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Kalsy M, Burant R, Ball S, Pohnert A, Dolansky MA. A human centered design approach to define and measure documentation quality using an EHR virtual simulation. PLoS One 2024; 19:e0308992. [PMID: 39159187 PMCID: PMC11332943 DOI: 10.1371/journal.pone.0308992] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/28/2024] [Accepted: 08/04/2024] [Indexed: 08/21/2024] Open
Abstract
Electronic health record (EHR) documentation serves multiple functions, including recording patient health status, enabling interprofessional communication, supporting billing, and providing data to support the quality infrastructure of a Learning Healthcare System. There is no definition and standardized method to assess documentation quality in EHRs. Using a human-centered design (HCD) approach, we define and describe a method to measure documentation quality. Documentation quality was defined as timely, accurate, user-centered, and efficient. Measurement of quality used a virtual simulated standardized patient visit via an EHR vendor platform. By observing and recording documentation efforts, nurse practitioners (NPs) (N = 12) documented the delivery of an Age-Friendly Health System (AFHS) 4Ms (what Matters, Medication, Mentation, and Mobility) clinic visit using a standardized case. Results for timely documentation indicated considerable variability in completion times of documenting the 4Ms. Accuracy varied, as there were many types of episodes of erroneous documentation and extra time in seconds in documenting the 4Ms. The type and frequency of erroneous documentation efforts were related to navigation burden when navigating to different documentation tabs. The evaluated system demonstrated poor usability, with most participants scoring between 60 and 70 on the System Usability Scale (SUS). Efficiency, measured as click burden (the number of clicks used to navigate through a software system), revealed significant variability in the number of clicks required, with the NPs averaging approximately 13 clicks above the minimum requirement. The HCD methodology used in this study to assess the documentation quality proved feasible and provided valuable information on the quality of documentation. By assessing the quality of documentation, the gathered data can be leveraged to enhance documentation, optimize user experience, and elevate the quality of data within a Learning Healthcare System.
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Affiliation(s)
- Megha Kalsy
- Frances Payne Bolton School of Nursing, Case Western Reserve University, Cleveland, Ohio, United States of America
- Informatics, Decision-Enhancement and Analytic Sciences (IDEAS) Center, George E. Wahlen Veterans Affairs Medical Center, Salt Lake City, Utah, United States of America
| | - Ryan Burant
- Frances Payne Bolton School of Nursing, Case Western Reserve University, Cleveland, Ohio, United States of America
- Notre Dame College, South Euclid, Ohio, United States of America
| | - Sarah Ball
- MinuteClinic, Woonsocket, Rhode Island, United States of America
| | - Anne Pohnert
- MinuteClinic, Woonsocket, Rhode Island, United States of America
| | - Mary A. Dolansky
- Frances Payne Bolton School of Nursing, Case Western Reserve University, Cleveland, Ohio, United States of America
- Veterans Affairs Northeast Ohio Healthcare System, Cleveland, Ohio, United States of America
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Lane C, Nathan N, Wiggers J, Hall A, Shoesmith A, Bauman A, Groombridge D, Sutherland R, Wolfenden L. Learning Health System to rapidly improve the implementation of a school physical activity policy. Implement Sci Commun 2024; 5:85. [PMID: 39085972 PMCID: PMC11292924 DOI: 10.1186/s43058-024-00619-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/08/2023] [Accepted: 07/13/2024] [Indexed: 08/02/2024] Open
Abstract
BACKGROUND Learning Health Systems (LHS) - characterised by cycles of evidence generation and application - are increasingly recognised for their potential to improve public health interventions and optimise health impacts; however there is little evidence of their application in the context of public health practice. Here, we describe how an Australian public health unit applied a LHS approach to successfully improve a model of support for implementation of a school-based physical activity policy. METHODS This body of work was undertaken in the context of a strong research-practice partnership. Core LHS capabilities included: i) partnerships and stakeholder engagement; ii) workforce development and learning health communities; iii) multi-disciplinary scientific expertise; iv) practice data collection and management system; v) evidence surveillance and synthesis; and vi) governance and organisational processes of decision making. Three cycles of data generation and application were used. Within each cycle, randomised controlled trials conducted in NSW primary schools were used to generate data on the support model's effectiveness for improving schools' implementation of a government physical activity policy, its delivery costs, and process measures such as adoption and acceptability. Each type of data were analysed independently, synthesised, and then presented to a multi-disciplinary team of researchers and practitioners, in consult with stakeholders, leading to collaborative decisions for incremental improvements to the support model. RESULTS Cycle 1 tested the first version of the support model (composed of five implementation strategies targeting identified barriers of policy implementation) and showed the model's feasibility and efficacy for improving schools' policy implementation. Data-informed changes were made to enhance impact, including the addition of three implementation strategies to address outstanding barriers. Cycle 2 (now, testing a package of eight implementation strategies) established the model's effectiveness and cost-effectiveness for improving school's policy implementation. Data-informed changes were made to reduce delivery costs, specifically adapting the costliest strategies to reduce in-person contact from external support personnel. Cycle 3 showed that the adaptations minimised the relative cost of delivery without adversely impacting on the effect. CONCLUSIONS Through this process, we identified an effective, cost-effective, acceptable and scalable policy implementation support model for service delivery. This provides important information to inform or support LHS approaches for other agencies seeking to optimise the health impact of evidence-based interventions.
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Affiliation(s)
- Cassandra Lane
- School of Medicine and Public Health, The University of Newcastle, 1 University Drive Callaghan, Newcastle, NSW, Australia.
- Hunter New England Population Health, Hunter New England Area Health Service, Newcastle , NSW, Australia.
- National Centre of Implementation Science, The University of Newcastle, Newcastle, NSW, Australia.
