1
|
Gazzarata R, Almeida J, Lindsköld L, Cangioli G, Gaeta E, Fico G, Chronaki CE. HL7 Fast Healthcare Interoperability Resources (HL7 FHIR) in digital healthcare ecosystems for chronic disease management: Scoping review. Int J Med Inform 2024; 189:105507. [PMID: 38870885 DOI: 10.1016/j.ijmedinf.2024.105507] [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: 01/07/2024] [Revised: 05/14/2024] [Accepted: 05/27/2024] [Indexed: 06/15/2024]
Abstract
BACKGROUND The prevalence of chronic diseases has shifted the burden of disease from incidental acute inpatient admissions to long-term coordinated care across healthcare institutions and the patient's home. Digital healthcare ecosystems emerge to target increasing healthcare costs and invest in standard Application Programming Interfaces (API), such as HL7 Fast Healthcare Interoperability Resources (HL7 FHIR) for trusted data flows. OBJECTIVES This scoping review assessed the role and impact of HL7 FHIR and associated Implementation Guides (IGs) in digital healthcare ecosystems focusing on chronic disease management. METHODS To study trends and developments relevant to HL7 FHIR, a scoping review of the scientific and gray English literature from 2017 to 2023 was used. RESULTS The selection of 93 of 524 scientific papers reviewed in English indicates that the popularity of HL7 FHIR as a robust technical interface standard for the health sector has been steadily rising since its inception in 2010, reaching a peak in 2021. Digital Health applications use HL7 FHIR in cancer (45 %), cardiovascular disease (CVD) (more than 15 %), and diabetes (almost 15 %). The scoping review revealed that references to HL7 FHIR IGs are limited to ∼ 20 % of articles reviewed. HL7 FHIR R4 was most frequently referenced when the HL7 FHIR version was mentioned. In HL7 FHIR IGs registries and the internet, we found 35 HL7 FHIR IGs addressing chronic disease management, i.e., cancer (40 %), chronic disease management (25 %), and diabetes (20 %). HL7 FHIR IGs frequently complement the information in the article. CONCLUSIONS HL7 FHIR matures with each revision of the standard as HL7 FHIR IGs are developed with validated data sets, common shared HL7 FHIR resources, and supporting tools. Referencing HL7 FHIR IGs cataloged in official registries and in scientific publications is recommended to advance data quality and facilitate mutual learning in growing digital healthcare ecosystems that nurture interoperability in digital health innovation.
Collapse
Affiliation(s)
- Roberta Gazzarata
- HL7 Europe Foundation, 38-40 Square de Meeus, Brussels, 1000, Belgium; Healthropy Srl, Corso Vittorio Veneto 14B, Savona, 17100, Italy.
| | - Joao Almeida
- HL7 Europe Foundation, 38-40 Square de Meeus, Brussels, 1000, Belgium; MEDCIDS - Faculty of Medicine of University of Porto, Porto, Portugal; PDH - Pharma Data Hub, Porto, Portugal.
| | - Lars Lindsköld
- European Federation for Medical Informatics, Ch de Maillefer 37, CH-1052 Le Mont-sur-Lausanne, Switzerland; SciLifeLab Datacenter, University of Uppsala, S-752 37 Uppsala, Sweden.
| | - Giorgio Cangioli
- HL7 Europe Foundation, 38-40 Square de Meeus, Brussels, 1000, Belgium.
| | - Eugenio Gaeta
- Life Supporting Technologies, Universidad Politécnica de Madrid, Avenida Complutense 30, 28040 Madrid, Spain.
| | - Giuseppe Fico
- Life Supporting Technologies, Universidad Politécnica de Madrid, Avenida Complutense 30, 28040 Madrid, Spain.
| | - Catherine E Chronaki
- HL7 Europe Foundation, 38-40 Square de Meeus, Brussels, 1000, Belgium; European Federation for Medical Informatics, Ch de Maillefer 37, CH-1052 Le Mont-sur-Lausanne, Switzerland.
| |
Collapse
|
2
|
Balch JA, Ruppert MM, Loftus TJ, Guan Z, Ren Y, Upchurch GR, Ozrazgat-Baslanti T, Rashidi P, Bihorac A. Machine Learning-Enabled Clinical Information Systems Using Fast Healthcare Interoperability Resources Data Standards: Scoping Review. JMIR Med Inform 2023; 11:e48297. [PMID: 37646309 PMCID: PMC10468818 DOI: 10.2196/48297] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/19/2023] [Revised: 06/15/2023] [Accepted: 06/17/2023] [Indexed: 09/01/2023] Open
Abstract
Background Machine learning-enabled clinical information systems (ML-CISs) have the potential to drive health care delivery and research. The Fast Healthcare Interoperability Resources (FHIR) data standard has been increasingly applied in developing these systems. However, methods for applying FHIR to ML-CISs are variable. Objective This study evaluates and compares the functionalities, strengths, and weaknesses of existing systems and proposes guidelines for optimizing future work with ML-CISs. Methods Embase, PubMed, and Web of Science were searched for articles describing machine learning systems that were used for clinical data analytics or decision support in compliance with FHIR standards. Information regarding each system's functionality, data sources, formats, security, performance, resource requirements, scalability, strengths, and limitations was compared across systems. Results A total of 39 articles describing FHIR-based ML-CISs were divided into the following three categories according to their primary focus: clinical decision support systems (n=18), data management and analytic platforms (n=10), or auxiliary modules and application programming interfaces (n=11). Model strengths included novel use of cloud systems, Bayesian networks, visualization strategies, and techniques for translating unstructured or free-text data to FHIR frameworks. Many intelligent systems lacked electronic health record interoperability and externally validated evidence of clinical efficacy. Conclusions Shortcomings in current ML-CISs can be addressed by incorporating modular and interoperable data management, analytic platforms, secure interinstitutional data exchange, and application programming interfaces with adequate scalability to support both real-time and prospective clinical applications that use electronic health record platforms with diverse implementations.
Collapse
Affiliation(s)
- Jeremy A Balch
- Department of Surgery, University of Florida Health, Gainesville, FL, United States
- Intelligent Critical Care Center, University of Florida, Gainesville, FL, United States
| | - Matthew M Ruppert
- Intelligent Critical Care Center, University of Florida, Gainesville, FL, United States
- Department of Medicine, University of Florida, Gainesville, FL, United States
| | - Tyler J Loftus
- Department of Surgery, University of Florida Health, Gainesville, FL, United States
- Intelligent Critical Care Center, University of Florida, Gainesville, FL, United States
| | - Ziyuan Guan
- Intelligent Critical Care Center, University of Florida, Gainesville, FL, United States
- Department of Medicine, University of Florida, Gainesville, FL, United States
| | - Yuanfang Ren
- Intelligent Critical Care Center, University of Florida, Gainesville, FL, United States
- Department of Medicine, University of Florida, Gainesville, FL, United States
| | - Gilbert R Upchurch
- Department of Surgery, University of Florida Health, Gainesville, FL, United States
| | - Tezcan Ozrazgat-Baslanti
- Intelligent Critical Care Center, University of Florida, Gainesville, FL, United States
- Department of Medicine, University of Florida, Gainesville, FL, United States
| | - Parisa Rashidi
- Intelligent Critical Care Center, University of Florida, Gainesville, FL, United States
- Department of Biomedical Engineering, University of Florida, Gainesville, FL, United States
| | - Azra Bihorac
- Intelligent Critical Care Center, University of Florida, Gainesville, FL, United States
- Department of Medicine, University of Florida, Gainesville, FL, United States
| |
Collapse
|
3
|
Nan J, Xu LQ. Designing Interoperable Health Care Services Based on Fast Healthcare Interoperability Resources: Literature Review. JMIR Med Inform 2023; 11:e44842. [PMID: 37603388 PMCID: PMC10477925 DOI: 10.2196/44842] [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: 12/05/2022] [Revised: 04/07/2023] [Accepted: 07/10/2023] [Indexed: 08/22/2023] Open
Abstract
BACKGROUND With the advent of the digital economy and the aging population, the demand for diversified health care services and innovative care delivery models has been overwhelming. This trend has accelerated the urgency to implement effective and efficient data exchange and service interoperability, which underpins coordinated care services among tiered health care institutions, improves the quality of oversight of regulators, and provides vast and comprehensive data collection to support clinical medicine and health economics research, thus improving the overall service quality and patient satisfaction. To meet this demand and facilitate the interoperability of IT systems of stakeholders, after years of preparation, Health Level 7 formally introduced, in 2014, the Fast Healthcare Interoperability Resources (FHIR) standard. It has since continued to evolve. FHIR depends on the Implementation Guide (IG) to ensure feasibility and consistency while developing an interoperable health care service. The IG defines rules with associated documentation on how FHIR resources are used to tackle a particular problem. However, a gap remains between IGs and the process of building actual services because IGs are rules without specifying concrete methods, procedures, or tools. Thus, stakeholders may feel it nontrivial to participate in the ecosystem, giving rise to the need for a more actionable practice guideline (PG) for promoting FHIR's fast adoption. OBJECTIVE This study aimed to propose a general FHIR PG to facilitate stakeholders in the health care ecosystem to understand FHIR and quickly develop interoperable health care services. METHODS We selected a collection of FHIR-related papers about the latest studies or use cases on designing and building FHIR-based interoperable health care services and tagged each use case as belonging to 1 of the 3 dominant innovation feature groups that are also associated with practice stages, that is, data standardization, data management, and data integration. Next, we reviewed each group's detailed process and key techniques to build respective care services and collate a complete FHIR PG. Finally, as an example, we arbitrarily selected a use case outside the scope of the reviewed papers and mapped it back to the FHIR PG to demonstrate the effectiveness and generalizability of the PG. RESULTS The FHIR PG includes 2 core elements: one is a practice design that defines the responsibilities of stakeholders and outlines the complete procedure from data to services, and the other is a development architecture for practice design, which lists the available tools for each practice step and provides direct and actionable recommendations. CONCLUSIONS The FHIR PG can bridge the gap between IGs and the process of building actual services by proposing actionable methods, procedures, and tools. It assists stakeholders in identifying participants' roles, managing the scope of responsibilities, and developing relevant modules, thus helping promote FHIR-based interoperable health care services.
