Medication concepts, records, and lists in electronic medical record systems

Thesis (S.M.)--Harvard-MIT Division of Health Sciences and Technology, 2006.Includes bibliographical references.A well-designed implementation of medication concepts, records, and lists in an electronic medical record (EMR) system allows it to successfully perform many functions vital for the provision of quality health care. A controlled medication terminology provides the foundation for decision support services, such as duplication checking, allergy checking, and drug-drug interaction alerts. Clever modeling of medication records makes it easy to provide a history of any medication the patient is on and to generate the patient's medication list for any arbitrary point in time. Medication lists that distinguish between description and prescription and that are exportable in a standard format can play an essential role in medication reconciliation and contribute to the reduction of medication errors. At present, there is no general agreement on how to best implement medication concepts, records, and lists. The underlying implementation in an EMR often reflects the needs, culture, and history of both the developers and the local users. survey of a sample of medication terminologies (COSTAR Directory, the MDD, NDDF Plus, and RxNorm) and EMR implementations of medication records (OnCall, LMR, and the Benedum EMR) reveals the advantages and disadvantages of each. There is no medication system that would fit perfectly in every single context, but some features should strongly be considered in the development of any new system.(cont.) A survey of a sample of medication terminologies (COSTAR Directory, the MDD, NDDF Plus, and RxNorm) and EMR implementations of medication records (OnCall, LMR, and the Benedum EMR) reveals the advantages and disadvantages of each. There is no medication system that would fit perfectly in every single context, but some features should strongly be considered in the development of any new system.by Jaime Chang.S.M

A Core Reference Hierarchical Primitive Ontology for Electronic Medical Records Semantics Interoperability

Currently, electronic medical records (EMR) cannot be exchanged among hospitals, clinics, laboratories, pharmacies, and insurance providers or made available to patients outside of local networks. Hospital, laboratory, pharmacy, and insurance provider legacy databases can share medical data within a respective network and limited data with patients. The lack of interoperability has its roots in the historical development of electronic medical records. Two issues contribute to interoperability failure. The first is that legacy medical record databases and expert systems were designed with semantics that support only internal information exchange. The second is ontological commitment to the semantics of a particular knowledge representation language formalism. This research seeks to address these interoperability failures through demonstration of the capability of a core reference, hierarchical primitive ontological architecture with concept primitive attributes definitions to integrate and resolve non-interoperable semantics among and extend coverage across existing clinical, drug, and hospital ontologies and terminologies

Multinational patterns of second line antihyperglycaemic drug initiation across cardiovascular risk groups:federated pharmacoepidemiological evaluation in LEGEND-T2DM

Objective: To assess the uptake of second line antihyperglycaemic drugs among patients with type 2 diabetes mellitus who are receiving metformin.Design: Federated pharmacoepidemiological evaluation in LEGEND-T2DM.Setting: 10 US and seven non-US electronic health record and administrative claims databases in the Observational Health Data Sciences and Informatics network in eight countries from 2011 to the end of 2021.Participants: 4.8 million patients (≥18 years) across US and non-US based databases with type 2 diabetes mellitus who had received metformin monotherapy and had initiated second line treatments.Exposure: The exposure used to evaluate each database was calendar year trends, with the years in the study that were specific to each cohort.Main outcomes measures: The outcome was the incidence of second line antihyperglycaemic drug use (ie, glucagon-like peptide-1 receptor agonists, sodium-glucose cotransporter-2 inhibitors, dipeptidyl peptidase-4 inhibitors, and sulfonylureas) among individuals who were already receiving treatment with metformin. The relative drug class level uptake across cardiovascular risk groups was also evaluated.Results: 4.6 million patients were identified in US databases, 61 382 from Spain, 32 442 from Germany, 25 173 from the UK, 13 270 from France, 5580 from Scotland, 4614 from Hong Kong, and 2322 from Australia. During 2011-21, the combined proportional initiation of the cardioprotective antihyperglycaemic drugs (glucagon-like peptide-1 receptor agonists and sodium-glucose cotransporter-2 inhibitors) increased across all data sources, with the combined initiation of these drugs as second line drugs in 2021 ranging from 35.2% to 68.2% in the US databases, 15.4% in France, 34.7% in Spain, 50.1% in Germany, and 54.8% in Scotland. From 2016 to 2021, in some US and non-US databases, uptake of glucagon-like peptide-1 receptor agonists and sodium-glucose cotransporter-2 inhibitors increased more significantly among populations with no cardiovascular disease compared with patients with established cardiovascular disease. No data source provided evidence of a greater increase in the uptake of these two drug classes in populations with cardiovascular disease compared with no cardiovascular disease.Conclusions: Despite the increase in overall uptake of cardioprotective antihyperglycaemic drugs as second line treatments for type 2 diabetes mellitus, their uptake was lower in patients with cardiovascular disease than in people with no cardiovascular disease over the past decade. A strategy is needed to ensure that medication use is concordant with guideline recommendations to improve outcomes of patients with type 2 diabetes mellitus.</p