- Hunter Medical Research Institute, New Lambton Heights, Newcastle, NSW, Australia.
| | - Nicole Nathan
- School of Medicine and Public Health, The University of Newcastle, 1 University Drive Callaghan, Newcastle, NSW, Australia
- Hunter New England Population Health, Hunter New England Area Health Service, Newcastle , NSW, Australia
- National Centre of Implementation Science, The University of Newcastle, Newcastle, NSW, Australia
- Hunter Medical Research Institute, New Lambton Heights, Newcastle, NSW, Australia
| | - John Wiggers
- School of Medicine and Public Health, The University of Newcastle, 1 University Drive Callaghan, Newcastle, NSW, Australia
- Hunter New England Population Health, Hunter New England Area Health Service, Newcastle , NSW, Australia
- National Centre of Implementation Science, The University of Newcastle, Newcastle, NSW, Australia
- Hunter Medical Research Institute, New Lambton Heights, Newcastle, NSW, Australia
| | - Alix Hall
- School of Medicine and Public Health, The University of Newcastle, 1 University Drive Callaghan, Newcastle, NSW, Australia
- Hunter New England Population Health, Hunter New England Area Health Service, Newcastle , NSW, Australia
- National Centre of Implementation Science, The University of Newcastle, Newcastle, NSW, Australia
- Hunter Medical Research Institute, New Lambton Heights, Newcastle, NSW, Australia
| | - Adam Shoesmith
- School of Medicine and Public Health, The University of Newcastle, 1 University Drive Callaghan, Newcastle, NSW, Australia
- Hunter New England Population Health, Hunter New England Area Health Service, Newcastle , NSW, Australia
- National Centre of Implementation Science, The University of Newcastle, Newcastle, NSW, Australia
- Hunter Medical Research Institute, New Lambton Heights, Newcastle, NSW, Australia
| | - Adrian Bauman
- School of Public Health, University of Sydney, Sydney, NSW, Australia
| | - Daniel Groombridge
- Hunter New England Population Health, Hunter New England Area Health Service, Newcastle , NSW, Australia
| | - Rachel Sutherland
- School of Medicine and Public Health, The University of Newcastle, 1 University Drive Callaghan, Newcastle, NSW, Australia
- Hunter New England Population Health, Hunter New England Area Health Service, Newcastle , NSW, Australia
- National Centre of Implementation Science, The University of Newcastle, Newcastle, NSW, Australia
- Hunter Medical Research Institute, New Lambton Heights, Newcastle, NSW, Australia
| | - Luke Wolfenden
- School of Medicine and Public Health, The University of Newcastle, 1 University Drive Callaghan, Newcastle, NSW, Australia
- Hunter New England Population Health, Hunter New England Area Health Service, Newcastle , NSW, Australia
- National Centre of Implementation Science, The University of Newcastle, Newcastle, NSW, Australia
- Hunter Medical Research Institute, New Lambton Heights, Newcastle, NSW, Australia
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McCabe E, Dyson M, McNeil D, Hindmarch W, Ortega I, Arnold PD, Dimitropoulos G, Clements R, Santana MJ, Zwicker JD. A protocol for the formative evaluation of the implementation of patient-reported outcome measures in child and adolescent mental health services as part of a learning health system. Health Res Policy Syst 2024; 22:85. [PMID: 39010106 PMCID: PMC11251393 DOI: 10.1186/s12961-024-01174-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/15/2023] [Accepted: 07/01/2024] [Indexed: 07/17/2024] Open
Abstract
BACKGROUND Mental health conditions affect one in seven young people and research suggests that current mental health services are not meeting the needs of most children and youth. Learning health systems are an approach to enhancing services through rapid, routinized cycles of continuous learning and improvement. Patient-reported outcome measures provide a key data source for learning health systems. They have also been shown to improve outcomes for patients when integrated into routine clinical care. However, implementing these measures into health systems is a challenging process. This paper describes a protocol for a formative evaluation of the implementation of patient-reported measures in a newly operational child and adolescent mental health centre in Calgary, Canada. The purpose is to optimize the collection and use of patient-reported outcome measures. Our specific objectives are to assess the implementation progress, identify barriers and facilitators to implementation, and explore patient, caregivers and clinician experiences of using these measures in routine clinical care. METHODS This study is a mixed-methods, formative evaluation using the Consolidated Framework for Implementation Research. Participants include patients and caregivers who have used the centre's services, as well as leadership, clinical and support staff at the centre. Focus groups and semi-structured interviews will be conducted to assess barriers and facilitators to the implementation and sustainability of the use of patient-reported outcome measures, as well as individuals' experiences with using these measures within clinical care. The data generated by the patient-reported measures over the first five months of the centre's operation will be analyzed to understand implementation progress, as well as validity of the chosen measures for the centres' population. DISCUSSION The findings of this evaluation will help to identify and address the factors that are affecting the successful implementation of patient-reported measures at the centre. They will inform the co-design of strategies to improve implementation with key stakeholders, which include patients, clinical staff, and leadership at the centre. To our knowledge, this is the first study of the implementation of patient-reported outcome measures in child and adolescent mental health services and our findings can be used to enhance future implementation efforts in similar settings.
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Affiliation(s)
- Erin McCabe
- School of Public Policy and Department of Pediatrics, University of Calgary, Calgary, Canada.
| | - Michele Dyson
- Provincial Addictions and Mental Health, Alberta Health Services, Edmonton, Canada
| | - Deborah McNeil
- Maternal Newborn Child and Youth Strategic Clinical Network, Alberta Health Services, Edmonton, AB, Canada
- Department of Community Health Sciences and Faculty of Nursing, University of Calgary, Calgary, Canada
| | | | - Iliana Ortega
- Department of Psychiatry and Mathison Centre for Mental Health Research and Education, Hotchkiss Brain Institute, University of Calgary, Calgary, Canada
| | - Paul D Arnold
- Department of Psychiatry and Mathison Centre for Mental Health Research and Education, Hotchkiss Brain Institute, University of Calgary, Calgary, Canada
| | | | - Ryan Clements
- Alberta Health Services Calgary Zone, Calgary, Canada
| | - Maria J Santana
- Department of Pediatrics and Community Health Sciences, University of Calgary, Calgary, Canada
| | - Jennifer D Zwicker
- School of Public Policy and Faculty of Kinesiology, University of Calgary, Calgary, Canada
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Larson CL, Vanstone JR, Mise TR, Tupper SM, Groot G, Azizian AR. Development and validity testing of a matrix to evaluate maturity of clinical pathways: a case study in Saskatchewan, Canada. BMC Health Serv Res 2024; 24:793. [PMID: 38982479 PMCID: PMC11234781 DOI: 10.1186/s12913-024-11239-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/20/2023] [Accepted: 06/24/2024] [Indexed: 07/11/2024] Open
Abstract
BACKGROUND Healthcare systems are transforming into learning health systems that use data-driven and research-informed approaches to achieve continuous improvement. One of these approaches is the use of clinical pathways, which are tools to standardize care for a specific population and improve healthcare quality. Evaluating the maturity of clinical pathways is necessary to inform pathway development teams and health system decision makers about required pathway revisions or implementation supports. In an effort to improve the development, implementation, and sustainability of provincial clinical pathways, we developed a clinical pathways maturity evaluation matrix. To explore the initial content and face validity of the matrix, we used it to evaluate a case pathway within a provincial health authority in Saskatchewan, Canada. METHODS By using iterative consensus-based processes, we gathered feedback from stakeholders including patient and family partners, policy makers, clinicians, and quality improvement specialists, to rank, retain, or remove enablers and sub-enablers of the draft matrix. We tested the matrix on the Chronic Pain Pathway (CPP) for primary care in a local pilot area and revised the matrix based on feedback from the CPP development team leader. RESULTS The final matrix contains five enablers (i.e., Design, Ownership and Performer, Infrastructure, Performance Management, and Culture), 20 sub-enablers, and three trajectory definitions for each sub-enabler. Supplemental documents were created for six sub-enablers. The CPP scored 15 out of 40 possible points of maturity. Although the pathway scored highest in the Design enabler (10/12), it requires more attention in several areas, specifically the Ownership and Performer and the Performance Management enablers, each of which scored zero. Additionally, the Infrastructure and Culture enablers scored 2/4 and 3/8 points, respectively. These areas of the CPP are in need of improvement in order to enhance the overall maturity of the CPP. CONCLUSIONS We developed a clinical pathways maturity matrix to evaluate the various dimensions of clinical pathways' development and implementation. The goals of this initial work were to develop and validate a tool to assess the maturity and readiness of new or existing pathways and to track pathways' revisions and improvements.
