Electronic Health Records: An International Perspective on "Meaningful Use"

Examines the extent of meaningful use of electronic health records in Denmark, New Zealand, and Sweden, including sharing information with organizations, health authorities, and patients. Outlines challenges of and insights into encouraging U.S. adoption

J Biomed Inform

We followed a systematic approach based on the Preferred Reporting Items for Systematic Reviews and Meta-Analyses to identify existing clinical natural language processing (NLP) systems that generate structured information from unstructured free text. Seven literature databases were searched with a query combining the concepts of natural language processing and structured data capture. Two reviewers screened all records for relevance during two screening phases, and information about clinical NLP systems was collected from the final set of papers. A total of 7149 records (after removing duplicates) were retrieved and screened, and 86 were determined to fit the review criteria. These papers contained information about 71 different clinical NLP systems, which were then analyzed. The NLP systems address a wide variety of important clinical and research tasks. Certain tasks are well addressed by the existing systems, while others remain as open challenges that only a small number of systems attempt, such as extraction of temporal information or normalization of concepts to standard terminologies. This review has identified many NLP systems capable of processing clinical free text and generating structured output, and the information collected and evaluated here will be important for prioritizing development of new approaches for clinical NLP.CC999999/ImCDC/Intramural CDC HHS/United States2019-11-20T00:00:00Z28729030PMC6864736694

Automatic Detection of Adverse Drug Events in Geriatric Care: Study Proposal

BACKGROUND One-third of older inpatients experience adverse drug events (ADEs), which increase their mortality, morbidity, and health care use and costs. In particular, antithrombotic drugs are among the most at-risk medications for this population. Reporting systems have been implemented at the national, regional, and provider levels to monitor ADEs and design prevention strategies. Owing to their well-known limitations, automated detection technologies based on electronic medical records (EMRs) are being developed to routinely detect or predict ADEs. OBJECTIVE This study aims to develop and validate an automated detection tool for monitoring antithrombotic-related ADEs using EMRs from 4 large Swiss hospitals. We aim to assess cumulative incidences of hemorrhages and thromboses in older inpatients associated with the prescription of antithrombotic drugs, identify triggering factors, and propose improvements for clinical practice. METHODS This project is a multicenter, cross-sectional study based on 2015 to 2016 EMR data from 4 large hospitals in Switzerland: Lausanne, Geneva, and Zürich university hospitals, and Baden Cantonal Hospital. We have included inpatients aged ≥65 years who stayed at 1 of the 4 hospitals during 2015 or 2016, received at least one antithrombotic drug during their stay, and signed or were not opposed to a general consent for participation in research. First, clinical experts selected a list of relevant antithrombotic drugs along with their side effects, risks, and confounding factors. Second, administrative, clinical, prescription, and laboratory data available in the form of free text and structured data were extracted from study participants' EMRs. Third, several automated rule-based and machine learning-based algorithms are being developed, allowing for the identification of hemorrhage and thromboembolic events and their triggering factors from the extracted information. Finally, we plan to validate the developed detection tools (one per ADE type) through manual medical record review. Performance metrics for assessing internal validity will comprise the area under the receiver operating characteristic curve, F$_{1}$-score, sensitivity, specificity, and positive and negative predictive values. RESULTS After accounting for the inclusion and exclusion criteria, we will include 34,522 residents aged ≥65 years. The data will be analyzed in 2022, and the research project will run until the end of 2022 to mid-2023. CONCLUSIONS This project will allow for the introduction of measures to improve safety in prescribing antithrombotic drugs, which today remain among the drugs most involved in ADEs. The findings will be implemented in clinical practice using indicators of adverse events for risk management and training for health care professionals; the tools and methodologies developed will be disseminated for new research in this field. The increased performance of natural language processing as an important complement to structured data will bring existing tools to another level of efficiency in the detection of ADEs. Currently, such systems are unavailable in Switzerland. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID) DERR1-10.2196/40456

Clinical Document Classification Using Labeled and Unlabeled Data Across Hospitals

Reviewing radiology reports in emergency departments is an essential but laborious task. Timely follow-up of patients with abnormal cases in their radiology reports may dramatically affect the patient's outcome, especially if they have been discharged with a different initial diagnosis. Machine learning approaches have been devised to expedite the process and detect the cases that demand instant follow up. However, these approaches require a large amount of labeled data to train reliable predictive models. Preparing such a large dataset, which needs to be manually annotated by health professionals, is costly and time-consuming. This paper investigates a semi-supervised learning framework for radiology report classification across three hospitals. The main goal is to leverage clinical unlabeled data in order to augment the learning process where limited labeled data is available. To further improve the classification performance, we also integrate a transfer learning technique into the semi-supervised learning pipeline . Our experimental findings show that (1) convolutional neural networks (CNNs), while being independent of any problem-specific feature engineering, achieve significantly higher effectiveness compared to conventional supervised learning approaches, (2) leveraging unlabeled data in training a CNN-based classifier reduces the dependency on labeled data by more than 50% to reach the same performance of a fully supervised CNN, and (3) transferring the knowledge gained from available labeled data in an external source hospital significantly improves the performance of a semi-supervised CNN model over their fully supervised counterparts in a target hospital

