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Allen G, Setzer J, Jones R, Knapp G. Improving Safety During Transitions of Care Through the Use of Electronic Referral Loops to Receive and Reconcile Health Information. Jt Comm J Qual Patient Saf 2023; 49:247-254. [PMID: 36948985 DOI: 10.1016/j.jcjq.2023.01.014] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/17/2022] [Revised: 01/27/2023] [Accepted: 01/30/2023] [Indexed: 02/11/2023]
Abstract
BACKGROUND Medicare's Promoting Interoperability Program evaluates how often organizations completely reconcile differences between the internal medical record with problems, medications, and allergies received from outside electronic health records (EHRs) during hospitalizations. This quality improvement project sought to increase rates of complete reconciliation of patient problems, medications, and allergies to 80% of hospitalizations for 90 consecutive days at all eight hospitals in an academic medical system by December 31, 2021. METHODS Baseline characteristics were determined using monthly reconciliation performance from October 2019 to October 2020. The intervention period occurred from November 2020 to December 2021 and consisted of 26 Plan-Do-Study-Act cycles. Performance was monitored from January 2022 to June 2022 to observe the sustainability of the initiative. Statistical process control charts were used to identify special cause variation in system-level performance. RESULTS All eight hospitals successfully recorded 90 consecutive days of complete reconciliation above 80% in 2021, and seven of eight hospitals maintained this goal in the sustainability period. Average baseline reconciliation was 22.1%. System-level performance satisfied criteria for baseline shift after PDSA 17, when the average performance was recalculated as 52.4%. Criteria for a second baseline shift were satisfied during the sustainability period, when the average performance was recalculated at 79.9%. Overall performance has remained within the recalculated control limits throughout the sustainability period. CONCLUSION An intervention that included enhancing EHR workflows, training medical providers, and communicating division performance was successful in increasing and sustaining complete reconciliation of clinical information in a multihospital medical system.
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Li S, Rajapuri AS, Felix Gomez GG, Schleyer T, Mendonca EA, Thyvalikakath TP. How Do Dental Clinicians Obtain Up-To-Date Patient Medical Histories? Modeling Strengths, Drawbacks, and Proposals for Improvements. Front Digit Health 2022; 4:847080. [PMID: 35419556 PMCID: PMC8995974 DOI: 10.3389/fdgth.2022.847080] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/01/2022] [Accepted: 03/08/2022] [Indexed: 11/13/2022] Open
Abstract
BackgroundAccess to up-to-date patient medical history is essential for dental clinicians (DCs) to avoid potential harm to patients and to improve dental treatment outcomes. The predominant approach for dental clinicians (DCs) to gather patients' medical history is through patient-reported medical histories and medical consults. However, studies reported varied concordance and reliability of patient-reported medical conditions and medication histories compared to the patient medical records and this process also places a significant burden on patients. Information technology tools/platforms such as an integrated electronic health record containing an electronic dental record module may address these issues. However, these integrated systems are expensive and technically complex and may not be easily adopted by DCs in solo and small group practice who provide the most dental care. The recent expansion of regional healthcare information exchange (HIE) provides another approach, but to date, studies on connecting DCs with HIE are very limited. Our study objectives were to model different aspects of the current approaches to identify the strengths and weaknesses, and then model the HIE approach that addresses the weaknesses and retain the strengths of current approaches. The models of current approaches identified the people, resources, organizational aspects, workflow, and areas for improvement; while models of the HIE approach identified system requirements, functions, and processes that may be shared with software developers and other stakeholders for future development.MethodsThere are three phases in this study. In Phase 1, we retrieved peer-reviewed PubMed indexed manuscripts published between January 2013 and November 2020 and extracted modeling related data from selected manuscripts. In Phase 2, we built models for the current approaches by using the Integrated DEFinition Method 0 function modeling method (IDEF0), the Unified Modeling Language (UML) Use Case Diagram, and Business Process Model and Notation (BPMN) methods. In Phase 3, we created three conceptual models for the HIE approach.ResultsFrom the 47 manuscripts identified, three themes emerged: 1) medical consult process following patient-reported medical history, 2) integrated electronic dental record-electronic health record (EDR-EHR), and 3) HIE. Three models were built for each of the three themes. The use case diagrams described the actions of the dental patients, DCs, medical providers and the use of information systems (EDR-EHR/HIE). The IDEF0 models presented the major functions involved. The BPMN models depicted the detailed steps of the process and showed how the patient's medical history information flowed through different steps. The strengths and weaknesses revealed by the models of the three approaches were also compared.ConclusionsWe successfully modeled the DCs' current approaches of accessing patient medical history and designed an HIE approach that addressed the current approaches' weaknesses as well as leveraged their strengths. Organizational management and end-users can use this information to decide the optimum approach to integrate dental and medical care. The illustrated models are comprehensive and can also be adopted by EHR and EDR vendors to develop a connection between dental systems and HIEs.
