suggest that frequent mobile EMR usage is associated with quicker response time to ED consultation requests.

Factors to Overcoming Barriers Affecting Electronic Medical Record Usage by Physicians.

Background:Hospitals are adopting electronic medical records (EMRs) in larger numbers; however, the barrier to derive its full utility is the low acceptance by physicians.Aims and Objectives:This study is done with an objective to identify the factors to overcome the barriers preventing the adoption of EMR by physicians.Materials and Methods:This study is cross sectional in natures and a self-administered questionnaire is developed based on the Technology Acceptance Model.Results:The four identified factors are positive attitude toward EMR, reliability, difficulty to use, and adaptability, these factors together, have explained 62.54 percent variance in the data set.Conclusion:The physician's acceptance for EMRs can be improved by focusing on the identified four factors, which are “positive attitude toward electronic medical records,” reliability of electronic medical records,” “difficulty level of use,” and “adaptability of electronic medical records.”

116.2: Electronic hospital donor referrals have arrived.

Background: The Centers for Medicare and Medicaid Services in the United States and additional regulatory organizations require that hospitals refer all potential organ, tissue and/or eye donors to their designated Organ Procurement Organization (OPO). To replace the current time-consuming and error-prone telephonic referral process, one OPO, its software system partner, a Hospital clinical team and their Electronic Medical Record (EMR) team successfully collaborated to implement the first-ever secure direct and automatic electronic donor referral interface between a Hospital EMR system and an OPO donor management system. The goals of the interface include: (1) increase the number and timeliness of referrals (and thus transplantable organs); (2) reduce Hospital and OPO resource costs; and (3) reduce risk of error due to human verbal communication and transcription risk. Methods: The multi-disciplinary team collaboratively created a secure technical interface directly connecting the OPO and Hospital systems. The team designed seamless donor referral triggers within the Hospital EMR which expanded upon existing Best Practice Advisory (BPA) notifications that previously instructed the EMR user to call in the referral, and now automatically deliver electronic donor referrals from the Hospital to the OPO. Immediately upon receipt, the OPO’s electronic donor management system creates a new referral that is auto-populated with actionable patient information and delivers real-time notifications alerting OPO staff of the new referral. The interface automatically returns the associated OPO referral ID number to the patient’s EMR. This interface was successfully launched in the Hospital ICU in October 2018. In April 2019 (six months later), the Hospital successfully rolled out the interface to additional units and facility locations, and numerous additional interface discussions and implementations are currently underway. Results: Immediately, the monthly referral count from the Hospital ICU to the OPO quadrupled (October 2017 = 18; October 2018 = 72). Since the launch, the OPO has been able to convert on a total of 36 organ, tissue, and eye donors across the facility. The use of an existing BPA resulted in minimal change to Standard Operating Procedures and reduced the need for clinical team training. Successfully transmitted referrals contained zero (0) errors in data transmission. Higher levels of awareness of organ donation potential in the units was observed. Conclusions: The current results were successful in the following measures: 1) increased number of referrals; 2) reduction in resource cost; and 3) decreased errors. The interface was established using standard interoperability technology connecting widely-adopted OPO donation software and Hospital EMR Systems to allow for scalable expansion. There is unlimited potential for streamlining the referral process through this electronic interface, ultimately leading to more donors and transplantations.

Prioritizing Features to Redesign in an EMR System.

Redesigning Electronic Medical Record (EMR) systems is needed to improve their usefulness and usability. For user-centered redesign, designers should consider which EMR features are the most important to the users. However, prioritizing the EMR features is complicated because: (i) EMR systems involve multiple users with different, and sometimes conflicting, priorities and (ii) targeting one feature will affect other features of the EMR system. In this work, we propose a method for prioritizing the features to target when redesigning an EMR system. The method takes into consideration the different priorities of the users and the relationships between the different features. We illustrate the method through a case study on redesigning EMR systems in Japanese antenatal care settings. Our results show the importance of considering the different types of EMR users and the relationships between different EMR features. Designers could use the proposed method as a decision-aid tool in EMR redesign projects.