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Affiliation(s)
| | | | - Taysa-Rhea Mise
- Clinical Excellence, Saskatchewan Health Authority, Regina, SK, Canada
| | - Susan Mary Tupper
- Clinical Excellence, Saskatchewan Health Authority, Regina, SK, Canada
- College of Medicine, University of Saskatchewan, Saskatoon, SK, Canada
| | - Gary Groot
- Clinical Excellence, Saskatchewan Health Authority, Regina, SK, Canada
- College of Medicine, University of Saskatchewan, Saskatoon, SK, Canada
| | - Amir Reza Azizian
- Clinical Excellence, Saskatchewan Health Authority, Regina, SK, Canada.
- College of Medicine, University of Saskatchewan, Saskatoon, SK, Canada.
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Arsenault-Lapierre G, Lemay-Compagnat A, Guillette M, Couturier Y, Massamba V, Dufour I, Maubert E, Fournier C, Denis J, Morin C, Vedel I. Dashboards to Support Implementation of the Quebec Alzheimer Plan: Evaluation Study With Regional and Professional Considerations. JMIR Form Res 2024; 8:e55064. [PMID: 38717803 PMCID: PMC11112472 DOI: 10.2196/55064] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/01/2023] [Revised: 03/27/2024] [Accepted: 04/04/2024] [Indexed: 05/25/2024] Open
Abstract
BACKGROUND Health organizations face the critical task of executing and overseeing comprehensive health care. To address the challenges associated with this task, evidence-based dashboards have emerged as valuable tools. Since 2016, the regional health organizations of Quebec, Canada, have been responsible for ensuring implementation of the Quebec Alzheimer Plan (QAP), a provincial plan that aims to reinforce the capacity of primary care services to detect, diagnose, and treat persons with dementia. Despite the provincial scope of the QAP, the diverse material and human resources across regions introduce variability in the interest, utility, and specific needs associated with these dashboards. OBJECTIVE The aim of this study was to assess the interest and utility of dashboards to support the QAP implementation, as well as to determine the needs for improving these aspects according to the perspectives of various types of professionals involved across regions. METHODS An evaluative study using qualitative methods was conducted within a collaborative research approach involving different stakeholders, including the ministerial advisor and the four project managers responsible for supporting the implementation of the QAP, as well as researchers/scientific advisors. To support these organizations, we developed tailored, 2-page paper dashboards, detailing quantitative data on the prevalence of dementia, the use of health services by persons with dementia, and achievements and challenges of the QAP implementation in each organization's jurisdiction. We then conducted 23 focus groups with the managers and leading clinicians involved in the implementation of the QAP of each regional health organization. Real-time notes were taken using a structured observation grid. Content analysis was conducted according to different regions (organizations with university mandates or nearby organizations, labeled "university/peripheral"; organizations for which only part of the territory is in rural areas, labeled "mixed"; and organizations in remote or isolated areas, labeled "remote/isolated") and according to different types of participants (managers, leading clinicians, and other participants). RESULTS Participants from organizations in all regions expressed interest in these dashboards and found them useful in several ways. However, they highlighted the need for indicators on orphan patients and other health care providers. Differences between regions were observed, particularly in the interest in continuity of care in university/peripheral regions and the need for diagnostic tools adapted to the culture in remote/isolated regions. CONCLUSIONS These dashboards support the implementation of an Alzheimer Plan and contribute to the emergence of a learning health care system culture. This project allows each region to increase its monitoring capacity for the implementation of the QAP and facilitates reflection among individuals locally carrying out the implementation. The perspectives expressed will guide the preparation of the next iteration of the dashboards.
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Affiliation(s)
- Genevieve Arsenault-Lapierre
- Center for Research and Expertise in Social Gerontology, Centre intégré universitaire de santé et de services sociaux du Centre-Ouest de l'Ile de Montréal, Côte Saint-Luc, QC, Canada
- Department of Family Medicine, McGill University, Montreal, QC, Canada
| | - Alexandra Lemay-Compagnat
- Lady Davis Institute for Medical Research, Centre intégré universitaire de santé et de services sociaux du Centre-Ouest de l'Ile de Montréal, Montreal, QC, Canada
| | - Maxime Guillette
- Department of Social Work, Sherbrooke University, Sherbrooke, QC, Canada
| | - Yves Couturier
- Department of Social Work, Sherbrooke University, Sherbrooke, QC, Canada
| | | | - Isabelle Dufour
- School of Nursing, Sherbrooke University, Sherbrooke, QC, Canada
- Center on Aging, Centre intégré universitaire de santé et de services sociaux de l'Estrie, Centre hospitalier universitaire de Sherbrooke, Sherbrooke, QC, Canada
| | - Eric Maubert
- Integrated University Health and Social Services Network of McGill University, Montreal, QC, Canada
| | - Christine Fournier
- Integrated University Health and Social Services Network of Université de Montréal, Montreal, QC, Canada
| | - Julie Denis
- Integrated University Health and Social Services Network of Université Laval, Quebec, QC, Canada
| | - Caroline Morin
- Integrated University Health and Social Services Network of Université de Sherbrooke, Sherbrooke, QC, Canada
| | - Isabelle Vedel
- Department of Family Medicine, McGill University, Montreal, QC, Canada
- Lady Davis Institute for Medical Research, Centre intégré universitaire de santé et de services sociaux du Centre-Ouest de l'Ile de Montréal, Montreal, QC, Canada
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Pinho X, Meijer W, de Graaf A. Deriving Treatment Decision Support From Dutch Electronic Health Records by Exploring the Applicability of a Precision Cohort-Based Procedure for Patients With Type 2 Diabetes Mellitus: Precision Cohort Study. Online J Public Health Inform 2024; 16:e51092. [PMID: 38691393 PMCID: PMC11097050 DOI: 10.2196/51092] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/21/2023] [Revised: 02/28/2024] [Accepted: 03/15/2024] [Indexed: 05/03/2024] Open
Abstract
BACKGROUND The rapidly increasing availability of medical data in electronic health records (EHRs) may contribute to the concept of learning health systems, allowing for better personalized care. Type 2 diabetes mellitus was chosen as the use case in this study. OBJECTIVE This study aims to explore the applicability of a recently developed patient similarity-based analytics approach based on EHRs as a candidate data analytical decision support tool. METHODS A previously published precision cohort analytics workflow was adapted for the Dutch primary care setting using EHR data from the Nivel Primary Care Database. The workflow consisted of extracting patient data from the Nivel Primary Care Database to retrospectively generate decision points for treatment change, training a similarity model, generating a precision cohort of the most similar patients, and analyzing treatment options. This analysis showed the treatment options that led to a better outcome for the precision cohort in terms of clinical readouts for glycemic control. RESULTS Data from 11,490 registered patients diagnosed with type 2 diabetes mellitus were extracted from the database. Treatment-specific filter cohorts of patient groups were generated, and the effect of past treatment choices in these cohorts was assessed separately for glycated hemoglobin and fasting glucose as clinical outcome variables. Precision cohorts were generated for several individual patients from the filter cohorts. Treatment options and outcome analyses were technically well feasible but in general had a lack of statistical power to demonstrate statistical significance for treatment options with better outcomes. CONCLUSIONS The precision cohort analytics workflow was successfully adapted for the Dutch primary care setting, proving its potential for use as a learning health system component. Although the approach proved technically well feasible, data size limitations need to be overcome before application for clinical decision support becomes realistically possible.
