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Canada has achieved near-universal adoption of electronic health records (EHRs) and yet interoperability, the secure exchange and use of health data across different systems and settings, remains limited. We aimed to describe the current state of EHRs in 10 provincial and 3 territorial jurisdictions in Canada and evaluate the maturity of their interoperability using a structured interoperability assessment model. We conducted an environmental scan of EHR use and interoperability across all provinces and territories using Canada Health Infoway documents and structured interviews with 23 subject matter experts. Using a rigorously designed interoperability maturity model, we evaluated jurisdictions across 4 enabler dimensions (governance, legislation and standards, incentives and capacity-building, and technical infrastructure) and 4 interoperability status dimensions (community EHRs, hospital EHRs, patient portals, and system analytics). We found that, although EHR adoption was high, maturity of EHR interoperability was low and uneven across Canada. Integrated EHR health data exchange was limited, and nearly all jurisdictions lacked EHR interoperability between hospitals, community specialists, and primary care. Data exchange between primary care and specialists, and between hospitals and community settings, was heavily dependent on fax (traditional or online) or mailed letters in every jurisdiction. Patient portal contents and system-level analytics using EHR data were underdeveloped nationally. No jurisdiction was advanced in all dimensions. Although most jurisdictions showed strength in at least 1 area, they also exhibited many areas for growth. We identified 8 key barriers to interoperability, each of which can be overcome. Canada has widespread EHR adoption, but maturity of EHR interoperability and the enabling conditions required for true interoperability are low and inconsistent across jurisdictions. Strengthening governance, legislation, standards, incentives, and technical infrastructure — supported by national legislation to mandate interoperability across different EHRs — will be essential to advancing connected care across Canada and realizing widespread benefits for patients, clinicians, and health systems.

AbstractObjectiveTo determine whether electronic health record alerts for acute kidney injury would improve patient outcomes of mortality, dialysis, and progression of acute kidney injury.DesignDouble blinded, multicenter, parallel, randomized controlled trial.SettingSix hospitals (four teaching and two non-teaching) in the Yale New Haven Health System in Connecticut and Rhode Island, US, ranging from small community hospitals to large tertiary care centers.Participants6030 adult inpatients with acute kidney injury, as defined by the Kidney Disease: Improving Global Outcomes (KDIGO) creatinine criteria.InterventionsAn electronic health record based “pop-up” alert for acute kidney injury with an associated acute kidney injury order set upon provider opening of the patient’s medical record.Main outcome measuresA composite of progression of acute kidney injury, receipt of dialysis, or death within 14 days of randomization. Prespecified secondary outcomes included outcomes at each hospital and frequency of various care practices for acute kidney injury.Results6030 patients were randomized over 22 months. The primary outcome occurred in 653 (21.3%) of 3059 patients with an alert and in 622 (20.9%) of 2971 patients receiving usual care (relative risk 1.02, 95% confidence interval 0.93 to 1.13, P=0.67). Analysis by each hospital showed worse outcomes in the two non-teaching hospitals (n=765, 13%), where alerts were associated with a higher risk of the primary outcome (relative risk 1.49, 95% confidence interval 1.12 to 1.98, P=0.006). More deaths occurred at these centers (15.6% in the alert group v 8.6% in the usual care group, P=0.003). Certain acute kidney injury care practices were increased in the alert group but did not appear to mediate these outcomes.ConclusionsAlerts did not reduce the risk of our primary outcome among patients in hospital with acute kidney injury. The heterogeneity of effect across clinical centers should lead to a re-evaluation of existing alerting systems for acute kidney injury.Trial registrationClinicalTrials.gov NCT02753751.

Big data, coupled with the use of advanced analytical approaches, such as artificial intelligence (AI), have the potential to improve medical outcomes and population health. Data that are routinely generated from, for example, electronic medical records and smart devices have become progressively easier and cheaper to collect, process, and analyze. In recent decades, this has prompted a substantial increase in biomedical research efforts outside traditional clinical trial settings. Despite the apparent enthusiasm of researchers, funders, and the media, evidence is scarce for successful implementation of products, algorithms, and services arising that make a real difference to clinical care. This article collection provides concrete examples of how “big data” can be used to advance healthcare and discusses some of the limitations and challenges encountered with this type of research. It primarily focuses on real-world data, such as electronic medical records and genomic medicine, considers new developments in AI and digital health, and discusses ethical considerations and issues related to data sharing. Overall, we remain positive that big data studies and associated new technologies will continue to guide novel, exciting research that will ultimately improve healthcare and medicine—but we are also realistic that concerns remain about privacy, equity, security, and benefit to all.