Geisinger Health System: Achieving the Potential of System Integration Through Innovation, Leadership, Measurement, and Incentives

Presents a case study of a physician-led nonprofit healthcare group exhibiting the attributes of an ideal healthcare delivery system as defined by the Fund. Describes how its ProvenCare model improved clinical outcomes with reduced resource utilization

DEEPEN: A negation detection system for clinical text incorporating dependency relation into NegEx

In Electronic Health Records (EHRs), much of valuable information regarding patients’ conditions is embedded in free text format. Natural language processing (NLP) techniques have been developed to extract clinical information from free text. One challenge faced in clinical NLP is that the meaning of clinical entities is heavily affected by modifiers such as negation. A negation detection algorithm, NegEx, applies a simplistic approach that has been shown to be powerful in clinical NLP. However, due to the failure to consider the contextual relationship between words within a sentence, NegEx fails to correctly capture the negation status of concepts in complex sentences. Incorrect negation assignment could cause inaccurate diagnosis of patients’ condition or contaminated study cohorts. We developed a negation algorithm called DEEPEN to decrease NegEx’s false positives by taking into account the dependency relationship between negation words and concepts within a sentence using Stanford dependency parser. The system was developed and tested using EHR data from Indiana University (IU) and it was further evaluated on Mayo Clinic dataset to assess its generalizability. The evaluation results demonstrate DEEPEN, which incorporates dependency parsing into NegEx, can reduce the number of incorrect negation assignment for patients with positive findings, and therefore improve the identification of patients with the target clinical findings in EHRs

Doctor of Philosophy

DissertationHealth information technology (HIT) in conjunction with quality improvement (QI) methodologies can promote higher quality care at lower costs. Unfortunately, most inpatient hospital settings have been slow to adopt HIT and QI methodologies. Successful adoption requires close attention to workflow. Workflow is the sequence of tasks, processes, and the set of people or resources needed for those tasks that are necessary to accomplish a given goal. Assessing the impact on workflow is an important component of determining whether a HIT implementation will be successful, but little research has been conducted on the impact of eMeasure (electronic performance measure) implementation on workflow. One solution to addressing implementation challenges such as the lack of attention to workflow is an implementation toolkit. An implementation toolkit is an assembly of instruments such as checklists, forms, and planning documents. We developed an initial eMeasure Implementation Toolkit for the heart failure (HF) eMeasure to allow QI and information technology (IT) professionals and their team to assess the impact of implementation on workflow. During the development phase of the toolkit, we undertook a literature review to determine the components of the toolkit. We conducted stakeholder interviews with HIT and QI key informants and subject matter experts (SMEs) at the US Department of Veteran Affairs (VA). Key informants provided a broad understanding about the context of workflow during eMeasure implementation. Based on snowball sampling, we also interviewed other SMEs based on the recommendations of the key informants who suggested tools and provided information essential to the toolkit development. The second phase involved evaluation of the toolkit for relevance and clarity, by experts in non-VA settings. The experts evaluated the sections of the toolkit that contained the tools, via a survey. The final toolkit provides a distinct set of resources and tools, which were iteratively developed during the research and available to users in a single source document. The research methodology provided a strong unified overarching implementation framework in the form of the Promoting Action on Research Implementation in Health Services (PARIHS) model in combination with a sociotechnical model of HIT that strengthened the overall design of the study

Doctor of Philosophy

dissertationThe problem of information transfer between healthcare sectors and across the continuum of care was examined using a mixed methods approach. These methods include qualitative interviews, retrospective case reviews and an informatic gap analysis. Findings and conclusions are reported for each study. Qualitative interviews were conducted with 16 healthcare representatives from 4 disciplines (medicine, pharmacy, nursing, and social work) and 3 healthcare sectors (hospital, skilled nursing care and community care). Three key themes from a Joint Cognitive Systems theoretical model were used to examine qualitative findings. Agreement on cross-sector care goals is neither defined nor made explicit and in some instances working at cross purposes. Care goals and information paradigms change as patients move from hospitalbased crisis stabilization, diagnosis and treatment to a postdischarge care to home or skilled nursing recovery, function restoration, or end of life support. Control of the transfer process is variable across institutions with little feedback and feed-forward. Lack of knowledge, competency and information tracking threatens sector interdependencies with suspicion and distrust. Sixty-three patients discharged between 2006 and 2008 from hospitals to skilled nursing facilities were randomly selected and reviewed. Most notably missing are discharge summaries (30%), nursing assessments or notes (17%), and social work documents (25%). Advanced directives or living wills necessary for end of life support were present in only 6% of the cases. The presence of information on activities of daily living (ADLs), other disabling conditions, and nutrition was associated with positive outcomes at the 0.001, 0.04 and 0.08levels. Consistent geriatric information transfer across the continuum is needed for relevant care management. An interoperability gap analysis conducted on the LINC (Linking Information Necessary for Care) transfer form determined its interoperability to be the semantic level 0. Detailed Clinical Models representing care management processes are challenged by the lack of consensus in terminology standards across sectors. Construction of information transfer solutions compliant with the Centers of Medicare and Medicaid Services (CMS) Stage 2 meaningful use criteria must address syntactic and semantic standards, map sector terminologies within care management processes, and account for the lack of standard terminologies in allied health domains