Collapse
Affiliation(s)
- Jingwen Nan
- Health IT Research, China Mobile (Chengdu) Industrial Research Institute, Chengdu, China
| | - Li-Qun Xu
- Health IT Research, China Mobile (Chengdu) Industrial Research Institute, Chengdu, China
| |
Collapse
|
4
|
Jenssen BP, Kelleher S, Karavite DJ, Nekrasova E, Thayer JG, Ratwani R, Shea JA, Nabi-Burza E, Drehmer JE, Winickoff JP, Grundmeier RW, Schnoll RA, Fiks AG. A Clinical Decision Support System for Motivational Messaging and Tobacco Cessation Treatment for Parents: Pilot Evaluation of Use and Acceptance. Appl Clin Inform 2023; 14:439-447. [PMID: 36972687 PMCID: PMC10247306 DOI: 10.1055/a-2062-9627] [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: 12/19/2022] [Accepted: 03/23/2023] [Indexed: 03/29/2023] Open
Abstract
BACKGROUND Research is needed to identify how clinical decision support (CDS) systems can support communication about and engagement with tobacco use treatment in pediatric settings for parents who smoke. We developed a CDS system that identifies parents who smoke, delivers motivational messages to start treatment, connects parents to treatment, and supports pediatrician-parent discussion. OBJECTIVE The objective of this study is to assess the performance of this system in clinical practice, including receipt of motivational messages and tobacco use treatment acceptance rates. METHODS The system was evaluated at one large pediatric practice through a single-arm pilot study from June to November 2021. We collected data on the performance of the CDS system for all parents. Additionally, we surveyed a sample of parents immediately after the clinical encounter who used the system and reported smoking. Measures were: (1) the parent remembered the motivational message, (2) the pediatrician reinforced the message, and (3) treatment acceptance rates. Treatments included nicotine replacement therapy, quitline referral (phone counseling), and/or SmokefreeTXT referral (text message counseling). We described survey response rates overall and with 95% confidence intervals (CIs). RESULTS During the entire study period, 8,488 parents completed use of the CDS: 9.3% (n = 786) reported smoking and 48.2% (n = 379) accepted at least one treatment. A total of 102 parents who smoke who used the system were approached to survey 100 parents (98% response rate). Most parents self-identified as female (84%), aged 25 to 34 years (56%), and Black/African American (94%), and had children with Medicaid insurance (95%). Of parents surveyed, 54% accepted at least one treatment option. Most parents recalled the motivational message (79%; 95% CI: 71-87%), and 31% (95% CI: 19-44%) reported that the pediatrician reinforced the motivational message. CONCLUSION A CDS system to support parental tobacco use treatment in pediatric primary care enhanced motivational messaging about smoking cessation and evidence-based treatment initiation.
Collapse
Affiliation(s)
- Brian P. Jenssen
- Department of Pediatrics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania, United States
- Clinical Futures, PolicyLab, and The Possibilities Project, Children's Hospital of Philadelphia, Pennsilvania, United States
- Department of Biomedical and Health Informatics, Children's Hospital of Philadelphia, Philadelphia, Pennsylvania, United States
| | - Shannon Kelleher
- Clinical Futures, PolicyLab, and The Possibilities Project, Children's Hospital of Philadelphia, Pennsilvania, United States
| | - Dean J. Karavite
- Department of Biomedical and Health Informatics, Children's Hospital of Philadelphia, Philadelphia, Pennsylvania, United States
| | - Ekaterina Nekrasova
- Clinical Futures, PolicyLab, and The Possibilities Project, Children's Hospital of Philadelphia, Pennsilvania, United States
| | - Jeritt G. Thayer
- Department of Biomedical and Health Informatics, Children's Hospital of Philadelphia, Philadelphia, Pennsylvania, United States
| | - Raj Ratwani
- MedStar Health National Center for Human Factors in Healthcare, Washington, District of Columbia, United States
| | - Judy A. Shea
- Department of Medicine, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania, United States
| | - Emara Nabi-Burza
- Division of General Academic Pediatrics, Tobacco Research and Treatment Center, Massachusetts General Hospital for Children, Boston, Massachusetts, United States
| | - Jeremy E. Drehmer
- Division of General Academic Pediatrics, Tobacco Research and Treatment Center, Massachusetts General Hospital for Children, Boston, Massachusetts, United States
| | - Jonathan P. Winickoff
- Division of General Academic Pediatrics, Tobacco Research and Treatment Center, Massachusetts General Hospital for Children, Boston, Massachusetts, United States
| | - Robert W. Grundmeier
- Department of Pediatrics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania, United States
- Department of Biomedical and Health Informatics, Children's Hospital of Philadelphia, Philadelphia, Pennsylvania, United States
| | - Robert A. Schnoll
- Department of Psychiatry, Abramson Cancer Center, University of Pennsylvania, Philadelphia, Pennsylvania, United States
| | - Alexander G. Fiks
- Department of Pediatrics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania, United States
- Clinical Futures, PolicyLab, and The Possibilities Project, Children's Hospital of Philadelphia, Pennsilvania, United States
- Department of Biomedical and Health Informatics, Children's Hospital of Philadelphia, Philadelphia, Pennsylvania, United States
| |
Collapse
|
5
|
Chen W, O’Bryan CM, Gorham G, Howard K, Balasubramanya B, Coffey P, Abeyaratne A, Cass A. Barriers and enablers to implementing and using clinical decision support systems for chronic diseases: a qualitative systematic review and meta-aggregation. Implement Sci Commun 2022; 3:81. [PMID: 35902894 PMCID: PMC9330991 DOI: 10.1186/s43058-022-00326-x] [Citation(s) in RCA: 9] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/13/2021] [Accepted: 07/10/2022] [Indexed: 11/17/2022] Open
Abstract
Background Clinical decision support (CDS) is increasingly used to facilitate chronic disease care. Despite increased availability of electronic health records and the ongoing development of new CDS technologies, uptake of CDS into routine clinical settings is inconsistent. This qualitative systematic review seeks to synthesise healthcare provider experiences of CDS—exploring the barriers and enablers to implementing, using, evaluating, and sustaining chronic disease CDS systems. Methods A search was conducted in Medline, CINAHL, APA PsychInfo, EconLit, and Web of Science from 2011 to 2021. Primary research studies incorporating qualitative findings were included if they targeted healthcare providers and studied a relevant chronic disease CDS intervention. Relevant CDS interventions were electronic health record-based and addressed one or more of the following chronic diseases: cardiovascular disease, diabetes, chronic kidney disease, hypertension, and hypercholesterolaemia. Qualitative findings were synthesised using a meta-aggregative approach. Results Thirty-three primary research articles were included in this qualitative systematic review. Meta-aggregation of qualitative data revealed 177 findings and 29 categories, which were aggregated into 8 synthesised findings. The synthesised findings related to clinical context, user, external context, and technical factors affecting CDS uptake. Key barriers to uptake included CDS systems that were simplistic, had limited clinical applicability in multimorbidity, and integrated poorly into existing workflows. Enablers to successful CDS interventions included perceived usefulness in providing relevant clinical knowledge and structured chronic disease care; user confidence gained through training and post training follow-up; external contexts comprised of strong clinical champions, allocated personnel, and technical support; and CDS technical features that are both highly functional, and attractive. Conclusion This systematic review explored healthcare provider experiences, focussing on barriers and enablers to CDS use for chronic diseases. The results provide an evidence-base for designing, implementing, and sustaining future CDS systems. Based on the findings from this review, we highlight actionable steps for practice and future research. Trial registration PROSPERO CRD42020203716 Supplementary Information The online version contains supplementary material available at 10.1186/s43058-022-00326-x.