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Affiliation(s)
- Shuning Li
- Dental Informatics, Indiana University School of Dentistry, Indianapolis, IN, United States
- *Correspondence: Shuning Li
| | - Anushri Singh Rajapuri
- Dental Informatics, Indiana University School of Dentistry, Indianapolis, IN, United States
- Center for Biomedical Informatics, Regenstrief Institute, Inc., Indianapolis, IN, United States
| | - Grace Gomez Felix Gomez
- Dental Informatics, Indiana University School of Dentistry, Indianapolis, IN, United States
- Center for Biomedical Informatics, Regenstrief Institute, Inc., Indianapolis, IN, United States
| | - Titus Schleyer
- Center for Biomedical Informatics, Regenstrief Institute, Inc., Indianapolis, IN, United States
- Indiana University School of Medicine, Indianapolis, IN, United States
| | - Eneida A. Mendonca
- Center for Biomedical Informatics, Regenstrief Institute, Inc., Indianapolis, IN, United States
- Indiana University School of Medicine, Indianapolis, IN, United States
| | - Thankam P. Thyvalikakath
- Dental Informatics, Indiana University School of Dentistry, Indianapolis, IN, United States
- Center for Biomedical Informatics, Regenstrief Institute, Inc., Indianapolis, IN, United States
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Tortolero GA, Brown MR, Sharma SV, de Oliveira Otto MC, Yamal JM, Aguilar D, Gunther MD, Mofleh DI, Harris RD, John JC, de Vries PS, Ramphul R, Serbo DM, Kiger J, Banerjee D, Bonvino N, Merchant A, Clifford W, Mikhail J, Xu H, Murphy RE, Wei Q, Vahidy FS, Morrison AC, Boerwinkle E. Leveraging a health information exchange for analyses of COVID-19 outcomes including an example application using smoking history and mortality. PLoS One 2021; 16:e0247235. [PMID: 34081724 PMCID: PMC8174716 DOI: 10.1371/journal.pone.0247235] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/28/2020] [Accepted: 02/03/2021] [Indexed: 01/08/2023] Open
Abstract
Understanding sociodemographic, behavioral, clinical, and laboratory risk factors in patients diagnosed with COVID-19 is critically important, and requires building large and diverse COVID-19 cohorts with both retrospective information and prospective follow-up. A large Health Information Exchange (HIE) in Southeast Texas, which assembles and shares electronic health information among providers to facilitate patient care, was leveraged to identify COVID-19 patients, create a cohort, and identify risk factors for both favorable and unfavorable outcomes. The initial sample consists of 8,874 COVID-19 patients ascertained from the pandemic's onset to June 12th, 2020 and was created for the analyses shown here. We gathered demographic, lifestyle, laboratory, and clinical data from patient's encounters across the healthcare system. Tobacco use history was examined as a potential risk factor for COVID-19 fatality along with age, gender, race/ethnicity, body mass index (BMI), and number of comorbidities. Of the 8,874 patients included in the cohort, 475 died from COVID-19. Of the 5,356 patients who had information on history of tobacco use, over 26% were current or former tobacco users. Multivariable logistic regression showed that the odds of COVID-19 fatality increased among those who were older (odds ratio = 1.07, 95% CI 1.06, 1.08), male (1.91, 95% CI 1.58, 2.31), and had a history of tobacco use (2.45, 95% CI 1.93, 3.11). History of tobacco use remained significantly associated (1.65, 95% CI 1.27, 2.13) with COVID-19 fatality after adjusting for age, gender, and race/ethnicity. This effort demonstrates the impact of having an HIE to rapidly identify a cohort, aggregate sociodemographic, behavioral, clinical and laboratory data across disparate healthcare providers electronic health record (HER) systems, and follow the cohort over time. These HIE capabilities enable clinical specialists and epidemiologists to conduct outcomes analyses during the current COVID-19 pandemic and beyond. Tobacco use appears to be an important risk factor for COVID-19 related death.