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Affiliation(s)
- Xavier Pinho
- Netherlands Organisation for Applied Scientific Research (TNO), Utrecht, Netherlands
| | - Willemijn Meijer
- Nivel, Nederlands Instituut voor Onderzoek van de Gezondheidszorg, Utrecht, Netherlands
| | - Albert de Graaf
- Netherlands Organisation for Applied Scientific Research (TNO), Utrecht, Netherlands
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Miranda da Silva M, Soares Lavareda Baixinho CR, Mendes Marques MF, Oliveira CS, de Moura Bubadué R, Franco de Souza SV, Cabral IE. Promoting knowledge translation: An ecosystem approach to evidence in health. Heliyon 2024; 10:e28871. [PMID: 38601590 PMCID: PMC11004558 DOI: 10.1016/j.heliyon.2024.e28871] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/06/2023] [Revised: 11/29/2023] [Accepted: 03/26/2024] [Indexed: 04/12/2024] Open
Abstract
The dissemination and implementation of evidence in health contexts have been a concern of several international organizations responsible for recommending actions to health policymakers. World Health Organization has been advocating for an ecosystem of evidence to improve clinical practice and health professional education. Thus, in this article, we address the challenges to developing the evidence ecosystem from the point of view of health professional education, considering the contexts of practice and teaching, focused on knowledge translation. There are three pivotal challenges: producing qualified knowledge; adequate communication of the synthesized evidence; and institutional policy to sustain the implemented evidence in continuous and updated flow. The evidence ecosystem helps to understand these flows between the production and implementation of knowledge, based on the capacity and resources of different health systems. It needs to be developed in the field of health professional education, feedback in the contexts of practice and teaching, to contribute to third-generation knowledge being used by different users of health services.
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Affiliation(s)
- Marcelle Miranda da Silva
- Escola de Enfermagem Ana Nery, Universidade Federal do Rio de Janeiro, 275 St Afonso Cavalcanti, Rio de Janeiro, RJ, Brazil
- Nursing Reserach, Innovation and Development Centre of Lisbon (CIDNUR), Av, Prof. Egas Moniz, 1600-190, Lisboa, Portugal
| | - Cristina Rosa Soares Lavareda Baixinho
- Nursing Reserach, Innovation and Development Centre of Lisbon (CIDNUR), Av, Prof. Egas Moniz, 1600-190, Lisboa, Portugal
- Department of Rehabilitation of Nursing, Lisbon Nursing School, Av. Prof. Egas Moniz, 1600-190, Lisboa, Portugal
| | - Maria Fátima Mendes Marques
- Nursing Reserach, Innovation and Development Centre of Lisbon (CIDNUR), Av, Prof. Egas Moniz, 1600-190, Lisboa, Portugal
- Department of Rehabilitation of Nursing, Lisbon Nursing School, Av. Prof. Egas Moniz, 1600-190, Lisboa, Portugal
| | - Claudia Sousa Oliveira
- Jean Piaget Higher School of Health, Jean Piaget Polytechnic Institute of the South, Jardim nº 1 do Enxerim, 8300-025, Silves, Portugal
| | - Renata de Moura Bubadué
- Escola de Enfermagem Ana Nery, Universidade Federal do Rio de Janeiro, 275 St Afonso Cavalcanti, Rio de Janeiro, RJ, Brazil
| | - Samhira Vieira Franco de Souza
- Escola de Enfermagem Ana Nery, Universidade Federal do Rio de Janeiro, 275 St Afonso Cavalcanti, Rio de Janeiro, RJ, Brazil
| | - Ivone Evangelista Cabral
- Escola de Enfermagem Ana Nery, Universidade Federal do Rio de Janeiro, 275 St Afonso Cavalcanti, Rio de Janeiro, RJ, Brazil
- Universidade do Estado do Rio de Janeiro, 157 Blvd. 28 de Setembro, Rio de Janeiro, RJ, Brazil
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Krishnamoorthy V, Harris R, Chowdhury AM, Bedoya A, Bartz R, Raghunathan K. Building Learning Healthcare Systems for Critical Care Medicine. Anesthesiology 2024; 140:817-823. [PMID: 38345893 DOI: 10.1097/aln.0000000000004847] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/13/2024]
Abstract
Learning healthcare systems are an evolving way of integrating informatics, analytics, and continuous improvement into daily practice in healthcare. This article discusses strategies to build learning healthcare systems for critical care medicine.
