Monica Cepoiu-Martin

Background: Measurement-based care (MBC) has emerged as a promising strategy to support effective chronic disease management. Patient-reported outcome measures (PROMs) are increasingly used to capture patients’ perspectives on symptoms and quality of life, and their integration into electronic health record (EHR) systems may enhance their clinical utility. However, evidence on their impact when embedded within routine clinical workflows remains limited. Objective: To systematically identify and synthesize evidence on the impact of integrating patient-reported outcome measures (PROMs) into electronic health record (EHR) systems on clinical care processes and patient outcomes among individuals with chronic conditions. Methods: A systematic review was conducted in accordance with PRISMA guidelines. Multiple databases (MEDLINE, Embase, CINAHL, PsycINFO, Cochrane Library, Scopus, and Web of Science) were searched from January 2000 to February 2026. Eligible studies included quantitative research evaluating EHR-integrated PROMs used in routine clinical care for chronic conditions. Data extracted included study characteristics, PROM type, integration approach, and clinical and process-related outcomes. Results: Of 10 included studies, most were conducted in the United States and spanned oncology, rheumatology, primary care, and musculoskeletal settings. Some of these studies reported that EHR-integrated PROMs improved symptom identification and monitoring and were associated with gains in clinic efficiency, including shorter wait times and fewer follow-up visits. Some studies reported reduced healthcare utilization and increased diagnostic activity. However, effects on patient satisfaction, visit volume, and broader clinical outcomes were inconsistent. Heterogeneity in PROMs, implementation approaches, and outcomes precluded meta-analysis. Conclusions: EHR-integrated PROMs may improve symptom monitoring and help clinicians identify patient needs more efficiently within routine chronic disease care. Several studies also reported benefits related to clinic workflow, including shorter wait times, fewer follow-up visits, and improved care coordination. However, the impact on patient satisfaction, healthcare utilization, and longer-term clinical outcomes was less consistent across studies. Overall, PROMs appeared to be most useful when they were directly integrated into clinical workflows and used to inform real-time care decisions. Further research in more diverse patient populations and healthcare settings is needed to better understand their long-term effectiveness and real-world use in chronic disease management. Systematic Review Registration: https://www.crd.york.ac.uk/PROSPERO/view/CRD420261297619 , PROSPERO CRD420261297619.

Rationale: Timely assessment of a chronic condition is critical to prevent long‐term irreversible consequences. Patients with inflammatory arthritis (IA) symptoms require diagnosis by a rheumatologist and intervention initiation to minimize potential joint damage. With limited rheumatologist capacity, meeting urgency wait time benchmarks can be challenging. We investigate the impact of the maximum wait time guarantee (MWTG) policy and referral volume changes in a rheumatology central intake (CI) system on meeting this challenge.Methods: We applied a system simulation approach to model a high‐volume CI rheumatology clinic. Model parameters were based on the referral and triage data from the CI and clinic appointment data. We compare the wait time performance of the current distribution policy MWTG and when referral volumes change.Results: The MWTG policy ensures 100% of new patients see a rheumatologist within their urgency wait time benchmark. However, the average wait time for new patients increased by 51% (178–269 days). A 10% decrease in referrals resulted in a 76% decrease on average wait times (178–43 days) for new patients and an increase in the number of patients seen by a rheumatologist within 1 year of the initial visit.Conclusion: An MWTG policy can result in intended and unintended consequences—ensuring that all patients meet the wait time benchmarks but increasing wait times overall. Relatively small changes in referral volume significantly impact wait times. These relationships can assist clinic managers and policymakers decide on the best approach to manage referrals for better system performance.

Delays beyond recommended wait times, especially for specialist services, are associated with adverse health outcomes. The Alberta Surgical Initiative aims to improve the referral wait time—the time between a referral is received at the central intake to the time a specialist sees the patient. Using the discrete event simulation modelling approach, we evaluated and compared the impact of four referral distribution policies in a central intake system on three system performance measures (number of consultations, referral wait time and surgeon utilization). The model was co-designed with clinicians and clinic staff to represent the flow of patients through the system. We used data from the Facilitated Access to Surgical Treatment (FAST) centralized intake referral program for General Surgery to parameterize the model. Four distribution policies were evaluated – next-available-surgeon, sequential, "blackjack," and "kanban." A sequential distribution of referrals for surgical consultation among the surgeons resulted in the worst performance in terms of the number of consultations, referral wait time and surgeon utilization. The three other distribution policies are comparable in performance. The "next available surgeon" model provided the most efficient and robust model, with approximately 1,000 more consultations, 100 days shorter referral time and a 14% increase in surgeon utilization. Discrete event simulation (DES) modelling can be an effective tool to illustrate and communicate the impact of the referral distribution policy on system performance in terms of the number of consultations, referral wait time and surgeon utilization.

