Progress in the Enhanced Use of Electronic Medical Records: Data From the Ontario Experience

An Approach to Potentially Increasing Adoption of an Artificial Intelligence-Enabled Electronic Medical Record Encounter in Canadian Primary Care: Protocol for a User-Centered Design

BACKGROUND

Primary care physicians are at the forefront of the clinical process that can lead to diagnosis, referral, and treatment. With electronic medical records (EMRs) being introduced and, over time, gaining acceptance by primary care users, they have now become a standard part of care. EMRs have the potential to be further optimized with the introduction of artificial intelligence (AI). There has yet to be a widespread exploration of the use of AI in primary health care and how clinicians envision AI use to encourage further uptake.

OBJECTIVE

The primary objective of this research is to understand if the user-centered design approach, rooted in contextual design, can lead to an increased likelihood of adoption of an AI-enabled encounter module embedded in a primary care EMR. In this study, we use human factor models and the technology acceptance model to understand the results.

METHODS

To accomplish this, a partnership has been established with an industry partner, TELUS Health, to use their EMR, the collaborative health record. The overall intention is to understand how to improve the user experience by using user-centered design to inform how AI should be embedded in an EMR encounter. Given this intention, a user-centered approach will be used to accomplish it. The approach of user-centered design requires qualitative interviewing to gain a clear understanding of users' approaches, intentions, and other key insights to inform the design process. A total of 5 phases have been designed for this study.

RESULTS

As of March 2024, a total of 14 primary care clinician participants have been recruited and interviewed. First-cycle coding of all qualitative data results is being conducted to inform redesign considerations.

CONCLUSIONS

Some limitations need to be acknowledged related to the approach of this study. There is a lack of market maturity of AI-enabled EMR encounters in primary care, requiring research to take place through scenario-based interviews. However, this participant group will still help inform design considerations for this tool. This study is targeted for completion in the late fall of 2024.

INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID)

DERR1-10.2196/54365.

A shifting terrain: Understanding the perspectives of walk-in physicians on their roles amid worsening primary care access in Ontario, Canada

BACKGROUND

High-quality primary care is associated with better health outcomes and more efficient and equitable health system performance. However, the rate of primary care attachment is falling, and timely access to primary care is worsening, driving many patients to use walk-in clinics for their comprehensive primary care needs. This study sought to explore the experiences and perceived roles and responsibilities of walk-in physicians in this current climate. Methods: Qualitative interviews were conducted with nineteen physicians currently providing walk-in care in Ontario, Canada between May and December 2022.

RESULTS

Limited capacity for continuity and comprehensiveness of care were identified as major sources of professional tension for walk-in physicians. Divergent perspectives on their roles were anchored in how physicians viewed their professional identity. Some saw providing continuous and comprehensive care as an infringement on their professional role; others saw their professional role as more flexible and responsive to population needs. Regardless of their professional identity, participants reported feeling ill-equipped to manage the swell of unattached patients, citing a lack of time, resources, connectivity to the system, and remuneration flexibility. Conclusions: As practice demands of walk-in clinics change, an evolution in the professional roles and responsibilities of walk-in physicians follows. However, the resources, structure, and incentives of walk-in care have not evolved to reflect this, leaving physicians to set their own professional boundaries with patients. This results in increasing variations in care and confusion across the primary care sector around who is responsible for what, when, and how.

Exploring maturity of electronic medical record use among allied health professionals

BACKGROUND

Electronic medical records (EMRs) have the potential to improve and streamline the quality and safety of patient care. Harnessing the full benefits of EMR implementation depends on the utilisation of advanced features, defined as "mature usage." At present, little is known about the maturity of EMR usage by allied health professionals (AHPs).

OBJECTIVE

To examine current maturity of EMR use by AHPs and explore perceived barriers to mature EMR utilisation and optimisation.

METHOD

AHPs were recruited from three health services. Participants completed a 27-question electronic questionnaire based on the EMR Adoption Framework, which measures clinician EMR utilisation (0 = paper chart, 5 = theoretical maximum) across 10 EMR feature categories. Interviews were conducted with both clinicians and managers to explore the nature of current EMR utilisation and perceived facilitators and barriers to mature usage.

RESULTS

Questionnaire responses were obtained from 193 participants AHPs. The majority of questions (74%) showed a mean score of <3, indicating a lack of mature EMR use. Pockets of mature usage were identified in the categories of health information, referrals and administration processes. Interviews with 21 clinicians and managers revealed barriers to optimisation across three themes: (1) limited understanding of EMR opportunities; (2) complexity of the EMR change process and (3) end-user and environmental factors.

CONCLUSION

Mature usage across EMR feature categories of the EMR Adoption Framework was low. However, questionnaire and qualitative interview data suggested pockets of mature utilisation.

