Evaluation in Life Cycle of Information Technology (ELICIT) framework: Supporting the innovation life cycle from business case assessment to summative evaluation.

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.

Utility of Poison Control Center Data for Automated Opioid Overdose Surveillance.

CONTEXT: Overdosing on opioids is a national epidemic and the number one cause of death from unintentional injury in the United States. Poison control centers (PCCs) may be a source of timely data that can track opioid exposure cases, identify clusters of opioid exposure cases by geographic region, and capture opioid exposure cases that may not seek medical attention from health care facilities. OBJECTIVE: The objectives were to (a) identify data requirements for opioid overdose case ascertainment and classification and visualization in a dashboard, and (b) assess the availability and quality of the relevant PCC data for state-based opioid overdose surveillance. DESIGN: We identified types of opioid exposure, demographic characteristics, and other features that may be relevant for public health officials to monitor and respond to opioid overdose events in the community. We operationalized case definitions for an opioid overdose event based on the Centers for Disease Control and Prevention case classification definitions. We assessed the PCC database for concepts and metrics needed to operationalize case definitions for opioid overdose events to determine the feasibility of using the PCC for automated surveillance. MAIN OUTCOME MEASURE: Quality and availability of required concepts to operationalize metrics and case definitions using PCC data. RESULTS: A subset of the probable case definition may be used for automated surveillance with available structured PCC data. In contrast, logic for confirmed, suspected, and part of the probable case definitions requires additional structured data or analysis of narrative text, which may not contain needed concepts. For example, the confirmed case definition currently requires evidence from narrative text of laboratory confirmation of an opioid in a clinical specimen or diagnosis of opioid overdose in a health care record. CONCLUSION: PCC data are a timely and potentially useful source for automated surveillance of a subset of opioid overdose events, but additional structured and/or coded data are required.

Developing a sampling method and preliminary taxonomy for classifying COVID-19 public health guidance for healthcare organizations and the general public.

BACKGROUND: Development and dissemination of public health (PH) guidance to healthcare organizations and the general public (e.g., businesses, schools, individuals) during emergencies like the COVID-19 pandemic is vital for policy, clinical, and public decision-making. Yet, the rapidly evolving nature of these events poses significant challenges for guidance development and dissemination strategies predicated on well-understood concepts and clearly defined access and distribution pathways. Taxonomies are an important but underutilized tool for guidance authoring, dissemination and updating in such dynamic scenarios. OBJECTIVE: To design a rapid, semi-automated method for sampling and developing a PH guidance taxonomy using widely available Web crawling tools and streamlined manual content analysis. METHODS: Iterative samples of guidance documents were taken from four state PH agency websites, the US Center for Disease Control and Prevention, and the World Health Organization. Documents were used to derive and refine a preliminary taxonomy of COVID-19 PH guidance via content analysis. RESULTS: Eight iterations of guidance document sampling and taxonomy revisions were performed, with a final corpus of 226 documents. The preliminary taxonomy contains 110 branches distributed between three major domains: stakeholders (24 branches), settings (25 branches) and topics (61 branches). Thematic saturation measures indicated rapid saturation (≤5% change) for the domains of "stakeholders" and "settings", and "topic"-related branches for clinical decision-making. Branches related to business reopening and economic consequences remained dynamic throughout sampling iterations. CONCLUSION: The PH guidance taxonomy can support public health agencies by aligning guidance development with curation and indexing strategies; supporting targeted dissemination; increasing the speed of updates; and enhancing public-facing guidance repositories and information retrieval tools. Taxonomies are essential to support knowledge management activities during rapidly evolving scenarios such as disease outbreaks and natural disasters.

Patient and Caregiver Perceptions of an Interface Design to Communicate Artificial Intelligence-Based Prognosis for Patients With Advanced Solid Tumors.

