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Mandl KD, Gottlieb D, Mandel JC. Integration of AI in healthcare requires an interoperable digital data ecosystem. Nat Med 2024; 30:631-634. [PMID: 38291298 DOI: 10.1038/s41591-023-02783-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/01/2024]
Affiliation(s)
- Kenneth D Mandl
- Computational Health Informatics Program, Boston Children's Hospital, Boston, MA, USA.
- Department of Biomedical Informatics, Harvard Medical School, Boston, MA, USA.
| | - Daniel Gottlieb
- Computational Health Informatics Program, Boston Children's Hospital, Boston, MA, USA
- Department of Biomedical Informatics, Harvard Medical School, Boston, MA, USA
- Central Square Solutions, Cambridge, MA, USA
| | - Joshua C Mandel
- Computational Health Informatics Program, Boston Children's Hospital, Boston, MA, USA
- Department of Biomedical Informatics, Harvard Medical School, Boston, MA, USA
- Microsoft Research, Redmond, WA, USA
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2
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Yudkin JS, Messiah SE, Allicock MA, Barlow SE. Integration of e-Health Strategies for Post-COVID-19 Pandemic Pediatric Weight Management Programs. Telemed J E Health 2024; 30:321-330. [PMID: 37552819 DOI: 10.1089/tmj.2023.0068] [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] [Indexed: 08/10/2023] Open
Abstract
Background: The COVID-19 pandemic catalyzed a renewed urgency to address the obesity pandemic and accelerated the use of technology to treat pediatric obesity. Yet, there are significant incongruities between the existing literature on technology for obesity management and the current health care system that may lead to suboptimal outcomes and increased costs. This study reviewed the types of e-health strategies currently in use, highlighted inconsistencies and overlap in terminology, and identified future research directions in e-health for childhood obesity, including gaps in implementation science. Methods: This narrative literature review synthesized seminal articles from the literature, as well as recent articles, using PubMed and Google Scholar that focused on the use of technology in treating pediatric obesity. This inclusive strategy was intended to elucidate the heterogeneity in how different disciplines are using digital health terminology in pediatric obesity research. Results: Both the prevalence of e-health interventions and its associated terminology are increasing in the peer-reviewed literature, especially since the beginning of the COVID-19 pandemic. Yet, their definitions and usage are unstandardized, leading to a lack of cohesion in the research and between disciplines. There is a gap in implementation science outcomes, including reimbursement, that may significantly impact external validity and uptake. Conclusion: A more systematic and precise approach to researching e-health that can assess specific technologies and combinations of technologies, their short-term and long-term effect sizes, and feasibility can produce the necessary data that may lead to reimbursement policies and, ultimately, improved pediatric weight management outcomes.
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Affiliation(s)
- Joshua S Yudkin
- School of Public Health, The University of Texas Health Science Center at Houston (UTHealth), Dallas, Texas, USA
| | - Sarah E Messiah
- School of Public Health, The University of Texas Health Science Center at Houston (UTHealth), Dallas, Texas, USA
- Center for Pediatric Population Health, School of Public Health, The University of Texas Health Science Center at Houston, Dallas, Texas, USA
- Department of Pediatrics, McGovern Medical School, Houston, Texas, USA
- Department of Population and Data Sciences, University of Texas Southwestern Medical Center, Dallas, Texas, USA
| | - Marlyn A Allicock
- School of Public Health, The University of Texas Health Science Center at Houston (UTHealth), Dallas, Texas, USA
- Center for Pediatric Population Health, School of Public Health, The University of Texas Health Science Center at Houston, Dallas, Texas, USA
| | - Sarah E Barlow
- Department of Population and Data Sciences, University of Texas Southwestern Medical Center, Dallas, Texas, USA
- Division of Pediatric Gastroenterology, Department of Pediatrics, University of Texas Southwestern Medical Center, Dallas, Texas, USA
- Children's Health, Children's Medical Center Dallas, Dallas, Texas, USA
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3
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Sanchez-Pinto LN, Bhavani SV, Atreya MR, Sinha P. Leveraging Data Science and Novel Technologies to Develop and Implement Precision Medicine Strategies in Critical Care. Crit Care Clin 2023; 39:627-646. [PMID: 37704331 DOI: 10.1016/j.ccc.2023.03.002] [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] [Indexed: 09/15/2023]
Abstract
Precision medicine aims to identify treatments that are most likely to result in favorable outcomes for subgroups of patients with similar clinical and biological characteristics. The gaps for the development and implementation of precision medicine strategies in the critical care setting are many, but the advent of data science and multi-omics approaches, combined with the rich data ecosystem in the intensive care unit, offer unprecedented opportunities to realize the promise of precision critical care. In this article, the authors review the data-driven and technology-based approaches being leveraged to discover and implement precision medicine strategies in the critical care setting.