IMPLICATIONS

Achieving mature allied health EMR use will require strategies implemented at the clinician, EMR support, and service levels.

Economic evaluation and analyses of hospital-based electronic medical records (EMRs): a scoping review of international literature

Digital transformation is expensive and rarely smooth, often leading to higher costs than anticipated. It is challenging to demonstrate the contribution of digital health investment in achieving the healthcare aims of population health and workforce sustainability. We conducted a scoping review to understand how electronic medical record (EMR) implementations in the hospital setting have been evaluated using cost-benefit analysis (CBA) approaches. The review search resulted in 1184 unique articles, a final list of 28 was collated of which 20 were US-based studies. All studies were published in 2010-2019, with fewer studies published in more recent years. The data used to estimate benefits and costs were dated from 1996 to 2016, with most data from 2000 to 2010. Only three studies were qualified as using cost-benefit analysis approaches. While studies indicated that there is a positive impact from the EMR implementation, the impacts measured varied greatly. We concluded that the current literature demonstrates a lack of appropriate and comprehensive economic frameworks to understand the value of digital hospital implementations. Additionally, most studies failed to align fully to the quadruple aims of healthcare: they focused either on cost savings and/or improved patient outcomes and population health, none investigated healthcare-workforce sustainability.

The current and potential uses of Electronic Medical Record (EMR) data for primary health care performance measurement in the Canadian context: a qualitative analysis

BACKGROUND

Electronic Medical Records (EMRs) are a rich data source to measure and improve quality of care. As Canadian primary health care (PHC) EMRs mature, there is increasing potential use of EMR data for performance measurement. This study identifies and describes current uses of EMR data for performance measurement and considerations to further its potential in the Canadian context.

METHODS

We applied a qualitative case study design and descriptive assessment in three phases, consulting multiple data sources including scientific and grey literature, system leaders (n = 41), and clinician/researchers (n = 20). Phases included a multimethod approach to identify initiatives using EMR data for performance measurement across Canadian jurisdictions; in-depth review of current initiatives identified from a healthcare performance intelligence lens; and triangulation and thematic analysis across data sources to explore considerations for advancing performance measurement uses of EMR data in the Canadian context.

RESULTS

Six initiatives of EMR data use for performance measurement were identified: one multi-jurisdictional; five jurisdiction-specific in the provinces of British Columbia, Manitoba and Ontario. EMR data uses were predominately for micro-level PHC physician and team performance improvement, with some use for meso-level organization/network-wide improvement. Indicator sets varied in number, though shared emphasis on chronic disease management and prevention/screening and to a lesser extent medication management. Key considerations for governing, resourcing and implementing EMR data for performance measurement were identified.

CONCLUSIONS

The extent of EMR data use for performance measurement varies across Canada. To further its potential, pan-Canadian data and privacy standards, performance intelligence competencies and renewed core PHC indicators should be prioritized. Experiences across countries, coupled with increasing momentum for performance measurement using real-world data, should be leveraged to avoid unnecessarily slow progress in Canada and abroad.

A model to measure self-assessed proficiency in electronic medical records: Validation using maturity survey data from Canadian community-based physicians

OBJECTIVE

Adoption of electronic medical records (EMRs) does not necessarily translate to proficiency -referred to here as EMR maturity. To realize the full benefit of wide scale EMR adoption, the focus must shift from adoption to advancing mature use. This calls for validated assessment models so that researchers, health system planners and digital health developers can better understand what contributes to maturity among physicians. This research aims to validate a measurement model for self-assessed EMR maturity among community-based physicians.

METHODS

As part of an Ontario government-funded EMR adoption program, the EMR Maturity Model for community-based practices was adapted from a hospital-based EMR maturity model. A survey instrument was developed on the foundation of the new model and revised by experts and stakeholders. Content validity, face validity and user acceptance were established before survey administration. Internal consistency and construct validity of the model were tested after survey data were collected. Finally, physicians' comments collected via the survey were qualitatively analyzed to provide additional insights that can be applied to refinement of the model and survey.

RESULTS

As of August 1, 2019, 1588 physicians completed the survey. Ordinal alpha tests for reliability and content validity yielded an alpha value of 0.86 across all key measures specifically associated with maturity. Among most of these, there was a pattern of weak to moderate significant (p < .0001) positive Spearman inter-correlations. One factor was extracted for items measuring dimensions of maturity and all factor loadings of the key measures were greater than 0.40. The fit of the one-factor model was moderately adequate. This indicates the model is valid and reliable, with consistency across key measures for measuring one factor: maturity.

CONCLUSIONS

This is the first known validated model published in English that measures EMR maturity among community-based physicians. While the model is shown to be valid and reliable statistically and qualitative analysis supports this, there is room for improvement. Both the statistical analysis and portions of the qualitative analysis suggest areas of exploration to strengthen the model and survey. Future efforts will include refining the survey to improve user interface and accrue further data, as the sample to date is insufficient for generalizability.

