The Morningside Initiative: Collaborative Development of a Knowledge Repository to Accelerate Adoption of Clinical Decision Support

The Morningside Initiative is a public-private activity that has evolved from an August, 2007, meeting at the Morningside Inn, in Frederick, MD, sponsored by the Telemedicine and Advanced Technology Research Center (TATRC) of the US Army Medical Research Materiel Command. Participants were subject matter experts in clinical decision support (CDS) and included representatives from the Department of Defense, Veterans Health Administration, Kaiser Permanente, Partners Healthcare System, Henry Ford Health System, Arizona State University, and the American Medical Informatics Association (AMIA). The Morningside Initiative was convened in response to the AMIA Roadmap for National Action on Clinical Decision Support and on the basis of other considerations and experiences of the participants. Its formation was the unanimous recommendation of participants at the 2007 meeting which called for creating a shared repository of executable knowledge for diverse health care organizations and practices, as well as health care system vendors. The rationale is based on the recognition that sharing of clinical knowledge needed for CDS across organizations is currently virtually non-existent, and that, given the considerable investment needed for creating, maintaining and updating authoritative knowledge, which only larger organizations have been able to undertake, this is an impediment to widespread adoption and use of CDS. The Morningside Initiative intends to develop and refine (1) an organizational framework, (2) a technical approach, and (3) CDS content acquisition and management processes for sharing CDS knowledge content, tools, and experience that will scale with growing numbers of participants and can be expanded in scope of content and capabilities. Intermountain Healthcare joined the initial set of participants shortly after its formation. The efforts of the Morningside Initiative are intended to serve as the basis for a series of next steps in a national agenda for CDS. It is based on the belief that sharing of knowledge can be highly effective as is the case in other competitive domains such as genomics. Participants in the Morningside Initiative believe that a coordinated effort between the private and public sectors is needed to accomplish this goal and that a small number of highly visible and respected health care organizations in the public and private sector can lead by example. Ultimately, a future collaborative knowledge sharing organization must have a sustainable long-term business model for financial support

Complex Care Management Program Overview

This report includes brief updates on various forms of complex care management including: Aetna - Medicare Advantage Embedded Case Management ProgramBrigham and Women's Hospital - Care Management ProgramIndependent Health - Care PartnersIntermountain Healthcare and Oregon Health and Science University - Care Management PlusJohns Hopkins University - Hospital at HomeMount Sinai Medical Center -- New York - Mount Sinai Visiting Doctors Program/ Chelsea-Village House Calls ProgramsPartners in Care Foundation - HomeMeds ProgramPrinceton HealthCare System - Partnerships for PIECEQuality Improvement for Complex Chronic Conditions - CarePartner ProgramSenior Services - Project Enhance/EnhanceWellnessSenior Whole Health - Complex Care Management ProgramSumma Health/Ohio Department of Aging - PASSPORT Medicaid Waiver ProgramSutter Health - Sutter Care Coordination ProgramUniversity of Washington School of Medicine - TEAMcar

Doctor of Philosophy

dissertationPublic health reporting is an important source of information for public health investigation and surveillance, which are necessary for the prevention and control of disease. There are two important problems with the current public health reporting process in the United States: (a) the reporting specifications are unstructured and are communicated with reporting facilities using nonstandard public health department Web sites and (b) most reporting facilities transmit reports to public health entities using manual and paper-based processes. Our research focuses on the development and evaluation of new strategies to improve the public health reporting process by addressing these problems. To improve the communication of public health reporting specifications by public health authorities, we: (a) examined the business process of a laboratory complying with the reporting requirements, (b) evaluated public health department Websites to understand the problems faced by reporting facilities while accessing the reporting specifications, (c) identified the content requirements of a knowledge management system for public health reporting specifications, (d) designed the representation of the public health reporting specifications, and (e) evaluated the content and design using a prototype web-based query system for public health reporting specifications. To improve the transmission of case reports from healthcare facilities to public health entities, we: (a) described public health workflow associated with the management of case reports, (b) identified the content of a case report to meet the needs of public health authorities, (c) modeled the case report using Health Level Seven (HL7) v2.5.1, and (d) evaluated the electronic case reports by comparing the timeliness, completeness of information content, and the completeness of the electronic reporting process with the paper-based reporting processes. We demonstrated a model for public health reporting specifications using a prototype web-based query system. The evaluation conducted with users from laboratories, healthcare facilities, and public health entities showed that the proposed model met most of the users' needs and requirements. We also identified variation in the reporting specifications, some of which could be standardized to improve reporting compliance. We implemented HL7 v2.5.1 case reports from Intermountain Healthcare hospitals to the Utah Department of Health. The electronic reports transmitted from the Intermountain hospitals were more timely (median delay: 2 days) than the paper reports sent from other clinical facilities (median delay: 3.5 days) but less timely than the paper reports from Intermountain laboratories (median: 1 day). However, the evaluation of the completeness of data elements needed for public health triage prior to investigation showed that electronic case reports from Intermountain hospitals included more complete information than paper reports from Intermountain laboratories. Even though the paper reports from Intermountain laboratories were more timely, the incomplete reports may delay investigation. There are informatics opportunities and public health needs to improve both electronic laboratory reporting and electronic case reporting