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Affiliation(s)
- Lazaro N Sanchez-Pinto
- Department of Pediatrics, Northwestern University Feinberg School of Medicine, Ann & Robert H. Lurie Children's Hospital of Chicago, Chicago, IL, USA; Department of Preventive Medicine, Northwestern University Feinberg School of Medicine, Ann & Robert H. Lurie Children's Hospital of Chicago, Chicago, IL, USA.
| | | | - Mihir R Atreya
- Division of Critical Care Medicine, Department of Pediatrics, Cincinnati Children's Hospital Medical Center, University of Cincinnati College of Medicine, 3333 Burnet Avenue, Cincinnati, OH 45229, USA
| | - Pratik Sinha
- Division of Clinical and Translational Research, Department of Anesthesia, Washington University School of Medicine, 1 Barnes Jewish Hospital Plaza, St. Louis, MO 63110, USA; Division of Critical Care, Department of Anesthesia, Washington University School of Medicine, 1 Barnes Jewish Hospital Plaza, St. Louis, MO 63110, USA
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Corbin CK, Maclay R, Acharya A, Mony S, Punnathanam S, Thapa R, Kotecha N, Shah NH, Chen JH. DEPLOYR: a technical framework for deploying custom real-time machine learning models into the electronic medical record. J Am Med Inform Assoc 2023; 30:1532-1542. [PMID: 37369008 PMCID: PMC10436147 DOI: 10.1093/jamia/ocad114] [Citation(s) in RCA: 8] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/13/2023] [Revised: 05/16/2023] [Accepted: 06/13/2023] [Indexed: 06/29/2023] Open
Abstract
OBJECTIVE Heatlhcare institutions are establishing frameworks to govern and promote the implementation of accurate, actionable, and reliable machine learning models that integrate with clinical workflow. Such governance frameworks require an accompanying technical framework to deploy models in a resource efficient, safe and high-quality manner. Here we present DEPLOYR, a technical framework for enabling real-time deployment and monitoring of researcher-created models into a widely used electronic medical record system. MATERIALS AND METHODS We discuss core functionality and design decisions, including mechanisms to trigger inference based on actions within electronic medical record software, modules that collect real-time data to make inferences, mechanisms that close-the-loop by displaying inferences back to end-users within their workflow, monitoring modules that track performance of deployed models over time, silent deployment capabilities, and mechanisms to prospectively evaluate a deployed model's impact. RESULTS We demonstrate the use of DEPLOYR by silently deploying and prospectively evaluating 12 machine learning models trained using electronic medical record data that predict laboratory diagnostic results, triggered by clinician button-clicks in Stanford Health Care's electronic medical record. DISCUSSION Our study highlights the need and feasibility for such silent deployment, because prospectively measured performance varies from retrospective estimates. When possible, we recommend using prospectively estimated performance measures during silent trials to make final go decisions for model deployment. CONCLUSION Machine learning applications in healthcare are extensively researched, but successful translations to the bedside are rare. By describing DEPLOYR, we aim to inform machine learning deployment best practices and help bridge the model implementation gap.