Development of an Online Health Care Assessment for Preventive Medicine: A Machine Learning Approach

BACKGROUND

In the era of information explosion, the use of the internet to assist with clinical practice and diagnosis has become a cutting-edge area of research. The application of medical informatics allows patients to be aware of their clinical conditions, which may contribute toward the prevention of several chronic diseases and disorders.

OBJECTIVE

In this study, we applied machine learning techniques to construct a medical database system from electronic medical records (EMRs) of subjects who have undergone health examination. This system aims to provide online self-health evaluation to clinicians and patients worldwide, enabling personalized health and preventive health.

METHODS

We built a medical database system based on the literature, and data preprocessing and cleaning were performed for the database. We utilized both supervised and unsupervised machine learning technology to analyze the EMR data to establish prediction models. The models with EMR databases were then applied to the internet platform.

RESULTS

The validation data were used to validate the online diagnosis prediction system. The accuracy of the prediction model for metabolic syndrome reached 91%, and the area under the receiver operating characteristic (ROC) curve was 0.904 in this system. For chronic kidney disease, the prediction accuracy of the model reached 94.7%, and the area under the ROC curve (AUC) was 0.982. In addition, the system also provided disease diagnosis visualization via clustering, allowing users to check their outcome compared with those in the medical database, enabling increased awareness for a healthier lifestyle.

CONCLUSIONS

Our web-based health care machine learning system allowed users to access online diagnosis predictions and provided a health examination report. Users could understand and review their health status accordingly. In the future, we aim to connect hospitals worldwide with our platform, so that health care practitioners can make diagnoses or provide patient education to remote patients. This platform can increase the value of preventive medicine and telemedicine.

Primary Care Physicians' Experience Using Advanced Electronic Medical Record Features to Support Chronic Disease Prevention and Management: Qualitative Study

Background

Chronic diseases are the leading cause of death worldwide. In Canada, more than half of all health care spending is used for managing chronic diseases. Although studies have shown that the use of advanced features of electronic medical record (EMR) systems improves the quality of chronic disease prevention and management (CDPM), a 2012 international survey found that Canadian physicians were the least likely to use 2 or more EMR system functions. Some studies show that maturity vis-à-vis clinicians’ EMR use is an important factor when evaluating the use of advanced features of health information systems. The Clinical Adoption Framework (CAF), a common evaluation framework used to assess the success of EMR adoption, does not incorporate the process of maturing. Nevertheless, the CAF and studies that discuss the barriers to and facilitators of the adoption of EMR systems can be the basis for exploring the use of advanced EMR features.

Objective

This study aimed to explore the factors that primary care physicians in Ontario identified as influencing their use of advanced EMR features to support CDPM and to extend the CAF to include primary care physicians’ perceptions of how their use of EMRs for performing clinical tasks has matured.

Methods

Guided by the CAF, directed content analysis was used to explore the barriers and facilitating factors encountered by primary care physicians when using EMR features. Participants were primary care physicians in Ontario, Canada, who use EMRs. Data were coded using categories from the CAF.

Results

A total of 9 face-to-face interviews were conducted from January 2017 to July 2017. Dimensions from the CAF emerged from the data, and one new dimension was derived: physicians’ perception of their maturity of EMR use. Primary care physicians identified the following key factors that impacted their use of advanced EMR features: performance of EMR features, information quality of EMR features, training and technical support, user satisfaction, provider’s productivity, personal characteristics and roles, cost benefits of EMR features, EMR systems infrastructure, funding, and government leadership.

Conclusions

The CAF was extended to include physicians’ perceptions of how their use of EMR systems had matured. Most participants agreed that their use of EMR systems for performing clinical tasks had evolved since their adoption of the system and that certain system features facilitated their care for patients with chronic diseases. However, several barriers were identified and should be addressed to further enhance primary care physicians’ use of advanced EMR features to support CDPM.

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

Background

Electronic 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.

Objective

This 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.

Methods

EPEP’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.

Results

After 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.

Conclusions

Applying 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.

Modeling and Predicting Outcomes of eHealth Usage by European Physicians: Multidimensional Approach from a Survey of 9196 General Practitioners

Background

The literature has noted the need to use more advanced methods and models to evaluate physicians’ outcomes in the shared health care model that electronic health (eHealth) proposes.

Objective

The goal of our study was to design and evaluate a predictive multidimensional model of the outcomes of eHealth usage by European physicians.

Methods

We used 2012-2013 survey data from a sample of 9196 European physicians (general practitioners). We proposed and tested two composite indicators of eHealth usage outcomes (internal practices and practices with patients) through 2-stage structural equation modeling. Logistic regression (odds ratios, ORs) to model the predictors of eHealth usage outcomes indicators were also calculated.