Doctor of Philosophy

dissertationDetailed clinical models (DCMs) are the basis for retaining computable meaning when data are exchanged between heterogeneous computer systems. DCMs are also the basis for shared computable meaning when clinical data are referenced in decision support logic, and they provide a basis for data consistency in a longitudinal electronic medical record. Intermountain Healthcare has a long history in the design and evolution of these models, beginning with PAL (PTXT Application Language) and then the Clinical Event Model, which was developed in partnership with 3M. After the partnership between Intermountain and 3M dissolved, Intermountain decided to design a next-generation architecture for DCMs. The aim of this research is to develop a detailed clinical model architecture that meets the needs of Intermountain Healthcare and other healthcare organizations. The approach was as follows: 1. An updated version of the Clinical Event Model was created using XML Schema as a formalism to describe models. 2. In response to problems with XML Schema, The Clinical Element Model was designed and created using Clinical Element Modeling Language as a formalism to describe models. 3. To verify that our model met the needs of Intermountain Healthcare and others, a desiderata for Detailed Clinical Models was developed. 4. The Clinical Element Model is then critiqued using the desiderata as a guide, and suggestions for further refinements to the Clinical Element Model are described

Doctor of Philosophy

dissertationTemporal reasoning denotes the modeling of causal relationships between different variables across different instances of time, and the prediction of future events or the explanation of past events. Temporal reasoning helps in modeling and understanding interactions between human pathophysiological processes, and in predicting future outcomes such as response to treatment or complications. Dynamic Bayesian Networks (DBN) support modeling changes in patients' condition over time due to both diseases and treatments, using probabilistic relationships between different clinical variables, both within and across different points in time. We describe temporal reasoning and representation in general and DBN in particular, with special attention to DBN parameter learning and inference. We also describe temporal data preparation (aggregation, consolidation, and abstraction) techniques that are applicable to medical data that were used in our research. We describe and evaluate various data discretization methods that are applicable to medical data. Projeny, an opensource probabilistic temporal reasoning toolkit developed as part of this research, is also described. We apply these methods, techniques, and algorithms to two disease processes modeled as Dynamic Bayesian Networks. The first test case is hyperglycemia due to severe illness in patients treated in the Intensive Care Unit (ICU). We model the patients' serum glucose and insulin drip rates using Dynamic Bayesian Networks, and recommend insulin drip rates to maintain the patients' serum glucose within a normal range. The model's safety and efficacy are proven by comparing it to the current gold standard. The second test case is the early prediction of sepsis in the emergency department. Sepsis is an acute life threatening condition that requires timely diagnosis and treatment. We present various DBN models and data preparation techniques that detect sepsis with very high accuracy within two hours after the patients' admission to the emergency department. We also discuss factors affecting the computational tractability of the models and appropriate optimization techniques. In this dissertation, we present a guide to temporal reasoning, evaluation of various data preparation, discretization, learning and inference methods, proofs using two test cases using real clinical data, an open-source toolkit, and recommend methods and techniques for temporal reasoning in medicine

A national clinical decision support infrastructure to enable the widespread and consistent practice of genomic and personalized medicine

<p>Abstract</p> <p>Background</p> <p>In recent years, the completion of the Human Genome Project and other rapid advances in genomics have led to increasing anticipation of an era of genomic and personalized medicine, in which an individual's health is optimized through the use of all available patient data, including data on the individual's genome and its downstream products. Genomic and personalized medicine could transform healthcare systems and catalyze significant reductions in morbidity, mortality, and overall healthcare costs.</p> <p>Discussion</p> <p>Critical to the achievement of more efficient and effective healthcare enabled by genomics is the establishment of a robust, nationwide clinical decision support infrastructure that assists clinicians in their use of genomic assays to guide disease prevention, diagnosis, and therapy. Requisite components of this infrastructure include the standardized representation of genomic and non-genomic patient data across health information systems; centrally managed repositories of computer-processable medical knowledge; and standardized approaches for applying these knowledge resources against patient data to generate and deliver patient-specific care recommendations. Here, we provide recommendations for establishing a national decision support infrastructure for genomic and personalized medicine that fulfills these needs, leverages existing resources, and is aligned with the <it>Roadmap for National Action on Clinical Decision Support </it>commissioned by the U.S. Office of the National Coordinator for Health Information Technology. Critical to the establishment of this infrastructure will be strong leadership and substantial funding from the federal government.</p> <p>Summary</p> <p>A national clinical decision support infrastructure will be required for reaping the full benefits of genomic and personalized medicine. Essential components of this infrastructure include standards for data representation; centrally managed knowledge repositories; and standardized approaches for leveraging these knowledge repositories to generate patient-specific care recommendations at the point of care.</p