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Affiliation(s)
- Conor K Corbin
- Department of Biomedical Data Science, Stanford, California, USA
| | - Rob Maclay
- Stanford Children’s Health, Palo Alto, California, USA
| | | | | | | | - Rahul Thapa
- Stanford Health Care, Palo Alto, California, USA
| | | | - Nigam H Shah
- Center for Biomedical Informatics Research, Division of Hospital Medicine, Department of Medicine, Stanford University, School of Medicine, Stanford, California, USA
| | - Jonathan H Chen
- Center for Biomedical Informatics Research, Division of Hospital Medicine, Department of Medicine, Stanford University, School of Medicine, Stanford, California, USA
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5
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Botsis T, Murray JC, Ghanem P, Balan A, Kernagis A, Hardart K, He T, Spiker J, Kreimeyer K, Tao J, Baras AS, Yegnasubramanian S, Canzoniero J, Anagnostou V. Precision Oncology Core Data Model to Support Clinical Genomics Decision Making. JCO Clin Cancer Inform 2023; 7:e2200108. [PMID: 37040583 PMCID: PMC10281442 DOI: 10.1200/cci.22.00108] [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: 07/25/2022] [Revised: 10/26/2022] [Accepted: 01/20/2023] [Indexed: 04/13/2023] Open
Abstract
PURPOSE Precision oncology mandates developing standardized common data models (CDMs) to facilitate analyses and enable clinical decision making. Expert-opinion-based precision oncology initiatives are epitomized in Molecular Tumor Boards (MTBs), which process large volumes of clinical-genomic data to match genotypes with molecularly guided therapies. METHODS We used the Johns Hopkins University MTB as a use case and developed a precision oncology core data model (Precision-DM) to capture key clinical-genomic data elements. We leveraged existing CDMs, building upon the Minimal Common Oncology Data Elements model (mCODE). Our model was defined as a set of profiles with multiple data elements, focusing on next-generation sequencing and variant annotations. Most elements were mapped to terminologies or code sets and the Fast Healthcare Interoperability Resources (FHIR). We subsequently compared our Precision-DM with existing CDMs, including the National Cancer Institute's Genomic Data Commons (NCI GDC), mCODE, OSIRIS, the clinical Genome Data Model (cGDM), and the genomic CDM (gCDM). RESULTS Precision-DM contained 16 profiles and 355 data elements. 39% of the elements derived values from selected terminologies or code sets, and 61% were mapped to FHIR. Although we used most elements contained in mCODE, we significantly expanded the profiles to include genomic annotations, resulting in a partial overlap of 50.7% between our core model and mCODE. Limited overlap was noted between Precision-DM and OSIRIS (33.2%), NCI GDC (21.4%), cGDM (9.3%), and gCDM (7.9%). Precision-DM covered most of the mCODE elements (87.7%), with less coverage for OSIRIS (35.8%), NCI GDC (11%), cGDM (26%) and gCDM (33.3%). CONCLUSION Precision-DM supports clinical-genomic data standardization to support the MTB use case and may allow for harmonized data pulls across health care systems, academic institutions, and community medical centers.