Understanding physicians’ adoption of electronic medical records: Healthcare technology self-efficacy, service level and risk perspectives

Most developed countries across the globe are deploying electronic medical record (EMR) as one of the most important initiatives in their healthcare policy. EMR can not only reduce the problems associated with managing paper medical records but also improve the accuracy of medical decisions made by physicians and increase the safety of patients. Considering that physicians are the primary users of EMR, their willingness to use EMR is a critical success factor for EMR implementation in a hospital. This study aims to extend an individual-level information technology adoption model by incorporating three additional variables to investigate whether the individual characteristics of a physician affect EMR adoption. A field survey is conducted with a total of 217 physicians from 15 different academic medical centers and metropolitan hospitals for six weeks. Then, the Structural Equation Modeling (SEM) analysis results indicate that perceived service level is an important antecedent of perceived usefulness. Healthcare technology self-efficacy, perceived risk, and perceived service level are also important antecedents of perceived ease of use. This study is concluded with implications for academics, hospital managers, governments, and medical information service providers.

The Usability of Electronic Medical Record Systems Implemented in Sub-Saharan Africa: A Literature Review of the Evidence.

BackgroundElectronic medical record (EMR) systems hold the exciting promise of accurate, real-time access to patient health care data and great potential to improve the quality of patient care through decision support to clinicians. This review evaluated the usability of EMR systems implemented in sub-Saharan Africa based on a usability evaluation criterion developed by the Healthcare Information and Management Systems Society (HIMSS).ObjectiveThis review aimed to evaluate EMR system implementations in sub-Saharan Africa against a well-defined evaluation methodology and assess their usability based on a defined set of metrics. In addition, the review aimed to identify the extent to which usability has been an enabling or hindering factor in the implementation of EMR systems in sub-Saharan Africa.MethodsFive key metrics for evaluating EMR system usability were developed based on the methodology proposed by HIMSS. These were efficiency, effectiveness, ease of learning, cognitive load, and user satisfaction. A 5-point rating system was developed for the review. EMR systems in 19 reviewed publications were scored based on this rating system. It awarded 5 points per metric to any EMR system that was identified as excellent, 4 points for good, 3 points for fair, 2 points for poor, and 1 point for bad. In addition, each of the 5 key metrics carried a maximum weighted score of 20. The percentage scores for each metric were then computed from the weighted scores from which the final overall usability score was derived.ResultsIn possibly contributing to the usability of implemented EMR systems, ease of learning obtained the highest percentage score of 71% (SD 1.09) followed by cognitive load in second place with a score of 68% (SD 1.62). Effectiveness followed closely in third place at 67% (SD 1.47) and efficiency was in fourth place at 64% (SD 1.04). User satisfaction came in last at 63% (SD 1.70). The overall usability score for all systems was calculated to be 66%.ConclusionsThe usability of EMR systems implemented in sub-Saharan Africa has been good with ease of learning possibly being the biggest positive contributor to this rating. Cognitive load and effectiveness have also possibly positively influenced the usability of EMR systems, whereas efficiency and user satisfaction have perhaps contributed least to positively influencing EMR system usability.

An Optimization Program to Help Practices Assess Data Quality and Workflow With Their Electronic Medical Records: Observational Study.

BackgroundElectronic medical record (EMR) adoption among Canadian primary care physicians continues to grow. In Ontario, >80% of primary care providers now use EMRs. Adopting an EMR does not guarantee better practice management or patient care; however, EMR users must understand how to effectively use it before they can realize its full benefit. OntarioMD developed an EMR Practice Enhancement Program (EPEP) to overcome challenges of clinicians and staff in finding time to learn a new technology or workflow. EPEP deploys practice consultants to work with clinicians onsite to harness their EMR toward practice management and patient care goals.ObjectiveThis paper aims to illustrate the application of the EPEP approach to address practice-level factors that impede or enhance the effective use of EMRs to support patient outcomes and population health. The secondary objective is to draw attention to the potential impact of this practice-level work to population health (system-level), as priority population health indicators are addressed by quality improvement work at the practice-level.MethodsEPEP’s team of practice consultants work with clinicians to identify gaps in their knowledge of EMR functionality, analyze workflow, review EMR data quality, and develop action plans with achievable tasks. Consultants establish baselines for data quality in key clinical indicators and EMR proficiency using OntarioMD-developed maturity assessment tools. We reassessed and compared postengagement, data quality, and maturity. Three examples illustrating the EPEP approach and results are presented to illuminate strengths, limitations, and implications for further analysis. In each example, a different consultant was responsible for engaging with the practice to conduct the EPEP method. No standard timeframe exists for an EPEP engagement, as requirements differ from practice to practice, and EPEP tailors its approach and timeframe according to the needs of the practice.ResultsAfter presenting findings of the initial data quality review, workflow, and gap analysis to the practice, consultants worked with practices to develop action plans and begin implementing recommendations. Each practice had different objectives in engaging the EPEP; here, we compared improvements across measures that were common priorities among all 3—screening (colorectal, cervical, and breast), diabetes diagnosis, and documentation of the smoking status. Consultants collected postengagement data at intervals (approximately 6, 12, and 18 months) to assess the sustainability of the changes. The postengagement assessment showed data quality improvements across several measures, and new confidence in their data enabled practices to implement more advanced functions (such as toolbars) and targeted initiatives for subpopulations of patients.ConclusionsApplying on-site support to analyze gaps in EMR knowledge and use, identify efficiencies to improve workflow, and correct data quality issues can make dramatic improvements in a practice’s EMR proficiency, allowing practices to experience greater benefit from their EMR, and consequently, improve their patient care.