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Affiliation(s)
- Vijay Krishnamoorthy
- Department of Anesthesiology, Division of Critical Care Medicine; Critical Care and Perioperative Population Health Research Program, Department of Anesthesiology; and Department of Population Health Sciences, Duke University School of Medicine, Durham, North Carolina
| | - Ronald Harris
- Duke University School of Medicine, Durham, North Carolina
| | - Ananda M Chowdhury
- Department of Medicine, Division of Pulmonary, Allergy, and Critical Care Medicine, Duke University School of Medicine, Durham, North Carolina
| | - Armando Bedoya
- Department of Medicine, Division of Pulmonary, Allergy, and Critical Care Medicine, Duke University School of Medicine, Durham, North Carolina
| | - Raquel Bartz
- Department of Anesthesiology, Perioperative and Pain Medicine, Brigham and Women's Hospital, Boston, Massachusetts
| | - Karthik Raghunathan
- Department of Anesthesiology, Division of Critical Care Medicine; Critical Care and Perioperative Population Health Research Program, Department of Anesthesiology; and Department of Population Health Sciences, Duke University School of Medicine, Durham, North Carolina
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Davidson J, Malhotra Y, Shay R, Arunachalam A, Sink D, Barry JS, Meyers J. Building a NICU quality & safety infrastructure. Semin Perinatol 2024; 48:151902. [PMID: 38692996 DOI: 10.1016/j.semperi.2024.151902] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 05/03/2024]
Abstract
The American Academy of Pediatrics (AAP) Standards for Levels of Neonatal Care, published in 2023, highlights key components of a Neonatal Patient Safety and Quality Improvement Program (NPSQIP). A comprehensive Neonatal Intensive Care Unit (NICU) quality and safety infrastructure (QSI) is based on four foundational domains: quality improvement, quality assurance, safety culture, and clinical guidelines. This paper serves as an operational guide for NICU clinical leaders and quality champions to navigate these domains and develop their local QSI to include the AAP NPSQIP standards.
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Affiliation(s)
- Jessica Davidson
- Department of Pediatrics, University of Utah School of Medicine, Salt Lake City, UT, United States.
| | - Yogangi Malhotra
- Department of Pediatrics, Albert Einstein College of Medicine, Bronx, NY, United States
| | - Rebecca Shay
- Department of Pediatrics, University of Colorado School of Medicine, Aurora, CO, United States
| | - Athis Arunachalam
- Department of Pediatrics, Texas Childrens Hospital & Baylor College of Medicine, Houston, TX, United States
| | - David Sink
- Department of Pediatrics, University of Connecticut School of Medicine, Hartford, CT, United States
| | - James S Barry
- Department of Pediatrics, University of Colorado School of Medicine, Aurora, CO, United States
| | - Jeffrey Meyers
- Department of Pediatrics, University of Rochester School of Medicine & Dentistry, Rochester, NY, United States
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Smith CL, Fisher G, Dharmayani PNA, Wijekulasuriya S, Ellis LA, Spanos S, Dammery G, Zurynski Y, Braithwaite J. Progress with the Learning Health System 2.0: a rapid review of Learning Health Systems' responses to pandemics and climate change. BMC Med 2024; 22:131. [PMID: 38519952 PMCID: PMC10960489 DOI: 10.1186/s12916-024-03345-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/06/2023] [Accepted: 01/23/2024] [Indexed: 03/25/2024] Open
Abstract
BACKGROUND Pandemics and climate change each challenge health systems through increasing numbers and new types of patients. To adapt to these challenges, leading health systems have embraced a Learning Health System (LHS) approach, aiming to increase the efficiency with which data is translated into actionable knowledge. This rapid review sought to determine how these health systems have used LHS frameworks to both address the challenges posed by the COVID-19 pandemic and climate change, and to prepare for future disturbances, and thus transition towards the LHS2.0. METHODS Three databases (Embase, Scopus, and PubMed) were searched for peer-reviewed literature published in English in the five years to March 2023. Publications were included if they described a real-world LHS's response to one or more of the following: the COVID-19 pandemic, future pandemics, current climate events, future climate change events. Data were extracted and thematically analyzed using the five dimensions of the Institute of Medicine/Zurynski-Braithwaite's LHS framework: Science and Informatics, Patient-Clinician Partnerships, Continuous Learning Culture, Incentives, and Structure and Governance. RESULTS The search yielded 182 unique publications, four of which reported on LHSs and climate change. Backward citation tracking yielded 13 additional pandemic-related publications. None of the climate change-related papers met the inclusion criteria. Thirty-two publications were included after full-text review. Most were case studies (n = 12, 38%), narrative descriptions (n = 9, 28%) or empirical studies (n = 9, 28%). Science and Informatics (n = 31, 97%), Continuous Learning Culture (n = 26, 81%), Structure and Governance (n = 23, 72%) were the most frequently discussed LHS dimensions. Incentives (n = 21, 66%) and Patient-Clinician Partnerships (n = 18, 56%) received less attention. Twenty-nine papers (91%) discussed benefits or opportunities created by pandemics to furthering the development of an LHS, compared to 22 papers (69%) that discussed challenges. CONCLUSIONS An LHS 2.0 approach appears well-suited to responding to the rapidly changing and uncertain conditions of a pandemic, and, by extension, to preparing health systems for the effects of climate change. LHSs that embrace a continuous learning culture can inform patient care, public policy, and public messaging, and those that wisely use IT systems for decision-making can more readily enact surveillance systems for future pandemics and climate change-related events. TRIAL REGISTRATION PROSPERO pre-registration: CRD42023408896.
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Affiliation(s)
- Carolynn L Smith
- Centre for Healthcare Resilience and Implementation Science, Australian Institute of Health Innovation, Macquarie University, 75 Talavera Road, North Ryde 2113, Sydney, Australia.
- NHMRC Partnership Centre for Health System Sustainability, Macquarie University, 75 Talavera Road, North Ryde 2113, Sydney, Australia.