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Affiliation(s)
- Taxiarchis Botsis
- Department of Oncology, The Sidney Kimmel Comprehensive Cancer Center, Johns Hopkins University School of Medicine, Baltimore, MD
| | - Joseph C. Murray
- Department of Oncology, The Sidney Kimmel Comprehensive Cancer Center, Johns Hopkins University School of Medicine, Baltimore, MD
| | - Paola Ghanem
- Department of Internal Medicine, Johns Hopkins University School of Medicine, Baltimore, MD
| | - Archana Balan
- Department of Oncology, The Sidney Kimmel Comprehensive Cancer Center, Johns Hopkins University School of Medicine, Baltimore, MD
| | - Alexander Kernagis
- Department of Oncology, The Sidney Kimmel Comprehensive Cancer Center, Johns Hopkins University School of Medicine, Baltimore, MD
| | - Kent Hardart
- Department of Oncology, The Sidney Kimmel Comprehensive Cancer Center, Johns Hopkins University School of Medicine, Baltimore, MD
| | - Ting He
- Department of Oncology, The Sidney Kimmel Comprehensive Cancer Center, Johns Hopkins University School of Medicine, Baltimore, MD
| | - Jonathan Spiker
- Department of Oncology, The Sidney Kimmel Comprehensive Cancer Center, Johns Hopkins University School of Medicine, Baltimore, MD
| | - Kory Kreimeyer
- Department of Oncology, The Sidney Kimmel Comprehensive Cancer Center, Johns Hopkins University School of Medicine, Baltimore, MD
| | - Jessica Tao
- Department of Oncology, The Sidney Kimmel Comprehensive Cancer Center, Johns Hopkins University School of Medicine, Baltimore, MD
| | - Alexander S. Baras
- Department of Pathology, The Sidney Kimmel Comprehensive Cancer Center, Johns Hopkins University School of Medicine, Baltimore, MD
| | - Srinivasan Yegnasubramanian
- Department of Oncology, The Sidney Kimmel Comprehensive Cancer Center, Johns Hopkins University School of Medicine, Baltimore, MD
| | - Jenna Canzoniero
- Department of Oncology, The Sidney Kimmel Comprehensive Cancer Center, Johns Hopkins University School of Medicine, Baltimore, MD
| | - Valsamo Anagnostou
- Department of Oncology, The Sidney Kimmel Comprehensive Cancer Center, Johns Hopkins University School of Medicine, Baltimore, MD
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6
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Leveraging Clinical Informatics and Data Science to Improve Care and Facilitate Research in Pediatric Acute Respiratory Distress Syndrome: From the Second Pediatric Acute Lung Injury Consensus Conference. Pediatr Crit Care Med 2023; 24:S1-S11. [PMID: 36661432 DOI: 10.1097/pcc.0000000000003155] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 01/21/2023]
Abstract
OBJECTIVES The use of electronic algorithms, clinical decision support systems, and other clinical informatics interventions is increasing in critical care. Pediatric acute respiratory distress syndrome (PARDS) is a complex, dynamic condition associated with large amounts of clinical data and frequent decisions at the bedside. Novel data-driven technologies that can help screen, prompt, and support clinician decision-making could have a significant impact on patient outcomes. We sought to identify and summarize relevant evidence related to clinical informatics interventions in both PARDS and adult respiratory distress syndrome (ARDS), for the second Pediatric Acute Lung Injury Consensus Conference. DATA SOURCES MEDLINE (Ovid), Embase (Elsevier), and CINAHL Complete (EBSCOhost). STUDY SELECTION We included studies of pediatric or adult critically ill patients with or at risk of ARDS that examined automated screening tools, electronic algorithms, or clinical decision support systems. DATA EXTRACTION Title/abstract review, full text review, and data extraction using a standardized data extraction form. DATA SYNTHESIS The Grading of Recommendations Assessment, Development and Evaluation approach was used to identify and summarize evidence and develop recommendations. Twenty-six studies were identified for full text extraction to address the Patient/Intervention/Comparator/Outcome questions, and 14 were used for the recommendations/statements. Two clinical recommendations were generated, related to the use of electronic screening tools and automated monitoring of compliance with best practice guidelines. Two research statements were generated, related to the development of multicenter data collaborations and the design of generalizable algorithms and electronic tools. One policy statement was generated, related to the provision of material and human resources by healthcare organizations to empower clinicians to develop clinical informatics interventions to improve the care of patients with PARDS. CONCLUSIONS We present two clinical recommendations and three statements (two research one policy) for the use of electronic algorithms and clinical informatics tools for patients with PARDS based on a systematic review of the literature and expert consensus.