Results

European general practitioners who were female (internal practices OR 1.15, 95% CI 1.10-1.20; practices with patients OR 1.19, 95% CI 1.14-1.24) and younger—aged <35 years (internal practices OR 1.14, 95% CI 1.02-1.26; practices with patients OR 1.32, 95% CI 1.13-1.54) and aged 36-45 years (internal practices OR 1.16, 95% CI 1.06-1.28; practices with patients OR 1.21, 95% CI 1.10-1.33)—had a greater propensity toward favorable eHealth usage outcomes in internal practices and practices with patients. European general practitioners who positively valued information and communication technology (ICT) impact on their personal working processes (internal practices OR 5.30, 95% CI 4.73-5.93; practices with patients OR 4.83, 95% CI 4.32-5.40), teamwork processes (internal practices OR 4.19, 95% CI 3.78-4.65; practices with patients OR 3.38, 95% CI 3.05-3.74), and the doctor-patient relationship (internal practices OR 3.97, 95% CI 3.60-4.37; practices with patients OR 6.02, 95% CI 5.43-6.67) had a high propensity toward favorable effects of eHealth usage on internal practices and practices with patients. More favorable eHealth outcomes were also observed for self-employed European general practitioners (internal practices OR 1.33, 95% CI 1.22-1.45; practices with patients OR 1.10, 95% CI 1.03-1.28). Finally, general practitioners who reported that the number of patients treated in the last 2 years had remained constant (internal practices OR 1.08, 95% CI 1.01-1.17) or increased (practices with patients OR 1.12, 95% CI 1.03-1.22) had a higher propensity toward favorable eHealth usage outcomes.

Conclusions

We provide new evidence of predictors (sociodemographic issues, attitudes toward ICT impacts, and working conditions) that explain favorable eHealth usage outcomes. The results highlight the need to develop more specific policies for eHealth usage to address different realities.

Development of a techno-humanist model for e-health adoption of innovative technology

BACKGROUND AND PURPOSE

After investing billions of dollars in an integrated Electronic Medical Records (physicians) and Personal Health Records (patients) system to allow both parties to manage and communicate through e-health innovative technologies, Canada is still making slow adoption progress. In an attempt to bridge the human and technological perspectives by developing and testing a holistic model, this study purports to predict patients' behavioral intentions to use e-health applications.

METHODS

An interdisciplinary approach labelled as a techno-humanism model (THM) is testing twelve constructs identified from the technological, sociological, psychological, and organizational research literature and deemed to have a significant effect upon and positive relationship with patients' e-health applications adoption. Subjects were Canadians recruited in a mall-intercept mode from a region representing a demographically diverse population, including rural and urban residents. The SmartPLS measurement tool was used to evaluate the reliability and validity of study constructs. The twelve constructs were separately tested with quantitative data such as factor analysis, single, multiple, and hierarchical multiple regression.

RESULTS

The hierarchical multiple regression analysis process led us to formulate four models, each hinged on a combination of interdisciplinary variables. Model 1 consisted of the technological predictors and explained 62.3% (p < .001) of variance in the behavioral intention to use e-health. Model 2 added the sociological predictors to the equation and explained 72.3% (p < .001) of variance. Model 3 added the psychological predictors to Model 2 and explained 72.8% (p < .001). Finally, Model 4 included all twelve predictors and explained 73% (p < .001) of variance in the behavioral intention to use e-health applications.

CONCLUSIONS

One of the greatest barriers to applying e-health records in Canada resides in the lack of coordination among stakeholders. The present study implies that healthcare policy makers must consider the twelve variables with their findings and implications as a whole. The techno-humanist model (THM) we are proposing is a more holistic and continuous approach. It pushes back to a breakdown of the various technological, sociological, psychological, and managerial factors and stakeholders that are at the root cause of behavioral intentions to use e-health, as opposed to merely observing behavioral outcomes at the end of the "assembly line". Active participation and coordination of all stakeholders is a key feature.

Precision medicine in COPD: where are we and where do we need to go?

Chronic obstructive pulmonary disease (COPD) was the fourth leading cause of death worldwide in 2015. Current treatments for patients ease discomfort and help decrease disease progression; however, none improve lung function or change mortality. COPD is heterogeneous in its molecular and clinical presentation, making it difficult to understand disease aetiology and define robust therapeutic strategies. Given the complexity of the disease we propose a precision medicine approach to understanding and better treating COPD. It is possible that multiOMICs can be used as a tool to integrate data from multiple fields. Moreover, analysis of electronic medical records could aid in the treatment of patients and in the predictions of outcomes. The Precision Medicine Initiative created in 2015 has made precision medicine approaches to treat disease a reality; one of these diseases being COPD.