Background: Community-based health care (CBHC) is a shift towards healthcare integration and community services closer to home. Variation in system approaches harkens the need for a conceptual framework to evaluate outcomes and impacts. We set out to develop a CBHC-specific evaluation framework in the context of a provincial ministry of health planning process in Canada. Methods: A multi-step approach was used to develop the CBHC evaluation framework. Modified Delphi informed conceptualization and prioritization of indicators. Formative research identified evaluation framework elements (triple aim, global measures, and impact), health system levels (tiers), and potential CBHC indicators (n = 461). Two Delphi rounds were held. Round 1, panelists independently ranked indicators on CBHC relevance and health system tiering. Results were analyzed by coding agreement/disagreement frequency and central tendency measures. Round 2, a consensus meeting was used to discuss disagreement, identify Tier 1 indicators and concepts, and define indicators not relevant to CBHC (Tier 4). Post-Delphi, indicators and concepts were refined, Tier 1 concepts mapped to the evaluation framework, and indicator narratives developed. Three stakeholder consultations (scientific, government, and public/patient communities) were held for endorsement and recommendation. Results: Round 1 Delphi results showed agreement for 300 and disagreement for 161 indicators. Round 2 consensus resulted in 103 top tier indicators (Tier 1 = 19, Tier 2 = 84), 358 bottom Tier 3 and 4 indicators, non-CBHC measure definitions, and eight Tier 1 indicator concepts—Mortality/Suicide; Quality of Life, and Patient Reported Outcome Measures; Global Patient Reported Experience Measures; Cost of Care, Access to Integrated Primary Care; Avoidable Emergency Department Use; Avoidable Hospitalization; and E-health Penetration. Post Delphi results refined Tier 3 (n = 289) and 4 (n = 69) indicators, and identified 18 Tier 2 and 3 concepts. When mapped to the evaluation framework, Tier 1 concepts showed full coverage across the elements. ‘Indicator narratives’ depicted systemness and integration for evaluating CBHC. Stakeholder consultations affirmed endorsement of the approach and evaluation framework; refined concepts; and provided key considerations to further operationalize and contextualize indicators, and evaluate CBHC as a health system approach. Conclusions: This research produced a novel evaluation framework to conceptualize and evaluate CBHC initiatives. The evaluation framework revealed the importance of a health system approach for evaluating CBHC.

Background: Simple visualizations in health research data, such as scatter plots, heat maps, and bar charts, typically present relationships between 2 variables. Interactive visualization methods allow for multiple related facets such as numerous risk factors to be studied simultaneously, leading to data insights through exploring trends and patterns from complex big health care data. The technique presents a powerful tool that can be used in combination with statistical analysis for knowledge discovery, hypothesis generation and testing, and decision support. Objective: The primary objective of this scoping review is to describe and summarize the evidence of interactive visualization applications, methods, and tools being used in population health and health services research (HSR) and their subdomains in the last 15 years, from January 1, 2005, to March 30, 2019. Our secondary objective is to describe the use cases, metrics, frameworks used, settings, target audience, goals, and co-design of applications. Methods: We adapted standard scoping review guidelines with a peer-reviewed search strategy: 2 independent researchers at each stage of screening and abstraction, with a third independent researcher to arbitrate conflicts and validate findings. A comprehensive abstraction platform was built to capture the data from diverse bodies of literature, primarily from the computer science and health care sectors. After screening 11,310 articles, we present findings from 56 applications from interrelated areas of population health and HSR, as well as their subdomains such as epidemiologic surveillance, health resource planning, access, and use and costs among diverse clinical and demographic populations. Results: In this companion review to our earlier systematic synthesis of the literature on visual analytics applications, we present findings in 6 major themes of interactive visualization applications developed for 8 major problem categories. We found a wide application of interactive visualization methods, the major ones being epidemiologic surveillance for infectious disease, resource planning, health service monitoring and quality, and studying medication use patterns. The data sources included mostly secondary administrative and electronic medical record data. In addition, at least two-thirds of the applications involved participatory co-design approaches while introducing a distinct category, embedded research, within co-design initiatives. These applications were in response to an identified need for data-driven insights into knowledge generation and decision support. We further discuss the opportunities stemming from the use of interactive visualization methods in studying global health; inequities, including social determinants of health; and other related areas. We also allude to the challenges in the uptake of these methods. Conclusions: Visualization in health has strong historical roots, with an upward trend in the use of these methods in population health and HSR. Such applications are being fast used by academic and health care agencies for knowledge discovery, hypotheses generation, and decision support. International Registered Report Identifier (IRRID): RR2-10.2196/14019