SHRINE: Enabling Nationally Scalable Multi-Site Disease Studies

Results of medical research studies are often contradictory or cannot be reproduced. One reason is that there may not be enough patient subjects available for observation for a long enough time period. Another reason is that patient populations may vary considerably with respect to geographic and demographic boundaries thus limiting how broadly the results apply. Even when similar patient populations are pooled together from multiple locations, differences in medical treatment and record systems can limit which outcome measures can be commonly analyzed. In total, these differences in medical research settings can lead to differing conclusions or can even prevent some studies from starting. We thus sought to create a patient research system that could aggregate as many patient observations as possible from a large number of hospitals in a uniform way. We call this system the ‘Shared Health Research Information Network’, with the following properties: (1) reuse electronic health data from everyday clinical care for research purposes, (2) respect patient privacy and hospital autonomy, (3) aggregate patient populations across many hospitals to achieve statistically significant sample sizes that can be validated independently of a single research setting, (4) harmonize the observation facts recorded at each institution such that queries can be made across many hospitals in parallel, (5) scale to regional and national collaborations. The purpose of this report is to provide open source software for multi-site clinical studies and to report on early uses of this application. At this time SHRINE implementations have been used for multi-site studies of autism co-morbidity, juvenile idiopathic arthritis, peripartum cardiomyopathy, colorectal cancer, diabetes, and others. The wide range of study objectives and growing adoption suggest that SHRINE may be applicable beyond the research uses and participating hospitals named in this report

Master of Science

thesisLack of information is a serious concern for clinicians. Information resources can address this problem, leading to improvements in decision making and patient outcomes. Genomics is an information-rich domain where searching for information can be complex. For example, most physicians agree that pharmacogenomics can be used to improve the quality of care, and there is evidence that many patients harbor actionable pharmacogenomic variation. However, surveys have shown that physicians feel their knowledge of pharmacogenomics to be inadequate. This represents an information need. A natural approach to meet this need is to provide context-aware access to the precise information needed. The Health Level 7 Context-Aware Knowledge Retrieval Standard, a.k.a the Infobutton, offers a modality to deliver context-aware knowledge into electronic health record (EHR) systems. OpenInfobutton is a reference implementation of this standard that offers an open-source instantiation. In this thesis, we aimed to provide insight into pharmacogenomics information needs and an automated mechanism for addressing these needs. Such work can aid the design of tools that support clinical decisions in genomics

The Knowledge Grid: A Platform to Increase the Interoperability of Computable Knowledge and Produce Advice for Health

Here we demonstrate how more highly interoperable computable knowledge enables systems to generate large quantities of evidence-based advice for health. We first provide a thorough analysis of advice. Then, because advice derives from knowledge, we turn our focus to computable, i.e., machine-interpretable, forms for knowledge. We consider how computable knowledge plays dual roles as a resource conveying content and as an advice enabler. In this latter role, computable knowledge is combined with data about a decision situation to generate advice targeted at the pending decision. We distinguish between two types of automated services. When a computer system provides computable knowledge, we say that it provides a knowledge service. When computer system combines computable knowledge with instance data to provide advice that is specific to an unmade decision we say that it provides an advice-giving service. The work here aims to increase the interoperability of computable knowledge to bring about better knowledge services and advice-giving services for health. The primary motivation for this research is the problem of missing or inadequate advice about health topics. The global demand for well-informed health advice far exceeds the global supply. In part to overcome this scarcity, the design and development of Learning Health Systems is being pursued at various levels of scale: local, regional, state, national, and international. Learning Health Systems fuse capabilities to generate new computable biomedical knowledge with other capabilities to rapidly and widely use computable biomedical knowledge to inform health practices and behaviors with advice. To support Learning Health Systems, we believe that knowledge services and advice-giving services have to be more highly interoperable. I use examples of knowledge services and advice-giving services which exclusively support medication use. This is because I am a pharmacist and pharmacy is the biomedical domain that I know. The examples here address the serious problems of medication adherence and prescribing safety. Two empirical studies are shared that demonstrate the potential to address these problems and make improvements by using advice. But primarily we use these examples to demonstrate general and critical differences between stand-alone, unique approaches to handling computable biomedical knowledge, which make it useful for one system, and common, more highly interoperable approaches, which can make it useful for many heterogeneous systems. Three aspects of computable knowledge interoperability are addressed: modularity, identity, and updateability. We demonstrate that instances of computable knowledge, and related instances of knowledge services and advice-giving services, can be modularized. We also demonstrate the utility of uniquely identifying modular instances of computable knowledge. Finally, we build on the computing concept of pipelining to demonstrate how computable knowledge modules can automatically be updated and rapidly deployed. Our work is supported by a fledgling technical knowledge infrastructure platform called the Knowledge Grid. It includes formally specified compound digital objects called Knowledge Objects, a conventional digital Library that serves as a Knowledge Object repository, and an Activator that provides an application programming interface (API) for computable knowledge. The Library component provides knowledge services. The Activator component provides both knowledge services and advice-giving services. In conclusion, by increasing the interoperability of computable biomedical knowledge using the Knowledge Grid, we demonstrate new capabilities to generate well-informed health advice at a scale. These new capabilities may ultimately support Learning Health Systems and boost health for large populations of people who would otherwise not receive well-informed health advice.PHDInformationUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttps://deepblue.lib.umich.edu/bitstream/2027.42/146073/1/ajflynn_1.pd