| | - Georgia Fisher
- Centre for Healthcare Resilience and Implementation Science, Australian Institute of Health Innovation, Macquarie University, 75 Talavera Road, North Ryde 2113, Sydney, Australia
| | - Putu Novi Arfirsta Dharmayani
- Centre for Healthcare Resilience and Implementation Science, Australian Institute of Health Innovation, Macquarie University, 75 Talavera Road, North Ryde 2113, Sydney, Australia
- NHMRC Partnership Centre for Health System Sustainability, Macquarie University, 75 Talavera Road, North Ryde 2113, Sydney, Australia
| | - Shalini Wijekulasuriya
- Centre for Healthcare Resilience and Implementation Science, Australian Institute of Health Innovation, Macquarie University, 75 Talavera Road, North Ryde 2113, Sydney, Australia
- NHMRC Partnership Centre for Health System Sustainability, Macquarie University, 75 Talavera Road, North Ryde 2113, Sydney, Australia
| | - Louise A Ellis
- Centre for Healthcare Resilience and Implementation Science, Australian Institute of Health Innovation, Macquarie University, 75 Talavera Road, North Ryde 2113, Sydney, Australia
- NHMRC Partnership Centre for Health System Sustainability, Macquarie University, 75 Talavera Road, North Ryde 2113, Sydney, Australia
| | - Samantha Spanos
- Centre for Healthcare Resilience and Implementation Science, Australian Institute of Health Innovation, Macquarie University, 75 Talavera Road, North Ryde 2113, Sydney, Australia
| | - Genevieve Dammery
- Centre for Healthcare Resilience and Implementation Science, Australian Institute of Health Innovation, Macquarie University, 75 Talavera Road, North Ryde 2113, Sydney, Australia
- NHMRC Partnership Centre for Health System Sustainability, Macquarie University, 75 Talavera Road, North Ryde 2113, Sydney, Australia
| | - Yvonne Zurynski
- Centre for Healthcare Resilience and Implementation Science, Australian Institute of Health Innovation, Macquarie University, 75 Talavera Road, North Ryde 2113, Sydney, Australia
- NHMRC Partnership Centre for Health System Sustainability, Macquarie University, 75 Talavera Road, North Ryde 2113, Sydney, Australia
| | - Jeffrey Braithwaite
- Centre for Healthcare Resilience and Implementation Science, Australian Institute of Health Innovation, Macquarie University, 75 Talavera Road, North Ryde 2113, Sydney, Australia
- NHMRC Partnership Centre for Health System Sustainability, Macquarie University, 75 Talavera Road, North Ryde 2113, Sydney, Australia
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11
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Bajgain KT, Mendoza J, Naqvi F, Aghajafari F, Tang K, Zwicker J, Santana MJ. Prioritizing Patient Reported Outcome Measures (PROMs) to use in the clinical care of youth living with mental health concerns: a nominal group technique study. J Patient Rep Outcomes 2024; 8:20. [PMID: 38381258 PMCID: PMC10881912 DOI: 10.1186/s41687-024-00694-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/16/2023] [Accepted: 01/13/2024] [Indexed: 02/22/2024] Open
Abstract
BACKGROUND In the past few decades, particularly in the mental health setting, there has been growing interest in using Patient Reported Outcome Measures (PROMs) to assess the efficacy of the treatments in healthcare systems. Despite recent initiatives for global harmonization, there remains a lack of consensus on which PROMs are best practice and appropriate. Engagement of the service users, such as patients and family members/caregivers, is vital at this stage to ensure the selected PROMs are feasible, relevant, and acceptable to them. This study aimed to prioritize PROMs by youth and family/caregiver based on feasibility, relevance, and overall importance to be used in the clinical care of youth living with anxiety and/or depression. METHODS Ten validated and widely used PROMs were presented to the patients and family/caregivers. Nominal group techniques were employed to prioritize the PROMs based on feasibility, relevance, and overall importance. RESULTS For patients and families/caregivers, the PROMs, Revised Child Anxiety and Depression Scale (RCAD 25), and The Young Person's Core (YP-CORE) were the highest priorities. Both felt that RCAD 25 was comprehensive, short, easy, and quick to complete, whereas regarding YP-CORE, patients and family/caregivers thought it was also short and relevant. Due to some specific concerns, the Strength and Difficulties Questionnaire and Child Health Questionnaire were the lowest prioritized by patients and family/caregivers. CONCLUSION It is of utmost importance that patient's and family/caregivers' voices or opinions are considered while selecting and implementing PROMs in mental health settings. Our study provides practical recommendations around measures best suited to achieve this.
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Affiliation(s)
- Kalpana Thapa Bajgain
- Department of Community Health Sciences, University of Calgary, Calgary, Alberta, Canada
| | - Justino Mendoza
- General Psychology, Capella University, Minneapolis, MN, USA
| | - Farwa Naqvi
- Department of Community Health Sciences, University of Calgary, Calgary, Alberta, Canada
| | - Fariba Aghajafari
- Department of Medicine and Community Health Science, University of Calgary, Calgary, Alberta, Canada
| | - Karen Tang
- Department of Medicine and Community Health Science, University of Calgary, Calgary, Alberta, Canada
| | - Jennifer Zwicker
- School of Public Policy, University of Calgary, Calgary, Alberta, Canada
- Faculty of Kinesiology, University of Calgary, Calgary, Alberta, Canada
| | - Maria-Jose Santana
- Department of Community Health Sciences, University of Calgary, Calgary, Alberta, Canada.
- Department of Pediatrics, University of Calgary, Calgary, Alberta, Canada.
- Alberta Children's Hospital, 2888 Shaganappi Trail NW, Calgary, Canada.
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12
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Smeddinck JD, Hussein R, Bull C, Foley T, van Gils M. Editorial: Supporting sustainable behavior change and empowerment in ubiquitous and learning health systems. Front Digit Health 2024; 6:1367567. [PMID: 38352053 PMCID: PMC10861645 DOI: 10.3389/fdgth.2024.1367567] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/09/2024] [Accepted: 01/15/2024] [Indexed: 02/16/2024] Open
Affiliation(s)
- Jan David Smeddinck
- Ludwig Boltzmann Institute for Digital Health and Prevention, Ludwig Boltzmann Gesellschaft, Salzburg, Austria
- Human-Centered Ubiquitous Media Research Group, LMU Munich, Munich, Germany
- Open Lab, School of Computing, Newcastle University, Newcastle upon Tyne, United Kingdom
| | - Rada Hussein
- Ludwig Boltzmann Institute for Digital Health and Prevention, Ludwig Boltzmann Gesellschaft, Salzburg, Austria
| | - Christopher Bull
- Open Lab, School of Computing, Newcastle University, Newcastle upon Tyne, United Kingdom
| | - Tom Foley
- Population Health Sciences Institute for Foley, Newcastle University, Newcastle upon Tyne, United Kingdom
- School of Medicine, University College Dublin, Dublin, Ireland
| | - Mark van Gils
- Faculty of Medicine and Health Technology, Tampere University, Tampere, Finland
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13