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7
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Klonoff AN, (Andy) Lee WA, Xu NY, Nguyen KT, DuBord A, Kerr D. Six Digital Health Technologies That Will Transform Diabetes. J Diabetes Sci Technol 2023; 17:239-249. [PMID: 34558330 PMCID: PMC9846384 DOI: 10.1177/19322968211043498] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 02/01/2023]
Abstract
The digital health revolution is transforming the landscape of medicine through innovations in sensor data, software, and wireless communication tools. As one of the most prevalent chronic diseases in the United States, diabetes is particularly impactful as a model disease for which to apply innovation. As with any other newly developed technologies, there are three key questions to consider: 1) How can the technology benefit people with diabetes?, 2) What barriers must be overcome to further advance the technology?, and 3) How will the technology be applied in the future?. In this article, we highlight six areas of innovation that have the potential to reduce the burden of diabetes for individuals living with the condition and their families as well as provide measurable benefits for all stakeholders involved in diabetes care. The six technologies which have the potential to transform diabetes care are (i) telehealth, (ii) incorporation of diabetes digital data into the electronic health record, (iii) qualitative hypoglycemia alarms, (iv) artificial intelligence, (v) cybersecurity of diabetes devices, and (vi) diabetes registries. To be successful, a new digital health technology must be accessible and affordable. Furthermore, the people and communities that would most likely benefit from the technology must be willing to use the innovation in their management of diabetes.
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Affiliation(s)
- Alexander N. Klonoff
- University of Southern California, Los
Angeles, CA, USA
- Alexander N. Klonoff, MD, MBA, LAC+USC
Medical Center, 2020 Zonal Avenue, IRD 620, Los Angeles, CA 90089, USA.
| | | | - Nicole Y. Xu
- Diabetes Technology Society,
Burlingame, CA, USA
| | | | | | - David Kerr
- Sansum Diabetes Research Institute,
Santa Barbara, CA, USA
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8
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Chaparro JD, Beus JM, Dziorny AC, Hagedorn PA, Hernandez S, Kandaswamy S, Kirkendall ES, McCoy AB, Muthu N, Orenstein EW. Clinical Decision Support Stewardship: Best Practices and Techniques to Monitor and Improve Interruptive Alerts. Appl Clin Inform 2022; 13:560-568. [PMID: 35613913 PMCID: PMC9132737 DOI: 10.1055/s-0042-1748856] [Citation(s) in RCA: 16] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/02/2022] Open
Abstract
Interruptive clinical decision support systems, both within and outside of electronic health records, are a resource that should be used sparingly and monitored closely. Excessive use of interruptive alerting can quickly lead to alert fatigue and decreased effectiveness and ignoring of alerts. In this review, we discuss the evidence for effective alert stewardship as well as practices and methods we have found useful to assess interruptive alert burden, reduce excessive firings, optimize alert effectiveness, and establish quality governance at our institutions. We also discuss the importance of a holistic view of the alerting ecosystem beyond the electronic health record.