Collapse
|
6
|
Reese TJ, Liu S, Steitz B, McCoy A, Russo E, Koh B, Ancker J, Wright A. Conceptualizing clinical decision support as complex interventions: a meta-analysis of comparative effectiveness trials. J Am Med Inform Assoc 2022; 29:1744-1756. [PMID: 35652167 PMCID: PMC9471719 DOI: 10.1093/jamia/ocac089] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/01/2022] [Revised: 04/26/2022] [Accepted: 05/23/2022] [Indexed: 12/26/2022] Open
Abstract
OBJECTIVES Complex interventions with multiple components and behavior change strategies are increasingly implemented as a form of clinical decision support (CDS) using native electronic health record functionality. Objectives of this study were, therefore, to (1) identify the proportion of randomized controlled trials with CDS interventions that were complex, (2) describe common gaps in the reporting of complexity in CDS research, and (3) determine the impact of increased complexity on CDS effectiveness. MATERIALS AND METHODS To assess CDS complexity and identify reporting gaps for characterizing CDS interventions, we used the Preferred Reporting Items for Systematic Reviews and Meta-Analyses reporting tool for complex interventions. We evaluated the effect of increased complexity using random-effects meta-analysis. RESULTS Most included studies evaluated a complex CDS intervention (76%). No studies described use of analytical frameworks or causal pathways. Two studies discussed use of theory but only one fully described the rationale and put it in context of a behavior change. A small but positive effect (standardized mean difference, 0.147; 95% CI, 0.039-0.255; P < .01) in favor of increasing intervention complexity was observed. DISCUSSION While most CDS studies should classify interventions as complex, opportunities persist for documenting and providing resources in a manner that would enable CDS interventions to be replicated and adapted. Unless reporting of the design, implementation, and evaluation of CDS interventions improves, only slight benefits can be expected. CONCLUSION Conceptualizing CDS as complex interventions may help convey the careful attention that is needed to ensure these interventions are contextually and theoretically informed.
Collapse
Affiliation(s)
- Thomas J Reese
- Department of Biomedical Informatics, Vanderbilt University Medical Center, Nashville, Tennessee, USA
| | - Siru Liu
- Department of Biomedical Informatics, Vanderbilt University Medical Center, Nashville, Tennessee, USA
| | - Bryan Steitz
- Department of Biomedical Informatics, Vanderbilt University Medical Center, Nashville, Tennessee, USA
| | - Allison McCoy
- Department of Biomedical Informatics, Vanderbilt University Medical Center, Nashville, Tennessee, USA
| | - Elise Russo
- Department of Biomedical Informatics, Vanderbilt University Medical Center, Nashville, Tennessee, USA
| | - Brian Koh
- Department of Biomedical Informatics, Vanderbilt University Medical Center, Nashville, Tennessee, USA
| | - Jessica Ancker
- Department of Biomedical Informatics, Vanderbilt University Medical Center, Nashville, Tennessee, USA
| | - Adam Wright
- Department of Biomedical Informatics, Vanderbilt University Medical Center, Nashville, Tennessee, USA
| |
Collapse
|
7
|
Garabedian PM, Gannon MP, Aaron S, Wu E, Burns Z, Samal L. Human-centered design of clinical decision support for management of hypertension with chronic kidney disease. BMC Med Inform Decis Mak 2022; 22:217. [PMID: 35964083 PMCID: PMC9375189 DOI: 10.1186/s12911-022-01962-y] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/24/2022] [Accepted: 08/02/2022] [Indexed: 11/16/2022] Open
Abstract
Background Primary care providers face challenges in recognizing and controlling hypertension in patients with chronic kidney disease (CKD). Clinical decision support (CDS) has the potential to aid clinicians in identifying patients who could benefit from medication changes. This study designed an alert to control hypertension in CKD patients using an iterative human-centered design process. Methods In this study, we present a human-centered design process employing multiple methods for gathering user requirements and feedback on design and usability. Initially, we conducted contextual inquiry sessions to gather user requirements for the CDS. This was followed by group design sessions and one-on-one formative think-aloud sessions to validate requirements, obtain feedback on the design and layout, uncover usability issues, and validate changes. Results This study included 20 participants. The contextual inquiry produced 10 user requirements which influenced the initial alert design. The group design sessions revealed issues related to several themes, including recommendations and clinical content that did not match providers' expectations and extraneous information on the alerts that did not provide value. Findings from the individual think-aloud sessions revealed that participants disagreed with some recommended clinical actions, requested additional information, and had concerns about the placement in their workflow. Following each step, iterative changes were made to the alert content and design. Discussion This study showed that participation from users throughout the design process can lead to a better understanding of user requirements and optimal design, even within the constraints of an EHR alerting system. While raising awareness of design needs, it also revealed concerns related to workflow, understandability, and relevance. Conclusion The human-centered design framework using multiple methods for CDS development informed the creation of an alert to assist in the treatment and recognition of hypertension in patients with CKD. Supplementary Information The online version contains supplementary material available at 10.1186/s12911-022-01962-y.
Collapse
Affiliation(s)
- Pamela M Garabedian
- Mass General Brigham, 399 Revolution Drive, Somerville, MA, 857-282-4091, USA.
| | - Michael P Gannon
- Division of General Internal Medicine, Brigham and Women's Hospital, Boston, MA, USA
| | - Skye Aaron
- Division of General Internal Medicine, Brigham and Women's Hospital, Boston, MA, USA
| | - Edward Wu
- Alabama College of Osteopathic Medicine, Dothan, AL, USA
| | - Zoe Burns
- Division of General Internal Medicine, Brigham and Women's Hospital, Boston, MA, USA
| | - Lipika Samal
- Division of General Internal Medicine, Brigham and Women's Hospital, Boston, MA, USA.,Harvard Medical School, Boston, MA, USA
| |
Collapse
|
8
|
Chen W, Howard K, Gorham G, O'Bryan CM, Coffey P, Balasubramanya B, Abeyaratne A, Cass A. Design, effectiveness, and economic outcomes of contemporary chronic disease clinical decision support systems: a systematic review and meta-analysis. J Am Med Inform Assoc 2022; 29:1757-1772. [PMID: 35818299 PMCID: PMC9471723 DOI: 10.1093/jamia/ocac110] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/01/2022] [Revised: 06/21/2022] [Accepted: 06/25/2022] [Indexed: 01/10/2023] Open
Abstract
Objectives Electronic health record-based clinical decision support (CDS) has the potential to improve health outcomes. This systematic review investigates the design, effectiveness, and economic outcomes of CDS targeting several common chronic diseases. Material and Methods We conducted a search in PubMed (Medline), EBSCOHOST (CINAHL, APA PsychInfo, EconLit), and Web of Science. We limited the search to studies from 2011 to 2021. Studies were included if the CDS was electronic health record-based and targeted one or more of the following chronic diseases: cardiovascular disease, diabetes, chronic kidney disease, hypertension, and hypercholesterolemia. Studies with effectiveness or economic outcomes were considered for inclusion, and a meta-analysis was conducted. Results The review included 76 studies with effectiveness outcomes and 9 with economic outcomes. Of the effectiveness studies, 63% described a positive outcome that favored the CDS intervention group. However, meta-analysis demonstrated that effect sizes were heterogenous and small, with limited clinical and statistical significance. Of the economic studies, most full economic evaluations (n = 5) used a modeled analysis approach. Cost-effectiveness of CDS varied widely between studies, with an estimated incremental cost-effectiveness ratio ranging between USD$2192 to USD$151 955 per QALY. Conclusion We summarize contemporary chronic disease CDS designs and evaluation results. The effectiveness and cost-effectiveness results for CDS interventions are highly heterogeneous, likely due to differences in implementation context and evaluation methodology. Improved quality of reporting, particularly from modeled economic evaluations, would assist decision makers to better interpret and utilize results from these primary research studies. Registration PROSPERO (CRD42020203716)
Collapse
Affiliation(s)
- Winnie Chen
- Menzies School of Health Research, Charles Darwin University, Casuarina, Northern Territory, Australia
| | - Kirsten Howard
- School of Public Health, Faculty of Medicine and Health, University of Sydney, Sydney, New South Wales, Australia
| | - Gillian Gorham
- Menzies School of Health Research, Charles Darwin University, Casuarina, Northern Territory, Australia
| | - Claire Maree O'Bryan
- Menzies School of Health Research, Charles Darwin University, Casuarina, Northern Territory, Australia
| | - Patrick Coffey
- Menzies School of Health Research, Charles Darwin University, Casuarina, Northern Territory, Australia
| | - Bhavya Balasubramanya
- Menzies School of Health Research, Charles Darwin University, Casuarina, Northern Territory, Australia
| | - Asanga Abeyaratne
- Menzies School of Health Research, Charles Darwin University, Casuarina, Northern Territory, Australia
| | - Alan Cass
- Menzies School of Health Research, Charles Darwin University, Casuarina, Northern Territory, Australia
| |
Collapse
|
9
|
Jenssen BP, Karavite DJ, Kelleher S, Nekrasova E, Thayer JG, Ratwani R, Shea J, Nabi-Burza E, Drehmer JE, Winickoff JP, Grundmeier RW, Schnoll RA, Fiks AG. Electronic Health Record-Embedded, Behavioral Science-Informed System for Smoking Cessation for the Parents of Pediatric Patients. Appl Clin Inform 2022; 13:504-515. [PMID: 35584789 DOI: 10.1055/s-0042-1748148] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/02/2022] Open