Development of a Nursing Electronic Medical Record Usability Protocol

Assessing usability of an electronic medical record is useful for organizations wishing to customize their electronic medical record and determine the impact on usability. The purpose of this article is to describe the development of a protocol to measure electronic medical record usability from a nursing perspective and to develop a scoring methodology. The Technical Evaluation, Testing, and Validation of the Usability of Electronic Health Records (NISTIR 7804), published by the National Institute of Standards and Technology, guided protocol development. Usability was determined by measuring effectiveness, efficiency, and satisfaction. Satisfaction was measured by the System Usability Scale, while effectiveness and efficiency were measured via performance testing by participants, who completed one of three scenarios. A protocol and scoring formula were developed and used to assess nursing usability, resulting in an overall "C" grade for the electronic medical record. Protocol implementation and the scoring/grading calculations can be replicated to assess electronic medical record usability. The three scenarios used in this protocol will be made available upon request from the primary author to promote the use of electronic medical record usability assessment. Using and expanding upon the government's recommended usability assessment guidelines, we were successful in measuring nursing electronic medical record usability and rating an electronic medical record.

Implementation to Optimization: A Tailored, Data-Driven Approach to Improve Provider Efficiency and Confidence in Use of the Electronic Medical Record.

Nine months after the implementation of a new electronic medical record (EMR) system at a single institution, physicians (MDs), nurse practitioners (NPs), and physician assistants (PAs) expressed frustration with its use. We aimed to test if an individually tailored training approach reduced time spent with the EMR and increased confidence. Two hours of training were conducted in a one-on-one manner with a trainer. Content was individualized according to the following: provider survey, EMR utilization profile, and shadowing in clinic. Surveys assessed confidence before training and immediately after training. Changes in time spent in various EMR activities before training and after training were compared. Three trainers delivered one-on-one training to 133 MDs, 42 NPs, and 10 PAs who specialized in medical oncology. Participants reported an increase in confidence across all activities, and almost all providers (98%) who responded to our survey agreed that the training enhanced their efficiency. A non-statistically significant trend toward reduction in the overall time in the system was observed. Time in system was reduced primarily in activities such as documentation and ordering of laboratory tests, imaging, medications, and chemotherapy. A personalized and data-driven training approach was highly regarded by providers. EMR usage reports provided extensive data to identify and prioritize training content and were valuable to measure the impact of training on provider time in system. With the growth of EMR implementation and the reported relationship of EMR use to burnout, continuous and personalized training after EMR implementation is effective to reduce the time in system and increase confidence.

Secondary use of electronic medical records for clinical research: Challenges and Opportunities.

With increasingly ubiquitous electronic medical record (EMR) implementation accelerated by the adoption of the HITECH Act, there is much interest in the secondary use of collected data to improve outcomes and promote personalized medicine. A plethora of research has emerged using EMRs to investigate clinical research questions and assess variations in both treatments and outcomes. However, whether because of genuine complexities of modeling disease physiology or because of practical problems regarding data capture, data accuracy, and data completeness, the state of current EMR research is challenging and gives rise to concerns regarding study accuracy and reproducibility. This work explores challenges in how different experimental design decisions can influence results using a specific example of breast cancer patients undergoing excision and reconstruction surgeries from EMRs in an academic hospital and the Veterans Health Administration (VHA) We discuss emerging strategies that will mitigate these limitations, including data sharing, application of natural language processing, and improved EMR user design.