Mobile health (mHealth) apps are increasingly being used to capture patient health data, provide information, and guide self-management, with reported improvements in health care service delivery and outcomes. However, the impact of integrating mHealth app data into electronic medical record or electronic health record (EMR/EHR) systems remains underexplored. This study aims to identify what is known about the impact of integrating mHealth app data into EMR/EHR systems on health care delivery and patient outcomes. A scoping review was conducted to identify original studies that investigated the integration of patient-facing mHealth app data into EMR/EHR systems and the impact on health care outcomes. The PubMed, Embase, Web of Science, Cochrane Library, CINAHL, ProQuest, and PsycINFO databases were searched for papers published between January 2014 and July 2024. Two authors independently screened and extracted data on study characteristics, mHealth app features, details of integration with EMR/EHR systems, and effects on health care delivery and patient outcomes. Nineteen studies with 113,135 participants were included. Among these, 6 were randomized clinical trial studies, 8 were conducted in the United States, 12 occurred in hospital settings, 15 involved adult participants, and 6 targeted diabetes management. Main features of the apps and EMR/EHR systems can be categorized into tracking or recording health data (n=19), app data integrated into EMR/EHR systems (n=19), app data summarized or presented on EMR/EHR interface (n=19), communication with the health care team (n=12), reminders or alerts (n=10), synchronization with other apps or devices (n=8), educational information (n=4), and using existing portal credentials to app access (n=2). Most studies reported benefits of integrating the app and EMR/EHR, such as enhanced patient education and self-management (n=5), real-time data recorded and shared with clinicians (n=4), support for clinical decision-making (n=3), improved communication between patients and clinicians (n=7), and improved patient outcomes (n=13). Challenges identified included high drop-off rates in app usage (n=3), limited accessibility due to device restrictions (n=3), incompatibility between mHealth apps and EMR/EHR systems (n=3), increased clinical workload in response to additional information (n=3), data accuracy issues due to network connectivity (n=1), and data security concerns (n=1). Evidence suggests that the effective integration of mHealth app data into EMR/EHR systems can enhance both clinicians' health care delivery and patients' health outcomes. However, current literature is limited, and future opportunities remain to examine the impact on long-term outcomes, such as mortality, readmissions, and costs, and assess the scalability and sustainability of integration among more broader health conditions and disabilities across diverse health care settings.

Objective: To propose a unifying set of definitions for prescription adherence research utilizing electronic health record prescribing databases, prescription dispensing databases, and pharmacy claims databases and to provide a conceptual framework to operationalize these definitions consistently across studies. Methods: We reviewed recent literature to identify definitions in electronic database studies of prescription-filling patterns for chronic oral medications. We then develop a conceptual model and propose standardized terminology and definitions to describe prescription-filling behavior from electronic databases. Results: The conceptual model we propose defines 2 separate constructs: medication adherence and persistence. We define primary and secondary adherence as distinct subtypes of adherence. Metrics for estimating secondary adherence are discussed and critiqued, including a newer metric (New Prescription Medication Gap measure) that enables estimation of both primary and secondary adherence. Discussion: Terminology currently used in prescription adherence research employing electronic databases lacks consistency. We propose a. clear, consistent, broadly applicable conceptual model and terminology for such studies. The model and definitions facilitate research utilizing electronic medication prescribing, dispensing, and/or claims databases and encompasses the entire continuum of prescription-filling behavior. Conclusion: Employing conceptually clear and consistent terminology to define medication adherence and persistence will facilitate future comparative effectiveness research and meta-analytic studies that utilize electronic prescription and dispensing records.