Abstract
BACKGROUND Helping parents quit smoking is a public health priority. However, parents are rarely, if ever, offered tobacco use treatment through pediatric settings. Clinical decision support (CDS) systems developed for the workflows of pediatric primary care may support consistent screening, treatment, and referral. OBJECTIVES This study aimed to develop a CDS system by using human-centered design (HCD) that identifies parents who smoke, provides motivational messages to quit smoking (informed by behavioral science), and supports delivery of evidence-based tobacco treatment. METHODS Our multidisciplinary team applied a rigorous HCD process involving analysis of the work environment, user involvement in formative design, iterative improvements, and evaluation of the system's use in context with the following three cohorts: (1) parents who smoke, (2) pediatric clinicians, and (3) clinic staff. Participants from each cohort were presented with scenario-based, high-fidelity mockups of system components and then provided input related to their role in using the CDS system. RESULTS We engaged 70 representative participants including 30 parents, 30 clinicians, and 10 clinic staff. A key theme of the design review sessions across all cohorts was the need to automate functions of the system. Parents emphasized a system that presented information in a simple way, highlighted benefits of quitting smoking, and allowed direct connection to treatment. Pediatric clinicians emphasized automating tobacco treatment. Clinical staff emphasized screening for parent smoking via several modalities prior to the patient's visit. Once the system was developed, most parents (80%) reported that it was easy to use, and the majority of pediatricians reported that they would use the system (97%) and were satisfied with it (97%). CONCLUSION A CDS system to support parental tobacco cessation in pediatric primary care, developed through an HCD process, proved easy to use and acceptable to parents, clinicians, and office staff. This preliminary work justifies evaluating the impact of the system on helping parents quit smoking.
Collapse
Affiliation(s)
- Brian P Jenssen
- Department of Pediatrics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania, United States.,Center for Pediatric Clinical Effectiveness, PolicyLab, and The Possibilities Project, Children's Hospital of Philadelphia, Philadelphia, Pennsylvania, United States.,Department of Biomedical and Health Informatics, Children's Hospital of Philadelphia, Philadelphia, Pennsylvania, United States
| | - Dean J Karavite
- Department of Biomedical and Health Informatics, Children's Hospital of Philadelphia, Philadelphia, Pennsylvania, United States
| | - Shannon Kelleher
- Center for Pediatric Clinical Effectiveness, PolicyLab, and The Possibilities Project, Children's Hospital of Philadelphia, Philadelphia, Pennsylvania, United States
| | - Ekaterina Nekrasova
- Center for Pediatric Clinical Effectiveness, PolicyLab, and The Possibilities Project, Children's Hospital of Philadelphia, Philadelphia, Pennsylvania, United States
| | - Jeritt G Thayer
- Department of Biomedical and Health Informatics, Children's Hospital of Philadelphia, Philadelphia, Pennsylvania, United States
| | - Raj Ratwani
- MedStar Health National Center for Human Factors in Healthcare, Washington, Dist. of Columbia, United States
| | - Judy Shea
- Department of Medicine, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania, United States
| | - Emara Nabi-Burza
- Division of General Academic Pediatrics and Tobacco Research and Treatment Center, Massachusetts General Hospital for Children, Boston, Massachusetts, United States
| | - Jeremy E Drehmer
- Division of General Academic Pediatrics and Tobacco Research and Treatment Center, Massachusetts General Hospital for Children, Boston, Massachusetts, United States
| | - Jonathan P Winickoff
- Division of General Academic Pediatrics and Tobacco Research and Treatment Center, Massachusetts General Hospital for Children, Boston, Massachusetts, United States
| | - Robert W Grundmeier
- Department of Pediatrics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania, United States.,Department of Biomedical and Health Informatics, Children's Hospital of Philadelphia, Philadelphia, Pennsylvania, United States
| | - Robert A Schnoll
- Department of Psychiatry and Abramson Cancer Center, University of Pennsylvania, Philadelphia, Pennsylvania, United States
| | - Alexander G Fiks
- Department of Pediatrics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania, United States.,Center for Pediatric Clinical Effectiveness, PolicyLab, and The Possibilities Project, Children's Hospital of Philadelphia, Philadelphia, Pennsylvania, United States.,Department of Biomedical and Health Informatics, Children's Hospital of Philadelphia, Philadelphia, Pennsylvania, United States
| |
Collapse
|
10
|
Thiess H, Del Fiol G, Malone DC, Cornia R, Sibilla M, Rhodes B, Boyce RD, Kawamoto K, Reese T. Coordinated use of Health Level 7 standards to support clinical decision support: Case study with shared decision making and drug-drug interactions. Int J Med Inform 2022; 162:104749. [PMID: 35358893 PMCID: PMC9703934 DOI: 10.1016/j.ijmedinf.2022.104749] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/30/2021] [Revised: 02/22/2022] [Accepted: 03/17/2022] [Indexed: 11/29/2022]
Abstract
BACKGROUND Despite advances in interoperability standards, it remains challenging and often costly to share clinical decision support (CDS) across healthcare organizations. This is due in part to limited coordination among CDS components. To improve coordination of CDS components, Health Level 7 (HL7) has developed a suite of interoperability standards with Fast Health Interoperability Resources (FHIR) specification as a common information model. Evidence is needed to determine the feasibility of implementing these CDS components; therefore, the objective of this study was to investigate the coordination of emerging HL7 standards with modular CDS architecture components. METHODS We used a modular, standards-based architecture consisting of four components: data, logic, services, and applications. The implementation use-case was an application to support shared decision making in the context of drug-drug interactions (DDInteract). RESULTS DDInteract uses FHIR as the data representation model, Clinical Quality Language for logic representation, CDS Hooks for the services layer, and Substitutable Medical Apps Reusable Technologies for application integration. DDInteract was first implemented in a sandbox environment and then in an electronic health record (Epic®) test environment. DDInteract can be integrated in clinical workflows through on-demand access from a menu or through CDS Hooks upon opening a patient's record or placing a medication order. CONCLUSION In the context of drug interactions, DDInteract is the first application to leverage a full stack of emerging interoperability standards for each component of modular CDS architecture. The demonstrated feasibility of interoperable components can be generalized to other modular CDS applications.
Collapse
Affiliation(s)
| | | | | | - Ryan Cornia
- University of Utah, Department of Biomedical Informatics, United States
| | - Max Sibilla
- University of Pittsburgh, Department of Biomedical Informatics, United States
| | - Bryn Rhodes
- Alphora, Chief Technology Officer, United States
| | - Richard D Boyce
- University of Pittsburgh, Department of Biomedical Informatics, United States
| | - Kensaku Kawamoto
- University of Utah, Department of Biomedical Informatics, United States
| | - Thomas Reese
- Vanderbilt University Medical Center, Department of Biomedical Informatics, Nashville, TN.
| |
Collapse
|
11
|
Kukhareva PV, Weir C, Fiol GD, Aarons GA, Taft TY, Schlechter CR, Reese TJ, Curran RL, Nanjo C, Borbolla D, Staes CJ, Morgan KL, Kramer HS, Stipelman CH, Shakib JH, Flynn MC, Kawamoto K. Evaluation in Life Cycle of Information Technology (ELICIT) framework: Supporting the innovation life cycle from business case assessment to summative evaluation. J Biomed Inform 2022; 127:104014. [PMID: 35167977 PMCID: PMC8959015 DOI: 10.1016/j.jbi.2022.104014] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/16/2021] [Revised: 11/16/2021] [Accepted: 02/02/2022] [Indexed: 11/28/2022]
Abstract
OBJECTIVE Our objective was to develop an evaluation framework for electronic health record (EHR)-integrated innovations to support evaluation activities at each of four information technology (IT) life cycle phases: planning, development, implementation, and operation. METHODS The evaluation framework was developed based on a review of existing evaluation frameworks from health informatics and other domains (human factors engineering, software engineering, and social sciences); expert consensus; and real-world testing in multiple EHR-integrated innovation studies. RESULTS The resulting Evaluation in Life Cycle of IT (ELICIT) framework covers four IT life cycle phases and three measure levels (society, user, and IT). The ELICIT framework recommends 12 evaluation steps: (1) business case assessment; (2) stakeholder requirements gathering; (3) technical requirements gathering; (4) technical acceptability assessment; (5) user acceptability assessment; (6) social acceptability assessment; (7) social implementation assessment; (8) initial user satisfaction assessment; (9) technical implementation assessment; (10) technical portability assessment; (11) long-term user satisfaction assessment; and (12) social outcomes assessment. DISCUSSION Effective evaluation requires a shared understanding and collaboration across disciplines throughout the entire IT life cycle. In contrast with previous evaluation frameworks, the ELICIT framework focuses on all phases of the IT life cycle across the society, user, and IT levels. Institutions seeking to establish evaluation programs for EHR-integrated innovations could use our framework to create such shared understanding and justify the need to invest in evaluation. CONCLUSION As health care undergoes a digital transformation, it will be critical for EHR-integrated innovations to be systematically evaluated. The ELICIT framework can facilitate these evaluations.