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McDonald PL, Foley TJ, Verheij R, Braithwaite J, Rubin J, Harwood K, Phillips J, Gilman S, van der Wees PJ. Data to knowledge to improvement: creating the learning health system. BMJ 2024; 384:e076175. [PMID: 38272498 PMCID: PMC10809034 DOI: 10.1136/bmj-2023-076175] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 01/27/2024]
Affiliation(s)
| | - Tom J Foley
- Newcastle University, Newcastle upon Tyne, UK
- University College Dublin, Dublin, Ireland
- Health Service Executive, Donegal, Ireland
| | - Robert Verheij
- Netherlands Institute of Health Services Research (NIVEL), Utrecht, Netherlands
- Tranzo, Department of Social and Behavioural Sciences, Tilburg University, Tilburg, Netherlands
- Dutch National Healthcare Institute, Diemen, Netherlands
| | | | | | | | - Jessica Phillips
- Translational Health Sciences, Department of Clinical Research and Leadership, George Washington University, Washington, DC, USA
| | - Sarah Gilman
- Translational Health Sciences, Department of Clinical Research and Leadership, George Washington University, Washington, DC, USA
| | - Philip J van der Wees
- George Washington University, Washington, DC, USA
- Radboud University Medical Center, Nijmegen, Netherlands
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14
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Evans HG, Murphy MF, Foy R, Dhiman P, Green L, Kotze A, von Neree L, Palmer AJ, Robinson SE, Shah A, Tomini F, Trompeter S, Warnakulasuriya S, Wong WK, Stanworth SJ. Harnessing the potential of data-driven strategies to optimise transfusion practice. Br J Haematol 2024; 204:74-85. [PMID: 37964471 DOI: 10.1111/bjh.19158] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/05/2023] [Revised: 09/24/2023] [Accepted: 10/03/2023] [Indexed: 11/16/2023]
Abstract
No one doubts the significant variation in the practice of transfusion medicine. Common examples are the variability in transfusion thresholds and the use of tranexamic acid for surgery with likely high blood loss despite evidence-based standards. There is a long history of applying different strategies to address this variation, including education, clinical guidelines, audit and feedback, but the effectiveness and cost-effectiveness of these initiatives remains unclear. Advances in computerised decision support systems and the application of novel electronic capabilities offer alternative approaches to improving transfusion practice. In England, the National Institute for Health and Care Research funded a Blood and Transplant Research Unit (BTRU) programme focussing on 'A data-enabled programme of research to improve transfusion practices'. The overarching aim of the BTRU is to accelerate the development of data-driven methods to optimise the use of blood and transfusion alternatives, and to integrate them within routine practice to improve patient outcomes. One particular area of focus is implementation science to address variation in practice.
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Affiliation(s)
- H G Evans
- NIHR Blood and Transplant Research Unit in Data Driven Transfusion Practice, Radcliffe Department of Medicine, University of Oxford, Oxford, UK
| | - M F Murphy
- NIHR Blood and Transplant Research Unit in Data Driven Transfusion Practice, Radcliffe Department of Medicine, University of Oxford, Oxford, UK
- Oxford University Hospitals NHS Foundation Trust, John Radcliffe Hospital, Oxford, UK
- NHS Blood and Transplant, John Radcliffe Hospital, Oxford, UK
| | - R Foy
- Leeds Institute of Health Sciences, University of Leeds, Leeds, UK
| | - P Dhiman
- Centre for Statistics in Medicine, Botnar Research Centre, Oxford, UK
| | - L Green
- Blizard Institute, Queen Mary University of London, London, UK
- Barts Health NHS Trust, London, UK
- NHS Blood and Transplant, London, UK
| | - A Kotze
- Leeds Teaching Hospitals, Leeds, UK
| | - L von Neree
- University College London Hospitals NHS Foundation Trust, London, UK
| | - A J Palmer
- Nuffield Orthopaedic Centre, Oxford University NHS Foundation Trust, Oxford, UK
| | - S E Robinson
- Guy's and St Thomas' NHS Foundation Trust, London, UK
| | - A Shah
- Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, UK
| | - F Tomini
- Queen Mary University of London, London, UK
| | - S Trompeter
- University College London Hospitals NHS Foundation Trust, London, UK
- University College London, London, UK
| | - S Warnakulasuriya
- University College London Hospitals NHS Foundation Trust, London, UK
- University College London, London, UK
| | - W K Wong
- Cambridge University Hospitals NHS Foundation Trust, Cambridge, UK
| | - S J Stanworth
- NIHR Blood and Transplant Research Unit in Data Driven Transfusion Practice, Radcliffe Department of Medicine, University of Oxford, Oxford, UK
- Oxford University Hospitals NHS Foundation Trust, John Radcliffe Hospital, Oxford, UK
- NHS Blood and Transplant, John Radcliffe Hospital, Oxford, UK
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15
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Welch LC, Brewer SK, Schleyer T, Daudelin D, Paranal R, Hunt JD, Dozier AM, Perry A, Cabrera AB, Gatto CL. Learning health system benefits: Development and initial validation of a framework. Learn Health Syst 2024; 8:e10380. [PMID: 38249854 PMCID: PMC10797574 DOI: 10.1002/lrh2.10380] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/28/2023] [Revised: 06/14/2023] [Accepted: 06/22/2023] [Indexed: 01/23/2024] Open
Abstract
Introduction Implementation of research findings in clinical practice often is not realized or only partially achieved, and if so, with a significant delay. Learning health systems (LHSs) hold promise to overcome this problem by embedding clinical research and evidence-based best practices into care delivery, enabling innovation and continuous improvement. Implementing an LHS is a complex process that requires participation and resources of a wide range of stakeholders, including healthcare leaders, clinical providers, patients and families, payers, and researchers. Engaging these stakeholders requires communicating clear, tangible value propositions. Existing models identify broad categories of benefits but do not explicate the full range of benefits or ways they can manifest in different organizations. Methods To develop such a framework, a working group with representatives from six Clinical and Translational Science Award (CTSA) hubs reviewed existing literature on LHS characteristics, models, and goals; solicited expert input; and applied the framework to their local LHS experiences. Results The Framework of LHS Benefits includes six categories of benefits (quality, safety, equity, patient satisfaction, reputation, and value) relevant for a range of stakeholders and defines key concepts within each benefit. Applying the framework to five LHS case examples indicated preliminary face validity across varied LHS approaches and revealed three dimensions in which the framework is relevant: defining goals of individual LHS projects, facilitating collaboration based on shared values, and establishing guiding tenets of an LHS program or mission. Conclusion The framework can be used to communicate the value of an LHS to different stakeholders across varied contexts and purposes, and to identify future organizational priorities. Further validation will contribute to the framework's evolution and support its potential to inform the development of tools to evaluate LHS impact.