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Affiliation(s)
- Guillermo A. Tortolero
- School of Public Health, University of Texas Health Science Center at Houston, Houston, TX, United States of America
| | - Michael R. Brown
- School of Public Health, University of Texas Health Science Center at Houston, Houston, TX, United States of America
| | - Shreela V. Sharma
- School of Public Health, University of Texas Health Science Center at Houston, Houston, TX, United States of America
| | - Marcia C. de Oliveira Otto
- School of Public Health, University of Texas Health Science Center at Houston, Houston, TX, United States of America
| | - Jose-Miguel Yamal
- School of Public Health, University of Texas Health Science Center at Houston, Houston, TX, United States of America
| | - David Aguilar
- School of Public Health, University of Texas Health Science Center at Houston, Houston, TX, United States of America
| | - Matt D. Gunther
- School of Public Health, University of Texas Health Science Center at Houston, Houston, TX, United States of America
| | - Dania I. Mofleh
- School of Public Health, University of Texas Health Science Center at Houston, Houston, TX, United States of America
| | - Rachel D. Harris
- School of Public Health, University of Texas Health Science Center at Houston, Houston, TX, United States of America
| | - Jemima C. John
- School of Public Health, University of Texas Health Science Center at Houston, Houston, TX, United States of America
| | - Paul S. de Vries
- School of Public Health, University of Texas Health Science Center at Houston, Houston, TX, United States of America
| | - Ryan Ramphul
- School of Public Health, University of Texas Health Science Center at Houston, Houston, TX, United States of America
| | - Dritana Marko Serbo
- School of Public Health, University of Texas Health Science Center at Houston, Houston, TX, United States of America
| | - Jennifer Kiger
- Harris County Public Health, Houston Texas, United States of America
| | - Deborah Banerjee
- City of Houston Health Department, Houston, Texas, United States of America
| | - Nick Bonvino
- Greater Houston Healthconnect, Houston, Texas, United States of America
| | - Angela Merchant
- Greater Houston Healthconnect, Houston, Texas, United States of America
| | - Warren Clifford
- Greater Houston Healthconnect, Houston, Texas, United States of America
| | - Jenny Mikhail
- Greater Houston Healthconnect, Houston, Texas, United States of America
| | - Hua Xu
- School of Biomedical Informatics, University of Texas Health Science Center at Houston, Houston, TX, United States of America
| | - Robert E. Murphy
- School of Biomedical Informatics, University of Texas Health Science Center at Houston, Houston, TX, United States of America
| | - Qiang Wei
- School of Biomedical Informatics, University of Texas Health Science Center at Houston, Houston, TX, United States of America
| | - Farhaan S. Vahidy
- Center for Outcomes Research, Houston Methodist, Houston, Texas, United States of America
| | - Alanna C. Morrison
- School of Public Health, University of Texas Health Science Center at Houston, Houston, TX, United States of America
| | - Eric Boerwinkle
- School of Public Health, University of Texas Health Science Center at Houston, Houston, TX, United States of America
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Hosseini M, Faiola A, Jones J, Vreeman DJ, Wu H, Dixon BE. Impact of document consolidation on healthcare providers' perceived workload and information reconciliation tasks: a mixed methods study. J Am Med Inform Assoc 2020; 26:134-142. [PMID: 30566630 DOI: 10.1093/jamia/ocy158] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/06/2018] [Accepted: 11/04/2018] [Indexed: 11/14/2022] Open
Abstract
Background Information reconciliation is a common yet complex and often time-consuming task performed by healthcare providers. While electronic health record systems can receive "outside information" about a patient in electronic documents, rarely does the computer automate reconciling information about a patient across all documents. Materials and Methods Using a mixed methods design, we evaluated an information system designed to reconcile information across multiple electronic documents containing health records for a patient received from a health information exchange (HIE) network. Nine healthcare providers participated in scenario-based sessions in which they manually consolidated information across multiple documents. Accuracy of consolidation was measured along with the time spent completing 3 different reconciliation scenarios with and without support from the information system. Participants also attended an interview about their experience. Perceived workload was evaluated quantitatively using the NASA-TLX tool. Qualitative analysis focused on providers' impression of the system and the challenges faced when reconciling information in practice. Results While 5 providers made mistakes when trying to manually reconcile information across multiple documents, no participants made a mistake when the system supported their work. Overall perceived workload decreased significantly for scenarios supported by the system (37.2% in referrals, 18.4% in medications, and 31.5% in problems scenarios, P < 0.001). Information reconciliation time was reduced significantly when the system supported provider tasks (58.8% in referrals, 38.1% in medications, and 65.1% in problem scenarios). Conclusion Automating retrieval and reconciliation of information across multiple electronic documents shows promise for reducing healthcare providers' task complexity and workload.