Collapse
Affiliation(s)
- Polina V. Kukhareva
- Department of Biomedical Informatics, University of Utah, Salt Lake City, UT, USA
| | - Charlene Weir
- Department of Biomedical Informatics, University of Utah, Salt Lake City, UT, USA.
| | - Guilherme Del Fiol
- Department of Biomedical Informatics, University of Utah, Salt Lake City, UT, USA
| | - Gregory A. Aarons
- Department of Psychiatry, UC San Diego ACTRI Dissemination and Implementation Science Center, UC San Diego, La Jolla, CA, USA
| | - Teresa Y. Taft
- Department of Biomedical Informatics, University of Utah, Salt Lake City, UT, USA
| | - Chelsey R. Schlechter
- Department of Population Health Sciences, Center for Health Outcomes and Population Equity, Huntsman Cancer Institute, University of Utah, Salt Lake City, UT, USA
| | - Thomas J. Reese
- Department of Biomedical Informatics, Vanderbilt University, Nashville, TN, USA
| | - Rebecca L. Curran
- Department of Biomedical Informatics, University of Utah, Salt Lake City, UT, USA
| | - Claude Nanjo
- Department of Biomedical Informatics, University of Utah, Salt Lake City, UT, USA.
| | - Damian Borbolla
- Department of Biomedical Informatics, University of Utah, Salt Lake City, UT, USA.
| | | | - Keaton L. Morgan
- Department of Biomedical Informatics, University of Utah, Salt Lake City, UT, USA
| | - Heidi S. Kramer
- Department of Biomedical Informatics, University of Utah, Salt Lake City, UT, USA
| | | | - Julie H. Shakib
- Department of Pediatrics, University of Utah, Salt Lake City, UT, USA
| | - Michael C. Flynn
- Department of Family & Preventive Medicine, University of Utah, Salt Lake City, UT, USA
| | - Kensaku Kawamoto
- Department of Biomedical Informatics, University of Utah, Salt Lake City, UT, USA.
| |
Collapse
|
12
|
Bradshaw RL, Kawamoto K, Kaphingst KA, Kohlmann WK, Hess R, Flynn MC, Nanjo CJ, Warner PB, Shi J, Morgan K, Kimball K, Ranade-Kharkar P, Ginsburg O, Goodman M, Chambers R, Mann D, Narus SP, Gonzalez J, Loomis S, Chan P, Monahan R, Borsato EP, Shields DE, Martin DK, Kessler CM, Del Fiol G. OUP accepted manuscript. J Am Med Inform Assoc 2022; 29:928-936. [PMID: 35224632 PMCID: PMC9006693 DOI: 10.1093/jamia/ocac028] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/10/2021] [Revised: 02/03/2022] [Accepted: 02/18/2022] [Indexed: 11/17/2022] Open
Abstract
Population health management (PHM) is an important approach to promote wellness and deliver health care to targeted individuals who meet criteria for preventive measures or treatment. A critical component for any PHM program is a data analytics platform that can target those eligible individuals. Objective The aim of this study was to design and implement a scalable standards-based clinical decision support (CDS) approach to identify patient cohorts for PHM and maximize opportunities for multi-site dissemination. Materials and Methods An architecture was established to support bidirectional data exchanges between heterogeneous electronic health record (EHR) data sources, PHM systems, and CDS components. HL7 Fast Healthcare Interoperability Resources and CDS Hooks were used to facilitate interoperability and dissemination. The approach was validated by deploying the platform at multiple sites to identify patients who meet the criteria for genetic evaluation of familial cancer. Results The Genetic Cancer Risk Detector (GARDE) platform was created and is comprised of four components: (1) an open-source CDS Hooks server for computing patient eligibility for PHM cohorts, (2) an open-source Population Coordinator that processes GARDE requests and communicates results to a PHM system, (3) an EHR Patient Data Repository, and (4) EHR PHM Tools to manage patients and perform outreach functions. Site-specific deployments were performed on onsite virtual machines and cloud-based Amazon Web Services. Discussion GARDE’s component architecture establishes generalizable standards-based methods for computing PHM cohorts. Replicating deployments using one of the established deployment methods requires minimal local customization. Most of the deployment effort was related to obtaining site-specific information technology governance approvals.
Collapse
Affiliation(s)
- Richard L Bradshaw
- Department of Biomedical Informatics, University of Utah, Salt Lake City, Utah, USA
- University of Utah Health, Salt Lake City, Utah, USA
- Corresponding Author: Richard L. Bradshaw, MS, PhD, Department of Biomedical Informatics, University of Utah, 421 Wakara Way, Suite 140, Salt Lake City, UT 84108-3514, USA;
| | - Kensaku Kawamoto
- Department of Biomedical Informatics, University of Utah, Salt Lake City, Utah, USA
- University of Utah Health, Salt Lake City, Utah, USA
| | - Kimberly A Kaphingst
- Huntsman Cancer Institute, University of Utah, Salt Lake City, Utah, USA
- Department of Communication, University of Utah, Salt Lake City, Utah, USA
| | - Wendy K Kohlmann
- University of Utah Health, Salt Lake City, Utah, USA
- Huntsman Cancer Institute, University of Utah, Salt Lake City, Utah, USA
- Departments of Population Health Sciences, University of Utah, Salt Lake City, Utah, USA
| | - Rachel Hess
- University of Utah Health, Salt Lake City, Utah, USA
- Departments of Population Health Sciences, University of Utah, Salt Lake City, Utah, USA
- Department of Internal Medicine, University of Utah, Salt Lake City, Utah, USA
| | - Michael C Flynn
- University of Utah Health, Salt Lake City, Utah, USA
- Department of Internal Medicine, University of Utah, Salt Lake City, Utah, USA
- Community Physicians Group, University of Utah, Salt Lake City, Utah, USA
| | - Claude J Nanjo
- Department of Biomedical Informatics, University of Utah, Salt Lake City, Utah, USA
- University of Utah Health, Salt Lake City, Utah, USA
| | - Phillip B Warner
- Department of Biomedical Informatics, University of Utah, Salt Lake City, Utah, USA
- University of Utah Health, Salt Lake City, Utah, USA
| | - Jianlin Shi
- Department of Biomedical Informatics, University of Utah, Salt Lake City, Utah, USA
| | - Keaton Morgan
- Department of Biomedical Informatics, University of Utah, Salt Lake City, Utah, USA
- University of Utah Health, Salt Lake City, Utah, USA
- Department of Surgery, University of Utah, Salt Lake City, Utah, USA
| | - Kadyn Kimball
- Huntsman Cancer Institute, University of Utah, Salt Lake City, Utah, USA
| | - Pallavi Ranade-Kharkar
- Department of Biomedical Informatics, University of Utah, Salt Lake City, Utah, USA
- Intermountain Healthcare, Salt Lake City, Utah, USA
| | - Ophira Ginsburg
- New York University Langone Health, New York City, New York, USA
| | - Melody Goodman
- School of Global and Public Health, New York University, New York City, New York, USA
| | | | - Devin Mann
- New York University Langone Health, New York City, New York, USA
| | - Scott P Narus
- Department of Biomedical Informatics, University of Utah, Salt Lake City, Utah, USA
| | - Javier Gonzalez
- New York University Langone Health, New York City, New York, USA
| | - Shane Loomis
- New York University Langone Health, New York City, New York, USA
- Epic Systems Corporation, Madison, Wisconsin, USA
| | - Priscilla Chan
- New York University Langone Health, New York City, New York, USA
| | - Rachel Monahan
- New York University Langone Health, New York City, New York, USA
| | - Emerson P Borsato
- Department of Biomedical Informatics, University of Utah, Salt Lake City, Utah, USA
| | - David E Shields
- Department of Biomedical Informatics, University of Utah, Salt Lake City, Utah, USA
- University of Utah Health, Salt Lake City, Utah, USA
| | - Douglas K Martin
- Department of Biomedical Informatics, University of Utah, Salt Lake City, Utah, USA
- University of Utah Health, Salt Lake City, Utah, USA
| | - Cecilia M Kessler
- University of Utah Health, Salt Lake City, Utah, USA
- Department of Communication, University of Utah, Salt Lake City, Utah, USA
| | - Guilherme Del Fiol
- Department of Biomedical Informatics, University of Utah, Salt Lake City, Utah, USA
| |
Collapse
|
13
|
Taber P, Radloff C, Del Fiol G, Staes C, Kawamoto K. New Standards for Clinical Decision Support: A Survey of The State of Implementation. Yearb Med Inform 2021; 30:159-171. [PMID: 34479387 PMCID: PMC8416232 DOI: 10.1055/s-0041-1726502] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/07/2023] Open
Abstract
Objectives:
To review the current state of research on designing and implementing clinical decision support (CDS) using four current interoperability standards: Fast Healthcare Interoperability Resources (FHIR); Substitutable Medical Applications and Reusable Technologies (SMART); Clinical Quality Language (CQL); and CDS Hooks.