Background: Visual analytics (VA) promotes the understanding of data with visual, interactive techniques, using analytic and visual engines. The analytic engine includes automated techniques, whereas common visual outputs include flow maps and spatiotemporal hot spots.Objective: This scoping review aims to address a gap in the literature, with the specific objective to synthesize literature on the use of VA tools, techniques, and frameworks in interrelated health care areas of population health and health services research (HSR).Methods: Using the 2018 PRISMA-ScR (Preferred Reporting Items for Systematic Reviews and Meta-Analyses Extension for Scoping Reviews) guidelines, the review focuses on peer-reviewed journal articles and full conference papers from 2005 to March 2019. Two researchers were involved at each step, and another researcher arbitrated disagreements. A comprehensive abstraction platform captured data from diverse bodies of the literature, primarily from the computer and health sciences.Results: After screening 11,310 articles, findings from 55 articles were synthesized under the major headings of visual and analytic engines, visual presentation characteristics, tools used and their capabilities, application to health care areas, data types and sources, VA frameworks, frameworks used for VA applications, availability and innovation, and co-design initiatives. We found extensive application of VA methods used in areas of epidemiology, surveillance and modeling, health services access, use, and cost analyses. All articles included a distinct analytic and visualization engine, with varying levels of detail provided. Most tools were prototypes, with 5 in use at the time of publication. Seven articles presented methodological frameworks. Toward consistent reporting, we present a checklist, with an expanded definition for VA applications in health care, to assist researchers in sharing research for greater replicability. We summarized the results in a Tableau dashboard.Conclusions: With the increasing availability and generation of big health care data, VA is a fast-growing method applied to complex health care data. What makes VA innovative is its capability to process multiple, varied data sources to demonstrate trends and patterns for exploratory analysis, leading to knowledge generation and decision support. This is the first review to bridge a critical gap in the literature on VA methods applied to the areas of population health and HSR, which further indicates possible avenues for the adoption of these methods in the future. This review is especially important in the wake of COVID-19 surveillance and response initiatives, where many VA products have taken center stage.International Registered Report Identifier (IRRID): RR2-10.2196/14019

BACKGROUND Visual analytics (VA) promotes the understanding of data with visual, interactive techniques, using analytic and visual engines. The analytic engine includes automated techniques, whereas common visual outputs include flow maps and spatiotemporal hot spots. OBJECTIVE This scoping review aims to address a gap in the literature, with the specific objective to synthesize literature on the use of VA tools, techniques, and frameworks in interrelated health care areas of population health and health services research (HSR). METHODS Using the 2018 PRISMA-ScR (Preferred Reporting Items for Systematic Reviews and Meta-Analyses Extension for Scoping Reviews) guidelines, the review focuses on peer-reviewed journal articles and full conference papers from 2005 to March 2019. Two researchers were involved at each step, and another researcher arbitrated disagreements. A comprehensive abstraction platform captured data from diverse bodies of the literature, primarily from the computer and health sciences. RESULTS After screening 11,310 articles, findings from 55 articles were synthesized under the major headings of visual and analytic engines, visual presentation characteristics, tools used and their capabilities, application to health care areas, data types and sources, VA frameworks, frameworks used for VA applications, availability and innovation, and co-design initiatives. We found extensive application of VA methods used in areas of epidemiology, surveillance and modeling, health services access, use, and cost analyses. All articles included a distinct analytic and visualization engine, with varying levels of detail provided. Most tools were prototypes, with 5 in use at the time of publication. Seven articles presented methodological frameworks. Toward consistent reporting, we present a checklist, with an expanded definition for VA applications in health care, to assist researchers in sharing research for greater replicability. We summarized the results in a Tableau dashboard. CONCLUSIONS With the increasing availability and generation of big health care data, VA is a fast-growing method applied to complex health care data. What makes VA innovative is its capability to process multiple, varied data sources to demonstrate trends and patterns for exploratory analysis, leading to knowledge generation and decision support. This is the first review to bridge a critical gap in the literature on VA methods applied to the areas of population health and HSR, which further indicates possible avenues for the adoption of these methods in the future. This review is especially important in the wake of COVID-19 surveillance and response initiatives, where many VA products have taken center stage. INTERNATIONAL REGISTERED REPORT RR2-10.2196/14019