OntoCR: A CEN/ISO-13606 clinical repository based on ontologies

Objective: To design a new semantically interoperable clinical repository, based on ontologies, conforming to CEN/ISO 13606 standard. Materials and Methods: The approach followed is to extend OntoCRF, a framework for the development of clinical repositories based on ontologies. The meta-model of OntoCRF has been extended by incorporating an OWL model integrating CEN/ISO 13606, ISO 21090 and SNOMED CT structure. Results: This approach has demonstrated a complete evaluation cycle involving the creation of the meta-model in OWL format, the creation of a simple test application, and the communication of standardized extracts to another organization. Discussion: Using a CEN/ISO 13606 based system, an indefinite number of archetypes can be merged (and reused) to build new applications. Our approach, based on the use of ontologies, maintains data storage independent of content specification. With this approach, relational technology can be used for storage, maintaining extensibility capabilities. Conclusions: The present work demonstrates that it is possible to build a native CEN/ISO 13606 repository for the storage of clinical data. We have demonstrated semantic interoperability of clinical information using CEN/ISO 13606 extracts

Clinical information modeling processes for semantic interoperability of electronic health records: systematic review and inductive analysis

This is a pre-copyedited, author-produced PDF of an article accepted for publication in Journal of the American Medical Informatics Association following peer review. The version of record is available online at: http://dx.doi.org/10.1093/jamia/ocv008[EN] [Objective] This systematic review aims to identify and compare the existing processes and methodologies that have been published in the literature for defining clinical information models (CIMs) that support the semantic interoperability of electronic health record (EHR) systems. [Material and Methods] Following the preferred reporting items for systematic reviews and meta-analyses systematic review methodology, the authors reviewed published papers between 2000 and 2013 that covered that semantic interoperability of EHRs, found by searching the PubMed, IEEE Xplore, and ScienceDirect databases. Additionally, after selection of a final group of articles, an inductive content analysis was done to summarize the steps and methodologies followed in order to build CIMs described in those articles. [Results] Three hundred and seventy-eight articles were screened and thirty six were selected for full review. The articles selected for full review were analyzed to extract relevant information for the analysis and characterized according to the steps the authors had followed for clinical information modeling. [Discussion] Most of the reviewed papers lack a detailed description of the modeling methodologies used to create CIMs. A representative example is the lack of description related to the definition of terminology bindings and the publication of the generated models. However, this systematic review confirms that most clinical information modeling activities follow very similar steps for the definition of CIMs. Having a robust and shared methodology could improve their correctness, reliability, and quality. [Conclusion] Independently of implementation technologies and standards, it is possible to find common patterns in methods for developing CIMs, suggesting the viability of defining a unified good practice methodology to be used by any clinical information modeler.This research has been partially funded by the Instituto de Salud Carlos III (Platform for Innovation in Medical Technologies and Health), grant PT13/0006/0036 and the Spanish Ministry of Economy and Competitiveness, grants TIN2010-21388-C02-01 and PTQ-12-05620.Moreno-Conde, A.; Moner Cano, D.; Da Cruz, WD.; Santos, MR.; Maldonado Segura, JA.; Robles Viejo, M.; Kalra, D. (2015). 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Doctor of Philosophy

dissertationFamily history has been called the "cornerstone of individualized disease prevention" but it is underutilized in clinical practice. In order to use it more effectively, its role in assessing risk for disease needs to be better quantified and understood. Family history has been identified as an important risk factor for colorectal cancer (CRC) and risk prediction in CRC is potentially worthwhile because of the possibility of preventing the disease through application of individualized screening programs tailored to risk. The overall project objective was to explore how family history can be better utilized to predict who will develop CRC. First, we used the Utah Population Database (UPDB) to define familial risk for CRC in more detail than has previously been reported. Second, we explored whether individuals at increased familial risk for CRC or at increased risk based on other risk factors such as a personal history of CRC or adenomatous polyps, are more compliant with screening and surveillance recommendations using colonoscopy than those who are at normal risk. Third, we measured how well family history can predict who will develop CRC over a period of 20 years, using family history by itself as a risk factor, and also in combination with the risk factor, age. We found that increased numbers of affected first-degree relatives influence risk much more than affected relatives from the second or third degrees. However, when combined with a positive firstdegree family history, a positive second- and third-degree family history can significantly increase risk. Next, we found that colonoscopy rates were higher in those with risk factors, according to risk-specific guidelines, but improvements in compliance are still warranted. Lastly, it was determined that family history by itself is not a strong predictor of exactly who will acquire colorectal cancer within 20 years. However, stratification of risk using absolute risk probabilities may be more helpful in focusing screening on individuals who are more likely to develop the disease. Future work includes using these findings as a basis for a cost/benefit analysis to determine optimal screening recommendations and building tools to better capture and utilize family history data in an electronic health record system