Methods:
We conducted a review of original studies describing development of specific CDS tools or infrastructures using one of the four targeted standards, regardless of implementation stage. Citations published any time before the literature search was executed on October 21, 2020 were retrieved from PubMed. Two reviewers independently screened articles and abstracted data according to a protocol designed by team consensus.
Results:
Of 290 articles identified via PubMed search, 44 were included in this study. More than three quarters were published since 2018. Forty-three (98%) used FHIR; 22 (50%) used SMART; two (5%) used CQL; and eight (18%) used CDS Hooks. Twenty-four (55%) were in the design stage, 15 (34%) in the piloting stage, and five (11%) were deployed in a real-world setting. Only 12 (27%) of the articles reported an evaluation of the technology under development. Three of the four articles describing a deployed technology reported an evaluation. Only two evaluations with randomized study components were identified.
Conclusion:
The diversity of topics and approaches identified in the literature highlights the utility of these standards. The infrequency of reported evaluations, as well as the high number of studies in the design or piloting stage, indicate that these technologies are still early in their life cycles. Informaticists will require a stronger evidence base to understand the implications of using these standards in CDS design and implementation.
Collapse
Affiliation(s)
- Peter Taber
- Department of Biomedical Informatics, University of Utah, Salt Lake City, UT, USA
| | | | - Guilherme Del Fiol
- Department of Biomedical Informatics, University of Utah, Salt Lake City, UT, USA
| | - Catherine Staes
- Department of Biomedical Informatics, University of Utah, Salt Lake City, UT, USA.,College of Nursing, University of Utah, Salt Lake City, UT, USA
| | - Kensaku Kawamoto
- Department of Biomedical Informatics, University of Utah, Salt Lake City, UT, USA
| |
Collapse
|
14
|
Abstract
OBJECTIVE Human factors and ergonomics (HF/E) frameworks and methods are becoming embedded in the health informatics community. There is now broad recognition that health informatics tools must account for the diverse needs, characteristics, and abilities of end users, as well as their context of use. The objective of this review is to synthesize the current nature and scope of HF/E integration into the health informatics community. METHODS Because the focus of this synthesis is on understanding the current integration of the HF/E and health informatics research communities, we manually reviewed all manuscripts published in primary HF/E and health informatics journals during 2020. RESULTS HF/E-focused health informatics studies included in this synthesis focused heavily on EHR customizations, specifically clinical decision support customizations and customized data displays, and on mobile health innovations. While HF/E methods aimed to jointly improve end user safety, performance, and satisfaction, most HF/E-focused health informatics studies measured only end user satisfaction. CONCLUSION HF/E-focused health informatics researchers need to identify and communicate methodological standards specific to health informatics, to better synthesize findings across resource intensive HF/E-focused health informatics studies. Important gaps in the HF/E design and evaluation process should be addressed in future work, including support for technology development platforms and training programs so that health informatics designers are as diverse as end users.
Collapse
|
15
|
Kawamoto K, Kukhareva PV, Weir C, Flynn MC, Nanjo CJ, Martin DK, Warner PB, Shields DE, Rodriguez-Loya S, Bradshaw RL, Cornia RC, Reese TJ, Kramer HS, Taft T, Curran RL, Morgan KL, Borbolla D, Hightower M, Turnbull WJ, Strong MB, Chapman WW, Gregory T, Stipelman CH, Shakib JH, Hess R, Boltax JP, Habboushe JP, Sakaguchi F, Turner KM, Narus SP, Tarumi S, Takeuchi W, Ban H, Wetter DW, Lam C, Caverly TJ, Fagerlin A, Norlin C, Malone DC, Kaphingst KA, Kohlmann WK, Brooke BS, Del Fiol G. Establishing a multidisciplinary initiative for interoperable electronic health record innovations at an academic medical center. JAMIA Open 2021; 4:ooab041. [PMID: 34345802 PMCID: PMC8325485 DOI: 10.1093/jamiaopen/ooab041] [Citation(s) in RCA: 26] [Impact Index Per Article: 8.7] [Reference Citation Analysis] [Abstract] [Key Words] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/12/2020] [Revised: 02/18/2021] [Accepted: 05/04/2021] [Indexed: 12/02/2022] Open
Abstract
Objective To establish an enterprise initiative for improving health and health care through interoperable electronic health record (EHR) innovations. Materials and Methods We developed a unifying mission and vision, established multidisciplinary governance, and formulated a strategic plan. Key elements of our strategy include establishing a world-class team; creating shared infrastructure to support individual innovations; developing and implementing innovations with high anticipated impact and a clear path to adoption; incorporating best practices such as the use of Fast Healthcare Interoperability Resources (FHIR) and related interoperability standards; and maximizing synergies across research and operations and with partner organizations. Results University of Utah Health launched the ReImagine EHR initiative in 2016. Supportive infrastructure developed by the initiative include various FHIR-related tooling and a systematic evaluation framework. More than 10 EHR-integrated digital innovations have been implemented to support preventive care, shared decision-making, chronic disease management, and acute clinical care. Initial evaluations of these innovations have demonstrated positive impact on user satisfaction, provider efficiency, and compliance with evidence-based guidelines. Return on investment has included improvements in care; over $35 million in external grant funding; commercial opportunities; and increased ability to adapt to a changing healthcare landscape. Discussion Key lessons learned include the value of investing in digital innovation initiatives leveraging FHIR; the importance of supportive infrastructure for accelerating innovation; and the critical role of user-centered design, implementation science, and evaluation. Conclusion EHR-integrated digital innovation initiatives can be key assets for enhancing the EHR user experience, improving patient care, and reducing provider burnout.