PURPOSE: Use of artificial intelligence (AI) in cancer care is increasing. What remains unclear is how best to design patient-facing systems that communicate AI output. With oncologist input, we designed an interface that presents patient-specific, machine learning-based 6-month survival prognosis information designed to aid oncology providers in preparing for and discussing prognosis with patients with advanced solid tumors and their caregivers. The primary purpose of this study was to assess patient and caregiver perceptions and identify enhancements of the interface for communicating 6-month survival and other prognosis information when making treatment decisions concerning anticancer and supportive therapy. METHODS: This qualitative study included interviews and focus groups conducted between November and December 2022. Purposive sampling was used to recruit former patients with cancer and/or former caregivers of patients with cancer who had participated in cancer treatment decisions from Utah or elsewhere in the United States. Categories and themes related to perceptions of the interface were identified. RESULTS: We received feedback from 20 participants during eight individual interviews and two focus groups, including four cancer survivors, 13 caregivers, and three representing both. Overall, most participants expressed positive perceptions about the tool and identified its value for supporting decision making, feeling less alone, and supporting communication among oncologists, patients, and their caregivers. Participants identified areas for improvement and implementation considerations, particularly that oncologists should share the tool and guide discussions about prognosis with patients who want to receive the information. CONCLUSION: This study revealed important patient and caregiver perceptions of and enhancements for the proposed interface. Originally designed with input from oncology providers, patient and caregiver participants identified additional interface design recommendations and implementation considerations to support communication about prognosis.

Design of an interface to communicate artificial intelligence-based prognosis for patients with advanced solid tumors: a user-centered approach.

OBJECTIVES: To design an interface to support communication of machine learning (ML)-based prognosis for patients with advanced solid tumors, incorporating oncologists' needs and feedback throughout design. MATERIALS AND METHODS: Using an interdisciplinary user-centered design approach, we performed 5 rounds of iterative design to refine an interface, involving expert review based on usability heuristics, input from a color-blind adult, and 13 individual semi-structured interviews with oncologists. Individual interviews included patient vignettes and a series of interfaces populated with representative patient data and predicted survival for each treatment decision point when a new line of therapy (LoT) was being considered. Ongoing feedback informed design decisions, and directed qualitative content analysis of interview transcripts was used to evaluate usability and identify enhancement requirements. RESULTS: Design processes resulted in an interface with 7 sections, each addressing user-focused questions, supporting oncologists to "tell a story" as they discuss prognosis during a clinical encounter. The iteratively enhanced interface both triggered and reflected design decisions relevant when attempting to communicate ML-based prognosis, and exposed misassumptions. Clinicians requested enhancements that emphasized interpretability over explainability. Qualitative findings confirmed that previously identified issues were resolved and clarified necessary enhancements (eg, use months not days) and concerns about usability and trust (eg, address LoT received elsewhere). Appropriate use should be in the context of a conversation with an oncologist. CONCLUSION: User-centered design, ongoing clinical input, and a visualization to communicate ML-related outcomes are important elements for designing any decision support tool enabled by artificial intelligence, particularly when communicating prognosis risk.

Characterization of public health alerts and their suitability for alerting in electronic health record systems.

Public health agencies including federal, state, and local governments routinely send out public health advisories and alerts via e-mail and text messages to health care providers to increase awareness of public health events and situations. Agencies must ensure that practitioners have timely and accessible information at the critical point-of-care. Electronic health record (EHR) systems have the potential to alert physicians of emerging health conditions deemed important for public health at the most critical time of need. To understand how public health agencies can leverage existing alerting mechanisms in EHR systems, it is important to understand characteristics of public health alerts to determine their suitability for alerting in EHR systems. Authors conducted a review and analysis of public health alerts for a 3-year period to identify critical data attributes necessary to support public health alerting in EHR systems. The alerts were restricted to those most relevant for clinical care. The results showed that there is an opportunity for disseminating actionable information to clinical practitioners at the point of care to guide care and reporting. Public health alerts in EHR systems can be useful in reporting, recommending specific tests, as well as suggesting secondary prevention.

Public health communication with frontline clinicians during the first wave of the 2009 influenza pandemic.