Background: Visual analytics (VA) promotes the understanding of data using visual, interactive techniques and using analytic and visual engines. The analytic engine includes machine learning and other automated techniques, whereas common visual outputs include flow maps and spatiotemporal hotspots for studying service gaps and disease distribution. The principal objective of this scoping review is to examine the state of science on VA and the various tools, strategies, and frameworks used in population health and health services research (HSR). Objective: The purpose of this scoping review is to develop an overarching global view of established techniques, frameworks, and methods of VA in population health and HSR. The main objectives are to explore, map, and synthesize the literature related to VA in its application to the two main focus areas of health care. Methods: We will use established scoping review methods to meet the study objective. As the use of the term visual analytics is inconsistent, one of the major challenges was operationalizing the concepts for developing the search strategy, based on the three main concepts of population health, HSR, and VA. We included peer reviewed and grey literature sources from 2005 till March 2019 in the search. Independent teams of researchers will screen the titles, abstracts and full text articles, whereas an independent researcher will arbiter conflicts. Data will be abstracted and presented using the Preferred Reporting Items for Systematic reviews and Meta-Analyses extension for Scoping Reviews checklist and explanation by two independent researchers. Results: As of late August 2019, the scoping review is in the full-text screening stage. Data synthesis will follow and the first results are expected to be submitted for publication in December 2019. In this protocol, the methods for undertaking this scoping review are detailed. We present how we operationalized the varied concepts of population health, health services, and VA. The main results of the scoping review will synthesize peer reviewed and grey literature sources on the main methods of VA in the interrelated fields of population health and health services research from January 2005 till March 2019. Conclusions: VA is being increasingly used and integrated with emerging technologies to support decision making using large data sets. This scoping review of the VA tools, strategies, and frameworks applied to population health and health services aims to increase awareness of this approach for uptake by decision makers working within and toward developing learning health systems globally. International Registered Report Identifier (IRRID): DERR1-10.2196/14019

BACKGROUND Visual analytics (VA) promotes the understanding of data using visual, interactive techniques and using analytic and visual engines. The analytic engine includes machine learning and other automated techniques, whereas common visual outputs include flow maps and spatiotemporal hotspots for studying service gaps and disease distribution. The principal objective of this scoping review is to examine the state of science on VA and the various tools, strategies, and frameworks used in population health and health services research (HSR). OBJECTIVE The purpose of this scoping review is to develop an overarching global view of established techniques, frameworks, and methods of VA in population health and HSR. The main objectives are to explore, map, and synthesize the literature related to VA in its application to the two main focus areas of health care. METHODS We will use established scoping review methods to meet the study objective. As the use of the term visual analytics is inconsistent, one of the major challenges was operationalizing the concepts for developing the search strategy, based on the three main concepts of population health, HSR, and VA. We included peer reviewed and grey literature sources from 2005 till March 2019 in the search. Independent teams of researchers will screen the titles, abstracts and full text articles, whereas an independent researcher will arbiter conflicts. Data will be abstracted and presented using the Preferred Reporting Items for Systematic reviews and Meta-Analyses extension for Scoping Reviews checklist and explanation by two independent researchers. RESULTS As of late August 2019, the scoping review is in the full-text screening stage. Data synthesis will follow and the first results are expected to be submitted for publication in December 2019. In this protocol, the methods for undertaking this scoping review are detailed. We present how we operationalized the varied concepts of population health, health services, and VA. The main results of the scoping review will synthesize peer reviewed and grey literature sources on the main methods of VA in the interrelated fields of population health and health services research from January 2005 till March 2019. CONCLUSIONS VA is being increasingly used and integrated with emerging technologies to support decision making using large data sets. This scoping review of the VA tools, strategies, and frameworks applied to population health and health services aims to increase awareness of this approach for uptake by decision makers working within and toward developing learning health systems globally. CLINICALTRIAL Not applicable. INTERNATIONAL REGISTERED REPORT DERR1-10.2196/14019

Antipsychotic medication use has steadily increased in Canada, with an expansion in the profile of users and the diagnoses for which they are used. The use of antipsychotics is associated with a number of adverse effects for which routine monitoring is recommended. The objectives of this study were to determine the most common diagnoses associated with antipsychotic use in children in Alberta, Canada and the proportion who receive recommended laboratory tests for adverse effects.