Collapse
Affiliation(s)
- Kensaku Kawamoto
- Department of Biomedical Informatics, University of Utah, Salt Lake City, Utah, USA.,University of Utah Health, Salt Lake City, Utah, USA
| | - Polina V Kukhareva
- Department of Biomedical Informatics, University of Utah, Salt Lake City, Utah, USA.,University of Utah Health, Salt Lake City, Utah, USA
| | - Charlene Weir
- Department of Biomedical Informatics, University of Utah, Salt Lake City, Utah, USA
| | - Michael C Flynn
- University of Utah Health, Salt Lake City, Utah, USA.,Community Physicians Group, University of Utah, Salt Lake City, Utah, USA.,Department of Internal Medicine, University of Utah, Salt Lake City, Utah, USA
| | - Claude J Nanjo
- Department of Biomedical Informatics, University of Utah, Salt Lake City, Utah, USA.,University of Utah Health, Salt Lake City, Utah, USA
| | - Douglas K Martin
- Department of Biomedical Informatics, University of Utah, Salt Lake City, Utah, USA.,University of Utah Health, Salt Lake City, Utah, USA
| | - Phillip B Warner
- Department of Biomedical Informatics, University of Utah, Salt Lake City, Utah, USA.,University of Utah Health, Salt Lake City, Utah, USA
| | - David E Shields
- Department of Biomedical Informatics, University of Utah, Salt Lake City, Utah, USA.,University of Utah Health, Salt Lake City, Utah, USA
| | - Salvador Rodriguez-Loya
- Department of Biomedical Informatics, University of Utah, Salt Lake City, Utah, USA.,University of Utah Health, Salt Lake City, Utah, USA
| | - Richard L Bradshaw
- Department of Biomedical Informatics, University of Utah, Salt Lake City, Utah, USA.,University of Utah Health, Salt Lake City, Utah, USA
| | - Ryan C Cornia
- Department of Biomedical Informatics, University of Utah, Salt Lake City, Utah, USA.,University of Utah Health, Salt Lake City, Utah, USA
| | - Thomas J Reese
- Department of Biomedical Informatics, University of Utah, Salt Lake City, Utah, USA
| | - Heidi S Kramer
- Department of Biomedical Informatics, University of Utah, Salt Lake City, Utah, USA
| | - Teresa Taft
- Department of Biomedical Informatics, University of Utah, Salt Lake City, Utah, USA
| | - Rebecca L Curran
- Department of Biomedical Informatics, University of Utah, Salt Lake City, Utah, USA.,Department of Family & Preventive Medicine, University of Utah, Salt Lake City, Utah, USA
| | - Keaton L Morgan
- Department of Biomedical Informatics, University of Utah, Salt Lake City, Utah, USA.,University of Utah Health, Salt Lake City, Utah, USA.,Department of Surgery, University of Utah, Salt Lake City, Utah, USA
| | - Damian Borbolla
- Department of Biomedical Informatics, University of Utah, Salt Lake City, Utah, USA
| | - Maia Hightower
- University of Utah Health, Salt Lake City, Utah, USA.,Department of Internal Medicine, University of Utah, Salt Lake City, Utah, USA
| | | | - Michael B Strong
- University of Utah Health, Salt Lake City, Utah, USA.,Department of Internal Medicine, University of Utah, Salt Lake City, Utah, USA
| | - Wendy W Chapman
- Department of Biomedical Informatics, University of Utah, Salt Lake City, Utah, USA
| | | | - Carole H Stipelman
- University of Utah Health, Salt Lake City, Utah, USA.,Department of Pediatrics, University of Utah, Salt Lake City, Utah, USA
| | - Julie H Shakib
- University of Utah Health, Salt Lake City, Utah, USA.,Department of Pediatrics, University of Utah, Salt Lake City, Utah, USA
| | - Rachel Hess
- University of Utah Health, Salt Lake City, Utah, USA.,Department of Internal Medicine, University of Utah, Salt Lake City, Utah, USA.,Department of Population Health Sciences, University of Utah, Salt Lake City, Utah, USA
| | - Jonathan P Boltax
- Division of Pulmonary Medicine, Department of Internal Medicine, University of Utah, Salt Lake City, Utah, USA
| | - Joseph P Habboushe
- MD Aware, LLC, New York, New York, USA.,Department of Emergency Medicine, New York University, New York, New York, USA
| | - Farrant Sakaguchi
- University of Utah Health, Salt Lake City, Utah, USA.,Community Physicians Group, University of Utah, Salt Lake City, Utah, USA.,Department of Family & Preventive Medicine, University of Utah, Salt Lake City, Utah, USA
| | - Kyle M Turner
- University of Utah Health, Salt Lake City, Utah, USA.,Community Physicians Group, University of Utah, Salt Lake City, Utah, USA.,Department of Pharmacotherapy, University of Utah College of Pharmacy, Salt Lake City, Utah, USA
| | - Scott P Narus
- Department of Biomedical Informatics, University of Utah, Salt Lake City, Utah, USA.,Intermountain Healthcare, Murray, Utah, USA
| | - Shinji Tarumi
- Research & Development Group, Hitachi, Ltd, Tokyo, Japan
| | | | - Hideyuki Ban
- Research & Development Group, Hitachi, Ltd, Tokyo, Japan
| | - David W Wetter
- Department of Population Health Sciences, University of Utah, Salt Lake City, Utah, USA.,Huntsman Cancer Institute, University of Utah, Salt Lake City, Utah, USA
| | - Cho Lam
- Department of Population Health Sciences, University of Utah, Salt Lake City, Utah, USA.,Huntsman Cancer Institute, University of Utah, Salt Lake City, Utah, USA
| | - Tanner J Caverly
- VA Center for Clinical Management Research, Ann Arbor, Michigan, USA.,Departments of Learning Health Sciences and Internal Medicine, University of Michigan, Ann Arbor, Michigan, USA
| | - Angela Fagerlin
- Department of Population Health Sciences, University of Utah, Salt Lake City, Utah, USA.,VA Center for Informatics Decision Enhancement and Surveillance (IDEAS), Salt Lake City, Utah, USA
| | - Chuck Norlin
- University of Utah Health, Salt Lake City, Utah, USA.,Department of Pediatrics, University of Utah, Salt Lake City, Utah, USA
| | - Daniel C Malone
- Department of Pharmacotherapy, University of Utah College of Pharmacy, Salt Lake City, Utah, USA
| | - Kimberly A Kaphingst
- Huntsman Cancer Institute, University of Utah, Salt Lake City, Utah, USA.,Department of Communication, University of Utah, Salt Lake City, Utah, USA
| | - Wendy K Kohlmann
- University of Utah Health, Salt Lake City, Utah, USA.,Huntsman Cancer Institute, University of Utah, Salt Lake City, Utah, USA
| | - Benjamin S Brooke
- Department of Biomedical Informatics, University of Utah, Salt Lake City, Utah, USA.,Department of Surgery, University of Utah, Salt Lake City, Utah, USA
| | - Guilherme Del Fiol
- Department of Biomedical Informatics, University of Utah, Salt Lake City, Utah, USA
| |
Collapse
|
16
|
Pattern of Use of Electronic Health Record (EHR) among the Chronically Ill: A Health Information National Trend Survey (HINTS) Analysis. INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH 2021; 18:ijerph18147254. [PMID: 34299705 PMCID: PMC8304473 DOI: 10.3390/ijerph18147254] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Subscribe] [Scholar Register] [Received: 06/04/2021] [Revised: 06/29/2021] [Accepted: 07/04/2021] [Indexed: 12/20/2022]
Abstract
Effective patient–provider communication is a cornerstone of patient-centered care. Patient portals provide an effective method for secure communication between patients or their proxies and their health care providers. With greater acceptability of patient portals in private practices, patients have a unique opportunity to manage their health care needs. However, studies have shown that less than 50% of patients reported accessing the electronic health record (EHR) in a 12-month period. We used HINTS 5 cycle 1 and cycle 2 to assess disparities among US residents 18 and older with any chronic condition regarding the use of EHR for secure direct messaging with providers, to request refills, to make clinical decisions, or to share medical records with another provider. The results indicate that respondents with multimorbidity are more likely to share their medical records with other providers. However, respondents who are 75 and older are less likely to share their medical records with another provider. Additionally, respondents who are 65 and older are less likely to use the EHR for secure direct messaging with their provider. Additional health care strategies and provider communication should be developed to encourage older patients with chronic conditions to leverage the use of patient portals for effective disease management.
Collapse
|
17
|
Strasberg HR, Rhodes B, Del Fiol G, Jenders RA, Haug PJ, Kawamoto K. Contemporary clinical decision support standards using Health Level Seven International Fast Healthcare Interoperability Resources. J Am Med Inform Assoc 2021; 28:1796-1806. [PMID: 34100949 DOI: 10.1093/jamia/ocab070] [Citation(s) in RCA: 15] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/13/2021] [Revised: 03/08/2021] [Accepted: 04/05/2021] [Indexed: 01/07/2023] Open
Abstract
OBJECTIVE To facilitate the development of standards-based clinical decision support (CDS) systems, we review the current set of CDS standards that are based on Health Level Seven International Fast Healthcare Interoperability Resources (FHIR). Widespread adoption of these standards may help reduce healthcare variability, improve healthcare quality, and improve patient safety. TARGET AUDIENCE This tutorial is designed for the broad informatics community, some of whom may be unfamiliar with the current, FHIR-based CDS standards. SCOPE This tutorial covers the following standards: Arden Syntax (using FHIR as the data model), Clinical Quality Language, FHIR Clinical Reasoning, SMART on FHIR, and CDS Hooks. Detailed descriptions and selected examples are provided.