Patients' access to their electronic health record (EHR) supports their participation and satisfaction with care. Despite the benefits, some patients have been upset after reading their EHR. Additionally, health care professionals are concerned that patients, particularly those with mental health conditions, may be offended, and they have expressed a need for further guidelines on how to write EHRs. Experiences among various patient groups are essential to support the relationship between patients and professionals. However, prior studies have often focused on single patient groups or specific clinical contexts, leaving a limited understanding of differences across multiple patient groups. This study aimed to determine whether certain patient groups are more likely to feel offended while reading their EHRs and which information is perceived as offensive and to provide a comparison across multiple patient groups using a mixed methods approach. A cross-sectional survey was conducted via the Finnish national patient portal using a web-based patient survey, adopting a mixed methods approach. The survey included multiple-choice and open-ended questions. The total sample comprised 4681 respondents. The survey respondents were placed into 4 patient groups: those who had received care for mental health, cancer, or other conditions and those who had received no care. Associations between the type of care and patients who felt offended were estimated using multivariate binary logistic regression. Inductive content analysis (n=502) was conducted to identify information perceived as offensive in the EHR. The patients who had received mental health care (166/654, 25.4%) or cancer and mental health care (9/39, 23.1%) were more likely to be offended by information in their EHR compared to the other groups (cancer care: 37/375, 9.9%; other conditions care: 383/3316, 11.6%; no care: 22/206, 10.7%; other conditions care: odds ratio 0.37, 95% CI 0.29-0.46; P<.001; model A). Additionally, female patients, those with bad or very bad health conditions, and patients with bachelor's or master's degrees were significantly more likely to feel offended. Errors, the health care professionals' disrespectful language, and perceived unnecessary information were the most frequently mentioned reasons for being offended. Patients with mental health care reported more often that unnecessary information and professionals' opinions and word choices were experienced as offensive compared to other patients. This study contributes new knowledge by identifying differences across multiple patient groups. Although a minority of patients felt offended by their EHR, health care professionals should consider that some patients, particularly those who have received mental health care or cancer and mental health care, may be offended by specific information or word choices in their EHRs. To address this, health care professionals should receive education on how to write their notes in a neutral tone and avoid potentially offensive topics. Improving the quality of EHRs could strengthen the relationship between patients and professionals.

Key Points Electronic health record (EHR) systems are increasingly being implemented all over the world, but represent a vast, underused data resource for biomedical research. Structured EHR data, such as encoded diagnosis and medication information, are the easiest data sources to process, but advances in text-mining methods has made it possible to also use the narrative parts of patient records. Statistical studies of the distribution and co-occurrence of clinical features in large collections of patient records enables identification of correlations between, for example, diseases (comorbidities) or between medications and adverse drug reactions. Knowledge-discovery and machine-learning methods can be used both for discovering novel patterns in patient data and for classification and predictive purposes, such as outcome or risk assessment. This has the potential to extend current EHR decision support systems, which integrate available patient data with clinical guidelines to provide assistance to the physician at the point of care. Research platforms built on EHR data, alone or coupled to genotype data, provide an inexpensive and timely way to sample relevant case and control cohorts based on relevant clinical features. As EHR and DNA databases become increasingly interlinked, genotype–phenotype association studies may be designed and conducted by re-using existing data. The growing political focus on the adoption of EHR systems must be accompanied by funding and strategic research into data standards, interoperability and security. Legal matters such as data ownership, privacy and consent need to be addressed to find the right balance between public demands for autonomy and privacy, and manageable procedures for researchers to access data. Fulfilling the full potential of electronic health data for scientific discovery and improved public health will require collaboration across stakeholders and research groups. The adoption of electronic health records will provide a rich resource for biomedical researchers. This Review discusses the potential for their use in informed decision making in the clinic, for a finer understanding of genotype–phenotype relationships and for selection of research cohorts, along with the current challenges for their mining and use. Clinical data describing the phenotypes and treatment of patients represents an underused data source that has much greater research potential than is currently realized. Mining of electronic health records (EHRs) has the potential for establishing new patient-stratification principles and for revealing unknown disease correlations. Integrating EHR data with genetic data will also give a finer understanding of genotype–phenotype relationships. However, a broad range of ethical, legal and technical reasons currently hinder the systematic deposition of these data in EHRs and their mining. Here, we consider the potential for furthering medical research and clinical care using EHR data and the challenges that must be overcome before this is a reality.