CONTEXT: During public health emergencies, office-based frontline clinicians are critical partners in the detection, treatment, and control of disease. Communication between public health authorities and frontline clinicians is critical, yet public health agencies, medical societies, and healthcare delivery organizations have all called for improvements. OBJECTIVES: Describe communication processes between public health and frontline clinicians during the first wave of the 2009 novel influenza A(H1N1) pandemic; assess clinicians' use of and knowledge about public health guidance; and assess clinicians' perceptions and preferences about communication during a public health emergency. DESIGN AND METHODS: During the first wave of the pandemic, we performed a process analysis and surveyed 509 office-based primary care providers in Utah. SETTING AND PARTICIPANTS: Public health and healthcare leaders from major agencies involved in emergency response in Utah and office-based primary care providers located throughout Utah. MAIN OUTCOME MEASURE(S): Communication process and information flow, distribution of e-mails, proportion of clinicians who accessed key Web sites at least weekly, clinicians' knowledge about recent guidance and perception about e-mail load, primary information sources, and qualitative findings from clinician feedback. RESULTS: The process analysis revealed redundant activities and messaging. The 141 survey respondents (28%) received information from a variety of sources: 68% received information from state public health; almost 100% received information from health care organizations. Only one-third visited a state public health or institutional Web site frequently enough (at least weekly) to obtain updated guidance. Clinicians were knowledgeable about guidance that did not change during the first wave; however, correct knowledge was lower after guidance changed. Clinicians felt overwhelmed by e-mail volume, preferred a single institutional e-mail for clinical guidance, and suggested that new information be concise and clearly identified. CONCLUSION: : Communication between public health, health care organizations and clinicians was redundant and overwhelming and can be enhanced considering clinician preferences and institutional communication channels.

A Conceptual Framework of Data Readiness: The Contextual Intersection of Quality, Availability, Interoperability, and Provenance.

BACKGROUND: Data readiness is a concept often used when referring to health information technology applications in the informatics disciplines, but it is not clearly defined in the literature. To avoid misinterpretations in research and implementation, a formal definition should be developed. OBJECTIVES: The objective of this research is to provide a conceptual definition and framework for the term data readiness that can be used to guide research and development related to data-based applications in health care. METHODS: PubMed, the National Institutes of Health RePORTER, Scopus, the Cochrane Library, and Duke University Library databases for business and information sciences were queried for formal mentions of the term "data readiness." Manuscripts found in the search were reviewed, and relevant information was extracted, evaluated, and assimilated into a framework for data readiness. RESULTS: Of the 264 manuscripts found in the database searches, 20 were included in the final synthesis to define data readiness. In these 20 manuscripts, the term data readiness was revealed to encompass the constructs of data quality, data availability, interoperability, and data provenance. DISCUSSION: Based upon our review of the literature, we define data readiness as the application-specific intersection of data quality, data availability, interoperability, and data provenance. While these concepts are not new, the combination of these factors in a novel data readiness model may help guide future informatics research and implementation science. CONCLUSION: This analysis provides a definition to guide research and development related to data-based applications in health care. Future work should be done to validate this definition, and to apply the components of data readiness to real-world applications so that specific metrics may be developed and disseminated.

A thematic analysis to examine the feasibility of EHR-based clinical decision support for implementing Choosing Wisely ® guidelines.

OBJECTIVE: To identify important barriers and facilitators relating to the feasibility of implementing clinical practice guidelines (CPGs) as clinical decision support (CDS). MATERIALS AND METHODS: We conducted a qualitative, thematic analysis of interviews from seven interviews with dyads (one clinical expert and one systems analyst) who discussed the feasibility of implementing 10 Choosing Wisely® guidelines at their institutions. We conducted a content analysis to extract salient themes describing facilitators, challenges, and other feasibility considerations regarding implementing CPGs as CDS. RESULTS: We identified five themes: concern about data quality impacts implementation planning; the availability of data in a computable format is a primary factor for implementation feasibility; customized strategies are needed to mitigate uncertainty and ambiguity when translating CPGs to an electronic health record-based tool; misalignment of expected CDS with pre-existing clinical workflows impact implementation; and individual level factors of end-users must be considered when selecting and implementing CDS tools. DISCUSSION: The themes reveal several considerations for CPG as CDS implementations regarding data quality, knowledge representation, and sociotechnical issues. Guideline authors should be aware that using CDS to implement CPGs is becoming increasingly popular and should consider providing clear guidelines to aid implementation. The complex nature of CPG as CDS implementation necessitates a unified effort to overcome these challenges. CONCLUSION: Our analysis highlights the importance of cooperation and co-development of standards, strategies, and infrastructure to address the difficulties of implementing CPGs as CDS. The complex interactions between the concepts revealed in the interviews necessitates the need that such work should not be conducted in silos. We also implore that implementers disseminate their experiences.