Towards optimal electronic medical record use: perspectives of advanced users.

While primary health care electronic medical record (EMR) adoption has increased in Canada, the use of advanced EMR features is limited. Realizing the potential benefits of primary health care EMR use is dependent not only on EMR acquisition, but also on its comprehensive use and integration into practice; yet, little is known about the advanced use of EMRs in primary health care. To explore the views of advanced primary health care EMR users practising in a team-based environment. A descriptive qualitative approach was used to explore the views of primary health care practitioners who were identified as advanced EMR users. Twelve individual semi-structured interviews were held with primary health care practitioners in Southwestern Ontario, Canada. Field notes were created after each interview. Interviews were audio recorded and transcribed verbatim. Researchers independently coded the transcripts and then met to discuss the results of the coding. We used a thematic approach to data analysis. Three themes emerged from the data analysis: advanced EMR users as individuals with signature characteristics, advanced EMR users as visionaries and advanced EMR users as agents of change. In any one participant, these elements could overlap, illuminating the important interplay between these themes. Taken together, these themes defined advanced use among this group of primary health care practitioners. To realize the potential benefits of EMR use in improved patient care and outcomes, we need to understand how to support EMR use. This study provides a necessary building block in furthering this understanding.

Can ‘meso-tier’ healthcare organizations enhance health system performance? - lessons from US Accountable Care Organizations (ACOs) and a discussion of the implications for Australia

Background: USA: ACOs are becoming a prominent feature of the US healthcare system. ACOs are groups of physicians, hospitals, and other providers that agree to be held financially responsible for the total cost and quality of care for a defined patient population. There are currently over 800 ACOs covering around 28 million people. With new Medicare reforms coming into effect soon, the ACO sector may cover over 100 million people in the next five years. Despite this rapid growth, outcomes have been mixed to date and there is little knowledge of how capable these organizations are in promoting delivery system changes. Australia: Like in the US, the Australian healthcare system has historically been dominated by volume-driven, fee-for-service models and overall costs are rising. New models of primary and integrated care are evolving with bundled primary care payments for patients with chronic and complex care needs currently being implemented nationally. Meso-level primary health networks and local hospital networks are leading the implementation of these models and there may be important lessons from the US ACO experience that could inform policy in this area. Methods: Using data from a national survey and Medicare, we reviewed structures, capabilities and performance outcomes for 399 ACOs in the US from 2012 to 2015. Results: ACOs with commercial insurer contracts were significantly larger, more integrated with hospitals, and had lower benchmark expenditures and higher quality scores, compared to ACOs participating only in public (Medicare and Medicaid) payer programs. For most ACOs, there was a low uptake of quality and efficiency activities, although generally commercial ACOs reported higher rates of electronic medical record usage, disease monitoring tools, patient satisfaction data, and quality improvement methods. Few ACOs reported having high-level performance and financial monitoring capabilities. Although ACOs were making modest savings overall, there was wide variation. A small number of ACOs accounted for the majority of total savings accrued and around one third had exceeded their expenditure benchmarks thus incurring a loss. There was no correlation between total savings and quality scores. Implications for the US: ACOs are at diverse stages of development and this is possibly an important driver of the wide performance variations currently being observed. ACOs may be using public programs as a testing ground to build their capacity to succeed in risk-based contracting. Policy initiatives to support ACOs in establishing essential infrastructure could make a major contribution to supporting delivery system changes. Implications for Australia: The US findings underscore the importance of building core capabilities within meso-tier organizations to bring about system change. Factors such as building integrated health information systems, embedding quality improvement processes within health services, and robust financial and performance monitoring systems are likely to be key drivers of success. State and federal governments ought to identify those organizations that lack sufficient capacity in these areas and intervene early with appropriate supports.