Background Although alert fatigue is blamed for high override rates in contemporary clinical decision support systems, the concept of alert fatigue is poorly defined. We tested hypotheses arising from two possible alert fatigue mechanisms: (A) cognitive overload associated with amount of work, complexity of work, and effort distinguishing informative from uninformative alerts, and (B) desensitization from repeated exposure to the same alert over time. Methods Retrospective cohort study using electronic health record data (both drug alerts and clinical practice reminders) from January 2010 through June 2013 from 112 ambulatory primary care clinicians. The cognitive overload hypotheses were that alert acceptance would be lower with higher workload (number of encounters, number of patients), higher work complexity (patient comorbidity, alerts per encounter), and more alerts low in informational value (repeated alerts for the same patient in the same year). The desensitization hypothesis was that, for newly deployed alerts, acceptance rates would decline after an initial peak. Results On average, one-quarter of drug alerts received by a primary care clinician, and one-third of clinical reminders, were repeats for the same patient within the same year. Alert acceptance was associated with work complexity and repeated alerts, but not with the amount of work. Likelihood of reminder acceptance dropped by 30% for each additional reminder received per encounter, and by 10% for each five percentage point increase in proportion of repeated reminders. The newly deployed reminders did not show a pattern of declining response rates over time, which would have been consistent with desensitization. Interestingly, nurse practitioners were 4 times as likely to accept drug alerts as physicians. Conclusions Clinicians became less likely to accept alerts as they received more of them, particularly more repeated alerts. There was no evidence of an effect of workload per se, or of desensitization over time for a newly deployed alert. Reducing within-patient repeats may be a promising target for reducing alert overrides and alert fatigue.

Abstract Electronic medical records have been touted as a solution to many of the shortcomings of health care systems. The aim of this essay is to review pertinent literature and present examples and recommendations from several decades of experience in the use of medical records in primary health care, in ways that can help primary care doctors to organize their work processes to improve patient care. Considerable problems have been noted to result from a lack of interoperability and standardization of interfaces among these systems, impairing the effective collaboration and information exchange in the care of complex patients. It is extremely important that regional and national health policies be established to assure standardization and interoperability of systems. Lack of interoperability contributes to the fragmentation of the information environment. The electronic medical record (EMR) is a disruptive technology that can revolutionize the way we care for patients. The EMR has been shown to improve quality and reliability in the delivery of healthcare services when appropriately implemented. Careful attention to the impact of the EMR on clinical workflows, in order to take full advantage of the potential of the EMR to improve patient care, is the key lesson from our experience in the deployment and use of these systems. Resumo Os registros médicos eletrônicos (RME) têm sido apontados como uma solução para muitas das deficiências dos sistemas de saúde. O objetivo deste ensaio é revisar a literatura pertinente e apresentar exemplos e recomendações de várias décadas de experiência no uso de registros médicos na atenção primária à saúde, de maneira a ajudá-los na organização de seus processos de trabalho para melhorar o atendimento ao paciente. Observou-se que problemas consideráveis resultam da falta de interoperabilidade e padronização de interfaces entre esses sistemas, prejudicando a colaboração efetiva e a troca de informações no atendimento a pacientes complexos. É extremamente importante que políticas regionais e nacionais de saúde sejam estabelecidas para garantir a padronização e interoperabilidade dos sistemas. A falta de interoperabilidade contribui para a fragmentação do ambiente de informações. O prontuário eletrônico (RME) é uma tecnologia disruptiva que pode revolucionar a maneira como cuidamos dos pacientes. Foi demonstrado que o RME melhora a qualidade e a confiabilidade na prestação de serviços de saúde quando implementada adequadamente. Uma atenção cuidadosa ao impacto do RME nos fluxos de trabalho clínicos, a fim de aproveitar ao máximo o potencial do RME para melhorar o atendimento ao paciente, é a principal lição de nossa experiência na implantação e uso desses sistemas.