Measuring implementation feasibility of clinical decision support alerts for clinical practice recommendations.

OBJECTIVE: The study sought to describe key features of clinical concepts and data required to implement clinical practice recommendations as clinical decision support (CDS) tools in electronic health record systems and to identify recommendation features that predict feasibility of implementation. MATERIALS AND METHODS: Using semistructured interviews, CDS implementers and clinician subject matter experts from 7 academic medical centers rated the feasibility of implementing 10 American College of Emergency Physicians Choosing Wisely Recommendations as electronic health record-embedded CDS and estimated the need for additional data collection. Ratings were combined with objective features of the guidelines to develop a predictive model for technical implementation feasibility. RESULTS: A linear mixed model showed that the need for new data collection was predictive of lower implementation feasibility. The number of clinical concepts in each recommendation, need for historical data, and ambiguity of clinical concepts were not predictive of implementation feasibility. CONCLUSIONS: The availability of data and need for additional data collection are essential to assess the feasibility of CDS implementation. Authors of practice recommendations and guidelines can enable organizations to more rapidly assess data availability and feasibility of implementation by including operational definitions for required data.

A case for using grid architecture for state public health informatics: the Utah perspective.

This paper presents the rationale for designing and implementing the next-generation of public health information systems using grid computing concepts and tools. Our attempt is to evaluate all grid types including data grids for sharing information and computational grids for accessing computational resources on demand. Public health is a broad domain that requires coordinated uses of disparate and heterogeneous information systems. System interoperability in public health is limited. The next-generation public health information systems must overcome barriers to integration and interoperability, leverage advances in information technology, address emerging requirements, and meet the needs of all stakeholders. Grid-based architecture provides one potential technical solution that deserves serious consideration. Within this context, we describe three discrete public health information system problems and the process by which the Utah Department of Health (UDOH) and the Department of Biomedical Informatics at the University of Utah in the United States has approached the exploration for eventual deployment of a Utah Public Health Informatics Grid. These three problems are: i) integration of internal and external data sources with analytic tools and computational resources; ii) provide external stakeholders with access to public health data and services; and, iii) access, integrate, and analyze internal data for the timely monitoring of population health status and health services. After one year of experience, we have successfully implemented federated queries across disparate administrative domains, and have identified challenges and potential solutions concerning the selection of candidate analytic grid services, data sharing concerns, security models, and strategies for reducing expertise required at a public health agency to implement a public health grid.

Should the pertussis case definition for public health reporting be refined?

BACKGROUND: The surveillance case definition for confirmed pertussis requires that an individual with a positive polymerase chain reaction (PCR) result for Bordetella pertussis have 2 weeks or more of cough and at least one of the following: paroxysmal coughing, inspiratory "whoop," or posttussive vomiting. OBJECTIVES: Determine (1) proportion of individuals with a positive PCR result who met additional criteria for surveillance confirmed pertussis, (2) whether the likelihood of PCR-positive individuals meeting additional elements of surveillance case definition varied by age or vaccination status, and (3) whether elements of the current case definition influence the likelihood of pertussis confirmation in PCR-positive individuals. METHODS: Pertussis PCR results were compared with case investigation data. RESULTS: Eighty-eight percent (165/188) of PCR-positive individuals met requirements for confirmed pertussis. Sixty-one percent (14/23) of PCR-positive individuals who had less than 2 weeks but more than 1 week of cough had at least one other reported sign or symptom. Fourteen (100%) reported paroxysmal coughing, 7 (50%) "whoop," and 7 (50%) posttussive vomiting. Infants who met case definition were more likely to have reported apnea than were older individuals (15/17 vs 45/86, OR = 6.8, 95% CI = 1.4-64.2). CONCLUSIONS: Decreasing cough duration from 2 weeks or more to more than 1 week would result in 95 percent of those with positive PCR results meeting confirmation criteria for pertussis. Apnea should be considered an additional sign for pertussis confirmation in infants.