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Affiliation(s)
- Juan D Chaparro
- Division of Clinical Informatics, Nationwide Children's Hospital, Columbus, Ohio, United States.,Departments of Pediatrics and Biomedical Informatics, The Ohio State University College of Medicine, Columbus, Ohio, United States
| | - Jonathan M Beus
- Department of Pediatrics, Emory University School of Medicine, Atlanta, Georgia, United States.,Children's Healthcare of Atlanta, Atlanta, Georgia, United States
| | - Adam C Dziorny
- Department of Pediatrics, University of Rochester School of Medicine, Rochester, New York, United States
| | - Philip A Hagedorn
- Department of Pediatrics, University of Cincinnati, College of Medicine, Cincinnati, Ohio, United States.,Division of Hospital Medicine, Cincinnati Children's Hospital Medical Center, Cincinnati, Ohio, United States
| | - Sean Hernandez
- Center for Healthcare Innovation, Wake Forest School of Medicine, Winston-Salem, North Carolina, United States.,Department of General Internal Medicine, Wake Forest School of Medicine, Winston-Salem, North Carolina, United States
| | - Swaminathan Kandaswamy
- Department of Pediatrics, Emory University School of Medicine, Atlanta, Georgia, United States
| | - Eric S Kirkendall
- Center for Healthcare Innovation, Wake Forest School of Medicine, Winston-Salem, North Carolina, United States.,Department of Pediatrics, Wake Forest School of Medicine, Winston-Salem, North Carolina, United States.,Center for Biomedical Informatics, Wake Forest School of Medicine, Winston-Salem NC, United States
| | - Allison B McCoy
- Department of Biomedical Informatics, Vanderbilt University Medical Center, Nashville, Tennessee, United States
| | - Naveen Muthu
- Department of Pediatrics, University of Pennsylvania Perelman School of Medicine, Philadelphia, Pennsylvania, United States.,Department of Biomedical and Health Informatics, Children's Hospital of Philadelphia, Philadelphia, Pennsylvania, United States
| | - Evan W Orenstein
- Department of Pediatrics, Emory University School of Medicine, Atlanta, Georgia, United States.,Children's Healthcare of Atlanta, Atlanta, Georgia, United States
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Hägglund M, Scandurra I. Usability of the Swedish Accessible Electronic Health Record: a Qualitative Study (Preprint). JMIR Hum Factors 2022; 9:e37192. [PMID: 35737444 PMCID: PMC9264119 DOI: 10.2196/37192] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/13/2022] [Revised: 05/06/2022] [Accepted: 05/14/2022] [Indexed: 02/06/2023] Open
Abstract
Background Patient portals are increasingly being implemented worldwide to ensure that patients have timely access to their health data, including patients’ access to their electronic health records. In Sweden, the e-service Journalen is a national patient-accessible electronic health record (PAEHR), accessible on the web through the national patient portal. User characteristics and perceived benefits of using a PAEHR will influence behavioral intentions to use and adoption; however, poor usability, which increases effort expectancy, may have a negative impact. Therefore, it is of interest to further explore how users of the PAEHR Journalen perceive its usability and usefulness. Objective On the basis of the analysis of the survey respondents’ experiences of the usability of the Swedish PAEHR, this study aimed to identify specific usability problems that may need to be addressed in the future. Methods A survey study was conducted to elicit opinions and experiences of patients using Journalen. Data were collected from June to October 2016. The questionnaire included a free-text question regarding the usability of the system, and the responses were analyzed using content analysis with a sociotechnical framework as guidance when grouping identified usability issues. Results During the survey period, 423,141 users logged into Journalen, of whom 2587 (0.61%) completed the survey (unique users who logged in; response rate 0.61%). Of the 2587 respondents, 186 (7.19%) provided free-text comments on the usability questions. The analysis resulted in 19 categories, which could be grouped under 7 of the 8 dimensions in the sociotechnical framework of Sittig and Singh. The most frequently mentioned problems were related to regional access limitations, structure and navigation of the patient portal, and language and understanding. Conclusions Although the survey respondents, who were also end users of the PAEHR Journalen, were overall satisfied with its usability, they also experienced important challenges when accessing their records. For all patients to be able to reap the benefits of record access, it is essential to understand both the usability challenges they encounter and, more broadly, how policies, regulations, and technical implementation decisions affect the usefulness of record access. The results presented here are specific to the Swedish PAEHR Journalen but also provide important insights into how design and implementation of record access can be improved in any context.