Collapse
Affiliation(s)
- Howard R Strasberg
- Clinical Effectiveness, Wolters Kluwer Health, San Diego, California, USA
| | | | - Guilherme Del Fiol
- Department of Biomedical Informatics, University of Utah, Salt Lake City, Utah, USA
| | - Robert A Jenders
- Department of Internal Medicine and Center for Biomedical Informatics, Charles R Drew University of Medicine and Science, Los Angeles, California, USA.,Department of Medicine and Clinical and Translational Science Institute, University of California, Los Angeles, Los Angeles, California, USA
| | - Peter J Haug
- Medical Informatics, Intermountain Healthcare, Salt Lake City, Utah, USA
| | - Kensaku Kawamoto
- Department of Biomedical Informatics, University of Utah, Salt Lake City, Utah, USA
| |
Collapse
|
18
|
Tarumi S, Takeuchi W, Chalkidis G, Rodriguez-Loya S, Kuwata J, Flynn M, Turner KM, Sakaguchi FH, Weir C, Kramer H, Shields DE, Warner PB, Kukhareva P, Ban H, Kawamoto K. Leveraging Artificial Intelligence to Improve Chronic Disease Care: Methods and Application to Pharmacotherapy Decision Support for Type-2 Diabetes Mellitus. Methods Inf Med 2021; 60:e32-e43. [PMID: 33975376 PMCID: PMC8294941 DOI: 10.1055/s-0041-1728757] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/10/2020] [Accepted: 02/21/2021] [Indexed: 12/17/2022]
Abstract
OBJECTIVES Artificial intelligence (AI), including predictive analytics, has great potential to improve the care of common chronic conditions with high morbidity and mortality. However, there are still many challenges to achieving this vision. The goal of this project was to develop and apply methods for enhancing chronic disease care using AI. METHODS Using a dataset of 27,904 patients with diabetes, an analytical method was developed and validated for generating a treatment pathway graph which consists of models that predict the likelihood of alternate treatment strategies achieving care goals. An AI-driven clinical decision support system (CDSS) integrated with the electronic health record (EHR) was developed by encapsulating the prediction models in an OpenCDS Web service module and delivering the model outputs through a SMART on FHIR (Substitutable Medical Applications and Reusable Technologies on Fast Healthcare Interoperability Resources) web-based dashboard. This CDSS enables clinicians and patients to review relevant patient parameters, select treatment goals, and review alternate treatment strategies based on prediction results. RESULTS The proposed analytical method outperformed previous machine-learning algorithms on prediction accuracy. The CDSS was successfully integrated with the Epic EHR at the University of Utah. CONCLUSION A predictive analytics-based CDSS was developed and successfully integrated with the EHR through standards-based interoperability frameworks. The approach used could potentially be applied to many other chronic conditions to bring AI-driven CDSS to the point of care.
Collapse
Affiliation(s)
- Shinji Tarumi
- Department of Media Intelligent Processing Research, Center for Technology Innovation Artificial Intelligence, Hitachi Ltd., Kokubunji, Tokyo, Japan
| | - Wataru Takeuchi
- Department of Media Intelligent Processing Research, Center for Technology Innovation Artificial Intelligence, Hitachi Ltd., Kokubunji, Tokyo, Japan
| | - George Chalkidis
- Department of Media Intelligent Processing Research, Center for Technology Innovation Artificial Intelligence, Hitachi Ltd., Kokubunji, Tokyo, Japan
| | - Salvador Rodriguez-Loya
- Department of Biomedical Informatics, University of Utah, Salt Lake City, Utah, United States
| | - Junichi Kuwata
- Department of Product Design, Center for Social Innovation, Hitachi Ltd., Kokubunji, Tokyo, Japan
| | - Michael Flynn
- Departments of Internal Medicine and Pediatrics, University of Utah, Salt Lake City, Utah, United States
| | - Kyle M. Turner
- Department of Pharmacotherapy, University of Utah, Salt Lake City, Utah, United States
| | - Farrant H. Sakaguchi
- Department of Family and Preventive Medicine, University of Utah, Salt Lake City, Utah, United States
| | - Charlene Weir
- Department of Biomedical Informatics, University of Utah, Salt Lake City, Utah, United States
| | - Heidi Kramer
- Department of Biomedical Informatics, University of Utah, Salt Lake City, Utah, United States
| | - David E. Shields
- Department of Biomedical Informatics, University of Utah, Salt Lake City, Utah, United States
| | - Phillip B. Warner
- Department of Biomedical Informatics, University of Utah, Salt Lake City, Utah, United States
| | - Polina Kukhareva
- Department of Biomedical Informatics, University of Utah, Salt Lake City, Utah, United States
| | - Hideyuki Ban
- Department of Media Intelligent Processing Research, Center for Technology Innovation Artificial Intelligence, Hitachi Ltd., Kokubunji, Tokyo, Japan
| | - Kensaku Kawamoto
- Department of Biomedical Informatics, University of Utah, Salt Lake City, Utah, United States
| |
Collapse
|
19
|
Thayer JG, Ferro DF, Miller JM, Karavite D, Grundmeier RW, Utidjian L, Zorc JJ. Human-centered development of an electronic health record-embedded, interactive information visualization in the emergency department using fast healthcare interoperability resources. J Am Med Inform Assoc 2021; 28:1401-1410. [PMID: 33682004 DOI: 10.1093/jamia/ocab016] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/15/2020] [Accepted: 01/21/2021] [Indexed: 11/13/2022] Open
Abstract
OBJECTIVE Develop and evaluate an interactive information visualization embedded within the electronic health record (EHR) by following human-centered design (HCD) processes and leveraging modern health information exchange standards. MATERIALS AND METHODS We applied an HCD process to develop a Fast Healthcare Interoperability Resources (FHIR) application that displays a patient's asthma history to clinicians in a pediatric emergency department. We performed a preimplementation comparative system evaluation to measure time on task, number of screens, information retrieval accuracy, cognitive load, user satisfaction, and perceived utility and usefulness. Application usage and system functionality were assessed using application logs and a postimplementation survey of end users. RESULTS Usability testing of the Asthma Timeline Application demonstrated a statistically significant reduction in time on task (P < .001), number of screens (P < .001), and cognitive load (P < .001) for clinicians when compared to base EHR functionality. Postimplementation evaluation demonstrated reliable functionality and high user satisfaction. DISCUSSION Following HCD processes to develop an application in the context of clinical operations/quality improvement is feasible. Our work also highlights the potential benefits and challenges associated with using internationally recognized data exchange standards as currently implemented. CONCLUSION Compared to standard EHR functionality, our visualization increased clinician efficiency when reviewing the charts of pediatric asthma patients. Application development efforts in an operational context should leverage existing health information exchange standards, such as FHIR, and evidence-based mixed methods approaches.
Collapse
Affiliation(s)
- Jeritt G Thayer
- Department of Biomedical and Health Informatics, Children's Hospital of Philadelphia, Philadelphia, Pennsylvania, USA
| | - Daria F Ferro
- Department of Biomedical and Health Informatics, Children's Hospital of Philadelphia, Philadelphia, Pennsylvania, USA.,Department of Pediatrics, Perelman School of Medicine at the University of Pennsylvania, Philadelphia, Pennsylvania, USA.,Division of General Pediatrics, Children's Hospital of Philadelphia, Philadelphia, Pennsylvania, USA
| | - Jeffrey M Miller
- Department of Biomedical and Health Informatics, Children's Hospital of Philadelphia, Philadelphia, Pennsylvania, USA
| | - Dean Karavite
- Department of Biomedical and Health Informatics, Children's Hospital of Philadelphia, Philadelphia, Pennsylvania, USA
| | - Robert W Grundmeier
- Department of Biomedical and Health Informatics, Children's Hospital of Philadelphia, Philadelphia, Pennsylvania, USA.,Department of Pediatrics, Perelman School of Medicine at the University of Pennsylvania, Philadelphia, Pennsylvania, USA.,Division of General Pediatrics, Children's Hospital of Philadelphia, Philadelphia, Pennsylvania, USA
| | - Levon Utidjian
- Department of Biomedical and Health Informatics, Children's Hospital of Philadelphia, Philadelphia, Pennsylvania, USA.,Department of Pediatrics, Perelman School of Medicine at the University of Pennsylvania, Philadelphia, Pennsylvania, USA.,Division of General Pediatrics, Children's Hospital of Philadelphia, Philadelphia, Pennsylvania, USA
| | - Joseph J Zorc
- Department of Biomedical and Health Informatics, Children's Hospital of Philadelphia, Philadelphia, Pennsylvania, USA.,Department of Pediatrics, Perelman School of Medicine at the University of Pennsylvania, Philadelphia, Pennsylvania, USA.,Department of Emergency Medicine, Children's Hospital of Philadelphia, Philadelphia, Pennsylvania, USA
| |
Collapse
|
20
|
Unertl KM, Abraham J, Bakken S. Building on Diana Forsythe's legacy: the value of human experience and context in biomedical and health informatics. J Am Med Inform Assoc 2021; 28:197-208. [PMID: 33587750 PMCID: PMC7883988 DOI: 10.1093/jamia/ocaa337] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/16/2020] [Accepted: 12/16/2020] [Indexed: 11/13/2022] Open
Affiliation(s)
- Kim M Unertl
- Department of Biomedical Informatics, Vanderbilt University Medical Center, Nashville, Tennessee, USA
| | - Joanna Abraham
- Department of Anesthesiology and Institute for Informatics, School of Medicine, Washington University in St. Louis, St. Louis, Missouri, USA
| | - Suzanne Bakken
- School of Nursing, Department of Biomedical Informatics, and Data Science Institute, Columbia University, New York, New York, USA
| |
Collapse
|