Urgent care providers' knowledge and attitude about public health reporting and pertussis control measures: implications for informatics.

OBJECTIVES: We assessed urgent care providers' knowledge about public health reporting, guidelines, and actions for the prevention and control of pertussis; attitudes about public health reporting and population-based data; and perception of reporting practices in their clinic. METHODS: We identified the 106 providers (95% are physicians) employed in 28 urgent care clinics owned by Intermountain Healthcare located throughout Utah and Southern Idaho. We performed a descriptive, cross-sectional survey and assessed providers' knowledge, attitudes, beliefs, and behaviors associated with population-based data and public health mandates and recommendations. The online survey was completed between November 1, 2007, and February 29, 2008. RESULTS: Among 63 practicing urgent care providers (60% response rate), 19 percent knew that clinically diagnosed pertussis was reportable, and only half (52%) the providers correctly responded about current pertussis vaccination recommendations. Most (35%-78%) providers did not know the prevention and control measures performed by public health practitioners after reporting occurs, including contact tracing, testing, treatment, and prophylaxis. Half (48%) the providers did not know that health department personnel can prescribe antibiotics for contacts of a reported case, and only 22 percent knew that health department personnel may perform diagnostic testing on contacts. Attitudes about reporting are variable, and reporting responsibility is diffused. CONCLUSION: To improve our ability to meet public health goals, systems need to be designed that engage urgent care providers in the public health process, improve their knowledge and attitude about reporting, and facilitate the flow of information between urgent care and public health settings.

Computerized alerts improve outpatient laboratory monitoring of transplant patients.

Authors evaluated the impact of computerized alerts on the quality of outpatient laboratory monitoring for transplant patients. For 356 outpatient liver transplant patients managed at LDS Hospital, Salt Lake City, this observational study compared traditional laboratory result reporting, using faxes and printouts, to computerized alerts implemented in 2004. Study alerts within the electronic health record notified clinicians of new results and overdue new orders for creatinine tests and immunosuppression drug levels. After implementing alerts, completeness of reporting increased from 66 to >99 %, as did positive predictive value that a report included new information (from 46 to >99 %). Timeliness of reporting and clinicians' responses improved after implementing alerts (p <0.001): median times for clinicians to receive and complete actions decreased to 9 hours from 33 hours using the prior traditional reporting system. Computerized alerts led to more efficient, complete, and timely management of laboratory information.

External Validation of a Machine Learning Model to Predict 6-Month Mortality for Patients With Advanced Solid Tumors.

This prognostic study performed external validation of a machine learning model to predict 6-month mortality among patients with advanced solid tumors.

State-level adoption of national guidelines for norovirus outbreaks in health care settings.

BACKGROUND: Clinical decision support (CDS) systems can help investigators use best practices when responding to outbreaks, but variation in guidelines between jurisdictions can make such systems hard to develop and implement. This study aimed to identify (1) the extent to which state-level guidelines adhere to national recommendations for norovirus outbreak response in health care settings and (2) the impact of variation between states on outbreak outcomes. METHODS: State guidelines were obtained from Internet searches and direct contact with state public health officials in early 2016. Outcomes from norovirus outbreaks that occurred in 2015 were compared using data from the National Outbreak Reporting System. RESULTS: Guidelines were obtained from 41 of 45 (91%) state health departments that responded to queries or had guidelines available on their Web sites. Most state guidelines addressed each of the national recommendations, but specific guidance varied considerably. For example, among 36 states with guidance on numbers of stool specimens to collect, there were 21 different recommendations. Furthermore, having guidelines consistent with national recommendations was associated with fewer outbreaks reported and more outbreaks with confirmed etiology. CONCLUSIONS: This study identified substantial variation in state health care-associated norovirus outbreak response guidelines, which must be considered when developing related CDS systems. More research is needed to understand why this variation exists, how it impacts outbreak outcomes, and where improvements in evidence-based recommendations and communication of national guidance are needed.