Nurses’ perceptions about and confidence in using an electronic medical record system

Introduction: Nurses are among the largest potential users of electronic medical record (EMR) systems in health care settings. Yet little is known about their perceptions and confidence toward using such systems. This study explored nurses’ perceptions toward and confidence in using the EMR system. Predictors for confidence status in using the system among nurses were postulated. Methods: A cross-sectional survey design was used. A sample of 169 nurses were recruited from a general governmental university hospital in Muscat, Oman. Results: Most of study participants did not have prior experience with EMR systems elsewhere. About half (52.1%) perceived that they were confident in using the system. A logistic regression model showed nurses who (a) had six or more years of experience in using the system, (b) perceived that their suggestions regarding improving the system were taken into consideration by the system managing team, (c) perceived that the changes introduced in the system were important to their work, and (d) perceived that the information retrieved through the system was updated, to be more likely confident in using the system. Discussion: When customizing the EMR system, the informatics team that manages the system is invited to more consider suggestions for improvement that are raised by nurses. More training on the system is suggested to increase confidence among nurses who had little experience in using the system. In order to enhance the preparation of future nurses with contemporary technology-driven health care practices, nursing schools officials are encouraged to include general computer information technology training into nursing curricula.

Factors affecting the utilisation of electronic medical records system in Malawian central hospitals.

In Malawi, paper-based medical record-keeping has been observed to exacerbate challenges related to accessing patient records and patient tracking. Despite the introduction of electronic medical record (EMR) systems in 2001, paper-based records continue to be in use. Some health workers prefer paper-based records to EMRs. This study assessed factors that affect the use of EMRs in Malawi, particularly at Queen Elizabeth and Kamuzu Central Hospitals. It further investigated the reasons why paper-based records are still in use despite the numerous associated disadvantages. The extent to which EMRs contribute to patient care was also analysed. In this cross-sectional study, 111 randomly selected health workers were interviewed, using a semistructured questionnaire, at the 2 largest central hospitals in Malawi, where EMRs were first introduced in the country. Focus group discussions were conducted to gather further information on factors identified during the individual interviews. Differences in age, gender, and previous computer experience were not associated with differences in EMR usage. However, education and employment levels has a positive association with EMR usage. Hardware and connectivity problems, as well as lack of training and managerial support negatively affected the use of EMRs. EMRs were found to improve data quality and efficiency in patient management.

Applying STOPP Guidelines in Primary Care Through Electronic Medical Record Decision Support: Randomized Control Trial Highlighting the Importance of Data Quality.

BackgroundPotentially Inappropriate Prescriptions (PIPs) are a common cause of morbidity, particularly in the elderly.ObjectiveWe sought to understand how the Screening Tool of Older People’s Prescriptions (STOPP) prescribing criteria, implemented in a routinely used primary care Electronic Medical Record (EMR), could impact PIP rates in community (non-academic) primary care practices.MethodsWe conducted a mixed-method, pragmatic, cluster, randomized control trial in research naïve primary care practices. Phase 1: In the randomized controlled trial, 40 fully automated STOPP rules were implemented as EMR alerts during a 16-week intervention period. The control group did not receive the 40 STOPP rules (but received other alerts). Participants were recruited through the OSCAR EMR user group mailing list and in person at user group meetings. Results were assessed by querying EMR data PIPs. EMR data quality probes were included. Phase 2: physicians were invited to participate in 1-hour semi-structured interviews to discuss the results.ResultsIn the EMR, 40 STOPP rules were successfully implemented. Phase 1: A total of 28 physicians from 8 practices were recruited (16 in intervention and 12 in control groups). The calculated PIP rate was 2.6% (138/5308) (control) and 4.11% (768/18,668) (intervention) at baseline. No change in PIPs was observed through the intervention (P=.80). Data quality probes generally showed low use of problem list and medication list. Phase 2: A total of 5 physicians participated. All the participants felt that they were aware of the alerts but commented on workflow and presentation challenges.ConclusionsThe calculated PIP rate was markedly less than the expected rate found in literature (2.6% and 4.0% vs 20% in literature). Data quality probes highlighted issues related to completeness of data in areas of the EMR used for PIP reporting and by the decision support such as problem and medication lists. Users also highlighted areas for better integration of STOPP guidelines with prescribing workflows. Many of the STOPP criteria can be implemented in EMRs using simple logic. However, data quality in EMRs continues to be a challenge and was a limiting step in the effectiveness of the decision support in this study. This is important as decision makers continue to fund implementation and adoption of EMRs with the expectation of the use of advanced tools (such as decision support) without ongoing review of data quality and improvement.Trial RegistrationClinicaltrials.gov NCT02130895; https://clinicaltrials.gov/ct2/show/NCT02130895 (Archived by WebCite at http://www.webcitation.org/6qyFigSYT)