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Affiliation(s)
- Masoud Hosseini
- Department of BioHealth Informatics, Indiana University school of Informatics and Computing, Indianapolis, Indiana, USA
| | - Anthony Faiola
- Department of Biomedical and Health Information Sciences, College of Applied Health Sciences, University of Illinois at Chicago, Chicago, IL, USA
| | - Josette Jones
- Department of BioHealth Informatics, Indiana University school of Informatics and Computing, Indianapolis, Indiana, USA
| | - Daniel J Vreeman
- Center for Biomedical Informatics, Regenstrief Institute, Inc., Indianapolis, Indiana, USA.,Department of Medicine, Indiana University School of Medicine, Indianapolis, Indiana, USA
| | - Huanmei Wu
- Department of BioHealth Informatics, Indiana University school of Informatics and Computing, Indianapolis, Indiana, USA
| | - Brian E Dixon
- Center for Biomedical Informatics, Regenstrief Institute, Inc., Indianapolis, Indiana, USA.,Department of Epidemiology, Richard M. Fairbanks School of Public Health, Indiana University, Indianapolis, USA
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Faiola A, Papautsky EL, Isola M. Empowering the Aging with Mobile Health: A mHealth Framework for Supporting Sustainable Healthy Lifestyle Behavior. Curr Probl Cardiol 2019; 44:232-266. [DOI: 10.1016/j.cpcardiol.2018.06.003] [Citation(s) in RCA: 16] [Impact Index Per Article: 3.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/18/2018] [Accepted: 06/18/2018] [Indexed: 11/25/2022]
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Cruz MF, Cavalcante CAMT, Sá Barretto ST. Using OPC and HL7 Standards to Incorporate an Industrial Big Data Historian in a Health IT Environment. J Med Syst 2018; 42:122. [PMID: 29846812 DOI: 10.1007/s10916-018-0979-5] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/19/2018] [Accepted: 05/18/2018] [Indexed: 11/30/2022]
Abstract
Health Level Seven (HL7) is one of the standards most used to centralize data from different vital sign monitoring systems. This solution significantly limits the data available for historical analysis, because it typically uses databases that are not effective in storing large volumes of data. In industry, a specific Big Data Historian, known as a Process Information Management System (PIMS), solves this problem. This work proposes the same solution to overcome the restriction on storing vital sign data. The PIMS needs a compatible communication standard to allow storing, and the one most commonly used is the OLE for Process Control (OPC). This paper presents a HL7-OPC Server that permits communication between vital sign monitoring systems with PIMS, thus allowing the storage of long historical series of vital signs. In addition, it carries out a review about local and cloud-based Big Medical Data researches, followed by an analysis of the PIMS in a Health IT Environment. Then it shows the architecture of HL7 and OPC Standards. Finally, it shows the HL7-OPC Server and a sequence of tests that proved its full operation and performance.
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Affiliation(s)
- Márcio Freire Cruz
- Graduate Program in Mechatronics, Federal University of Bahia (UFBA), Salvador, Bahia, 40170-110, Brazil.
| | | | - Sérgio Torres Sá Barretto
- Graduate Program in Mechatronics, Federal University of Bahia (UFBA), Salvador, Bahia, 40170-110, Brazil
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Kuziemsky CE, Gogia SB, Househ M, Petersen C, Basu A. Balancing Health Information Exchange and Privacy Governance from a Patient-Centred Connected Health and Telehealth Perspective. Yearb Med Inform 2018; 27:48-54. [PMID: 29681043 PMCID: PMC6115230 DOI: 10.1055/s-0038-1641195] [Citation(s) in RCA: 18] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/13/2022] Open
Abstract
Objectives:
Connected healthcare is an essential part of patient-centred care delivery. Technology such as telehealth is a critical part of connected healthcare. However, exchanging health information brings the risk of privacy issues. To better manage privacy risks we first need to understand the different patterns of patient-centred care in order to tailor solutions to address privacy risks.
Methods:
Drawing upon published literature, we develop a business model to enable patient-centred care via telehealth. The model identifies three patient-centred connected health patterns. We then use the patterns to analyse potential privacy risks and possible solutions from different types of telehealth delivery.
Results:
Connected healthcare raises the risk of unwarranted access to health data and related invasion of privacy. However, the risk and extent of privacy issues differ according to the pattern of patient-centred care delivery and the type of particular challenge as they enable the highest degree of connectivity and thus the greatest potential for privacy breaches.
Conclusion:
Privacy issues are a major concern in telehealth systems and patients, providers, and administrators need to be aware of these privacy issues and have guidance on how to manage them. This paper integrates patient-centred connected health care, telehealth, and privacy risks to provide an understanding of how risks vary across different patterns of patient-centred connected health and different types of telehealth delivery.
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Affiliation(s)
- Craig E Kuziemsky
- Telfer School of Management, University of Ottawa, Ottawa, Ontario, Canada
| | - Shashi B Gogia
- Society for Administration of Telemedicine and Healthcare Informatics, New Delhi, India
| | - Mowafa Househ
- College of Public Health and Health Informatics, King Saud Bin Abdul Aziz University for Health Sciences, King Abdullah International Medical Research Center, Riyadh, Saudi Arabia
| | - Carolyn Petersen
- Senior editor at Mayo Clinic, Rochester, Minnesota, United States
| | - Arindam Basu
- University of Canterbury School of Health Sciences, Christchurch, New Zealand
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