System analysis and improvement in the process of transplant patient care.

Clinical information concerning transplant patients is voluminous and difficult to manage using paper records. A system analysis was performed to assess information system needs of the liver, kidney, and pancreas transplant program at LDS Hospital in Salt Lake City, Utah. After evaluating workflow, decision support needs, and requirements, we designed and implemented an extendable information system to support care following liver transplantation. We developed and implemented a standardized operative note, forms to enter external laboratory results and transplant-related information into the electronic health record, and computerized alerts to notify the transplant nurses when liver transplant patients had new, abnormal, or overdue laboratory results. The information system has improved the quality of clinical data available in the EHR, clinician satisfaction, and efficiency with management of laboratory results. The components developed for this project can be extended to meet other transplant program needs.

A case for manual entry of structured, coded laboratory data from multiple sources into an ambulatory electronic health record.

Laboratory results provide necessary information for the management of ambulatory patients. To realize the benefits of an electronic health record (EHR) and coded laboratory data (e.g., decision support and improved data access and display), results from laboratories that are external to the health care enterprise need to be integrated with internal results. We describe the development and clinical impact of integrating external results into the EHR at Intermountain Health Care (IHC). During 2004, over 14,000 external laboratory results for 128 liver transplant patients were added to the EHR. The results were used to generate computerized alerts that assisted clinicians with managing laboratory tests in the ambulatory setting. The external results were sent from 85 different facilities and can now be viewed in the EHR integrated with IHC results. We encountered regulatory, logistic, economic, and data quality issues that should be of interest to others developing similar applications.

Melinda - A custom search engine that provides access to locally-developed content using the HL7 Infobutton standard.

Healthcare organizations use care pathways to standardize care, but once developed, adoption rates often remain low. One challenge for usage concerns clinicians' difficulty in accessing guidance when it is most needed. Although the HL7 'Infobutton Standard' allows clinicians easier access to external references, access to locally-developed resources often requires clinicians to deviate from their normal electronic health record (EHR) workflow to use another application. To address this gap between internal and external resources, we reviewed the literature and existing practices at the University of Utah Health Care. We identify the requirements to meet the needs of a healthcare enterprise and clinicians, describe the design and development of a prototype to aggregate both internal and external resources from within or outside the EHR, and evaluated strengths and limitations of the prototype. The system is functional but not implemented in a live EHR environment. We suggest next steps and enhancements.

Development of an information model for storing organ donor data within an electronic medical record.

OBJECTIVE: To develop a model to store information in an electronic medical record (EMR) for the management of transplant patients. The model for storing donor information must be designed to allow clinicians to access donor information from the transplant recipient's record and to allow donor data to be stored without needlessly proliferating new Logical Observation Identifier Names and Codes (LOINC) codes for already-coded laboratory tests. DESIGN: Information required to manage transplant patients requires the use of a donor's medical information while caring for the transplant patient. Three strategies were considered: (1) link the transplant patient's EMR to the donor's EMR; (2) use pre-coordinated observation identifiers (i.e., LOINC codes with *(wedge)DONOR specified in the system axes) to identify donor data stored in the transplant patient's EMR; and (3) use an information model that allows donor information to be stored in the transplant patient's record by allowing the "source" of the data (donor) and the "name" of the result (e.g., blood type) to be post-coordinated in the transplant patient's EMR. RESULTS: We selected the third strategy and implemented a flexible post-coordinated information model. There was no need to create new LOINC codes for already-coded laboratory tests. The model required that the data structure in the EMR allow for the storage of the "subject" of the test. CONCLUSION: The selected strategy met our design requirements and provided an extendable information model to store donor data. This model can be used whenever it is necessary to refer to one patient's data from another patient's EMR.