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Affiliation(s)
- Maria Hägglund
- Healthcare Sciences and e-Health, Department of Women's and Children's Health, Uppsala University, Uppsala, Sweden
| | - Isabella Scandurra
- Centre of Empirical Research on Information Systems, School of Business, Örebro University, Örebro, Sweden
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10
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Wang P, Li T, Yu L, Zhou L, Yan T. Towards an effective framework for integrating patient-reported outcomes in electronic health records. Digit Health 2022; 8:20552076221112152. [PMID: 35860613 PMCID: PMC9290150 DOI: 10.1177/20552076221112152] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/09/2021] [Accepted: 06/21/2022] [Indexed: 11/24/2022] Open
Abstract
Background In the past decade, electronic modalities are increasingly deployed to integrate patient-reported outcomes into electronic health records. Most popularly, patient portals are used for remote questionnaires, and tablets are provided to patients in-office in case they need help. They are both useful. But some barriers are still in the way, which place burdens on patients and clinicians in the process of routine data collection. Objective This study aims to describe a portable and scalable framework which can simplify the patient-reported outcome integration by mitigating the related burdens. Methods A framework was proposed to use a modular approach to replace the tethered approach. The framework was open-sourced on GitHub. After development and testing, it was evaluated on an instrument with 24 questions in a real clinical setting. Patients were randomly selected in every modality-based group. For objective analysis, completion time and response rate were collected. No-show data was collected and analyzed. For subjective analysis, the NASA Task Load Index was used to measure workload, and the Net Promoter Score was used to assess user satisfaction. Results The model could contain 46,656 questions. A quick response code could store 1120 encoded items. For remote visits, the response rate was improved compared to the portal group (76.6% vs. 61.1%). The completion time was reduced by 37.5% when compared to the tablet group and was reduced by 43.4% when compared to the portal group. The workload for clinicians and patients was both reduced significantly (p < 0.001). A higher Net Promoter Score was rated by both clinicians (89.3%) and patients (86.5%). Compared to the portal group, the no-show rate was reduced (11.7% vs. 8.6%). Conclusions Collecting patient-reported outcomes over a quick response code appears to be an alternative modality to enable a simplified integration. This study provides new insights to collect patient-reported outcomes with interoperability and substitutability in mind.
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Affiliation(s)
- Panzhang Wang
- Shanghai Jiao Tong University Affiliated Sixth People's Hospital, Shanghai, China
| | - Tao Li
- Shanghai Jiao Tong University Affiliated Sixth People's Hospital, Shanghai, China
| | - Lei Yu
- Shanghai Jiao Tong University Affiliated Sixth People's Hospital, Shanghai, China
| | - Liang Zhou
- Shanghai Jiao Tong University Affiliated Sixth People's Hospital, Shanghai, China
| | - Tao Yan
- Shanghai Jiao Tong University Affiliated Sixth People's Hospital, Shanghai, China
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11
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Taxter AJ, Natter MD. Using the Electronic Health Record to Enhance Care in Pediatric Rheumatology. Rheum Dis Clin North Am 2021; 48:245-258. [PMID: 34798950 DOI: 10.1016/j.rdc.2021.08.004] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
Abstract
The electronic health record (EHR) ecosystem is undergoing rapid evolution in response to new rules and regulations promulgated by the US HITECH Act (2009) and the 21st Century Cures Act (2016), which together promote and support enhanced information use, access, exchange, as well as vendor-agnostic application development. By leveraging emerging new standards and technology for EHR data interchange, for example, FHIR and SMART, pediatric rheumatology clinical care, research, and quality improvement communities will have the opportunity to streamline documentation workflows, integrate patient-reported outcomes into clinical care, reuse clinical data for research purposes, and embed implementation science approaches within the EHR.
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Affiliation(s)
- Alysha J Taxter
- Nationwide Children's Hospital, 700 Children's Drive, Columbus, OH 43205, USA.
| | - Marc D Natter
- Computational Health Informatics Program, Boston Children's Hospital, 300 Longwood Avenue BCH3187, Boston, MA 02115, USA; Mass General Hospital for Children, 55 Fruit Street, Boston, MA 02114, USA
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