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Affiliation(s)
- Lisa C. Welch
- Tufts Clinical and Translational Science InstituteTufts UniversityBostonMassachusettsUSA
| | - Sarah K. Brewer
- Tufts Clinical and Translational Science InstituteTufts UniversityBostonMassachusettsUSA
| | - Titus Schleyer
- Center for Biomedical InformaticsRegenstrief InstituteIndianapolisIndianaUSA
- Indiana Clinical and Translational Sciences InstituteIndiana UniversityIndianapolisIndianaUSA
| | - Denise Daudelin
- Tufts Clinical and Translational Science InstituteTufts UniversityBostonMassachusettsUSA
| | - Rechelle Paranal
- South Carolina Clinical and Translational Research Institute, Medical University of South CarolinaCharlestonSouth CarolinaUSA
| | - Joe D. Hunt
- Indiana Clinical and Translational Sciences InstituteIndiana UniversityIndianapolisIndianaUSA
| | - Ann M. Dozier
- University of Rochester Clinical and Translational Science Institute, University of RochesterRochesterNew YorkUSA
| | - Anna Perry
- Wake Forest Clinical and Translational Science Institute, Wake Forest University School of MedicineWinston‐SalemNorth CarolinaUSA
| | - Alyssa B. Cabrera
- Tufts Clinical and Translational Science InstituteTufts UniversityBostonMassachusettsUSA
| | - Cheryl L. Gatto
- Vanderbilt Institute for Clinical and Translational Research, Vanderbilt University Medical CenterNashvilleTennesseeUSA
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16
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Friedman CP, Lomotan EA, Richardson JE, Ridgeway JL. Socio-technical infrastructure for a learning health system. Learn Health Syst 2024; 8:e10405. [PMID: 38249851 PMCID: PMC10797563 DOI: 10.1002/lrh2.10405] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/11/2023] [Accepted: 12/12/2023] [Indexed: 01/23/2024] Open
Affiliation(s)
- Charles P. Friedman
- Department of Learning Health SciencesUniversity of MichiganAnn ArborMichiganUSA
| | - Edwin A. Lomotan
- Center for Evidence and Practice ImprovementAgency for Healthcare Research and QualityRockvilleMarylandUSA
| | | | - Jennifer L. Ridgeway
- Division of Health Care Delivery Research, Robert D. and Patricia E. Kern Center for the Science of Health Care DeliveryMayo ClinicRochesterMinnesotaUSA
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17
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van Velzen M, de Graaf-Waar HI, Ubert T, van der Willigen RF, Muilwijk L, Schmitt MA, Scheper MC, van Meeteren NLU. 21st century (clinical) decision support in nursing and allied healthcare. Developing a learning health system: a reasoned design of a theoretical framework. BMC Med Inform Decis Mak 2023; 23:279. [PMID: 38053104 PMCID: PMC10699040 DOI: 10.1186/s12911-023-02372-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/23/2023] [Accepted: 11/09/2023] [Indexed: 12/07/2023] Open
Abstract
In this paper, we present a framework for developing a Learning Health System (LHS) to provide means to a computerized clinical decision support system for allied healthcare and/or nursing professionals. LHSs are well suited to transform healthcare systems in a mission-oriented approach, and is being adopted by an increasing number of countries. Our theoretical framework provides a blueprint for organizing such a transformation with help of evidence based state of the art methodologies and techniques to eventually optimize personalized health and healthcare. Learning via health information technologies using LHS enables users to learn both individually and collectively, and independent of their location. These developments demand healthcare innovations beyond a disease focused orientation since clinical decision making in allied healthcare and nursing is mainly based on aspects of individuals' functioning, wellbeing and (dis)abilities. Developing LHSs depends heavily on intertwined social and technological innovation, and research and development. Crucial factors may be the transformation of the Internet of Things into the Internet of FAIR data & services. However, Electronic Health Record (EHR) data is in up to 80% unstructured including free text narratives and stored in various inaccessible data warehouses. Enabling the use of data as a driver for learning is challenged by interoperability and reusability.To address technical needs, key enabling technologies are suitable to convert relevant health data into machine actionable data and to develop algorithms for computerized decision support. To enable data conversions, existing classification and terminology systems serve as definition providers for natural language processing through (un)supervised learning.To facilitate clinical reasoning and personalized healthcare using LHSs, the development of personomics and functionomics are useful in allied healthcare and nursing. Developing these omics will be determined via text and data mining. This will focus on the relationships between social, psychological, cultural, behavioral and economic determinants, and human functioning.Furthermore, multiparty collaboration is crucial to develop LHSs, and man-machine interaction studies are required to develop a functional design and prototype. During development, validation and maintenance of the LHS continuous attention for challenges like data-drift, ethical, technical and practical implementation difficulties is required.
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Affiliation(s)
- Mark van Velzen
- Data Supported Healthcare: Data-Science unit, Research Center Innovations in care, Rotterdam University of Applied Sciences, Rotterdam, the Netherlands.
- Department of Anesthesiology, Erasmus Medical Center, Rotterdam, the Netherlands.
| | - Helen I de Graaf-Waar
- Data Supported Healthcare: Data-Science unit, Research Center Innovations in care, Rotterdam University of Applied Sciences, Rotterdam, the Netherlands
- Department of Anesthesiology, Erasmus Medical Center, Rotterdam, the Netherlands
| | - Tanja Ubert
- Institute for Communication, media and information Technology, Rotterdam University of Applied Sciences, Rotterdam, the Netherlands
| | - Robert F van der Willigen
- Institute for Communication, media and information Technology, Rotterdam University of Applied Sciences, Rotterdam, the Netherlands
| | - Lotte Muilwijk
- Data Supported Healthcare: Data-Science unit, Research Center Innovations in care, Rotterdam University of Applied Sciences, Rotterdam, the Netherlands
- Institute for Communication, media and information Technology, Rotterdam University of Applied Sciences, Rotterdam, the Netherlands
| | - Maarten A Schmitt
- Data Supported Healthcare: Data-Science unit, Research Center Innovations in care, Rotterdam University of Applied Sciences, Rotterdam, the Netherlands
| | - Mark C Scheper
- Data Supported Healthcare: Data-Science unit, Research Center Innovations in care, Rotterdam University of Applied Sciences, Rotterdam, the Netherlands
- Department of Anesthesiology, Erasmus Medical Center, Rotterdam, the Netherlands
- Allied Health professions, faculty of medicine and science, Macquarrie University, Sydney, Australia
| | - Nico L U van Meeteren
- Department of Anesthesiology, Erasmus Medical Center, Rotterdam, the Netherlands
- Top Sector Life Sciences and Health (Health~Holland), The Hague, the Netherlands
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18
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Reid RJ, Greene SM. Gathering speed and countering tensions in the rapid learning health system. Learn Health Syst 2023. [DOI: 10.1002/lrh2.10358] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/04/2023] Open
Affiliation(s)
- Robert J. Reid
- Institute for Better Health, Trillium Health Partners Mississauga Ontario Canada
- Dalla Lana School of Public Health University of Toronto Toronto Ontario Canada
| | - Sarah M. Greene
- National Academy of Medicine Washington District of Columbia USA
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