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Journal ArticleDOI

Use of a data warehouse at an academic medical center for clinical pathology quality improvement, education, and research

01 Jan 2015-Journal of Pathology Informatics (Medknow Publications)-Vol. 6, Iss: 1, pp 45-45
TL;DR: A data warehouse has significant potential for improving utilization of clinical pathology testing and software that can access data warehouse using a straightforward visual interface can be incorporated into pathology training programs.
About: This article is published in Journal of Pathology Informatics.The article was published on 2015-01-01 and is currently open access. It has received 21 citations till now. The article focuses on the topics: Data warehouse & Data access.
Citations
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Journal ArticleDOI
TL;DR: Dr. Warehouse is dedicated to translational research with cohort recruitment capabilities, high throughput phenotyping and patient centric views (including similarity metrics among patients), and features leverage Natural Language Processing based on the extraction of UMLS® concepts, as well as negation and family history detection.

76 citations

Journal ArticleDOI
TL;DR: The use of HEGP CDWs is a key facilitator for clinical research studies, however important methodological and organizational support efforts from a biomedical informatics department are required.

57 citations

Journal ArticleDOI
TL;DR: A list of sixteen recommendations regarding the usage of NLP systems and algorithms, usage of data, evaluation and validation, presentation of results, and generalizability of results was developed and believe will increase the reproducibility and reusability of future studies and NLP algorithms in medicine.
Abstract: Free-text descriptions in electronic health records (EHRs) can be of interest for clinical research and care optimization. However, free text cannot be readily interpreted by a computer and, therefore, has limited value. Natural Language Processing (NLP) algorithms can make free text machine-interpretable by attaching ontology concepts to it. However, implementations of NLP algorithms are not evaluated consistently. Therefore, the objective of this study was to review the current methods used for developing and evaluating NLP algorithms that map clinical text fragments onto ontology concepts. To standardize the evaluation of algorithms and reduce heterogeneity between studies, we propose a list of recommendations. Two reviewers examined publications indexed by Scopus, IEEE, MEDLINE, EMBASE, the ACM Digital Library, and the ACL Anthology. Publications reporting on NLP for mapping clinical text from EHRs to ontology concepts were included. Year, country, setting, objective, evaluation and validation methods, NLP algorithms, terminology systems, dataset size and language, performance measures, reference standard, generalizability, operational use, and source code availability were extracted. The studies’ objectives were categorized by way of induction. These results were used to define recommendations. Two thousand three hundred fifty five unique studies were identified. Two hundred fifty six studies reported on the development of NLP algorithms for mapping free text to ontology concepts. Seventy-seven described development and evaluation. Twenty-two studies did not perform a validation on unseen data and 68 studies did not perform external validation. Of 23 studies that claimed that their algorithm was generalizable, 5 tested this by external validation. A list of sixteen recommendations regarding the usage of NLP systems and algorithms, usage of data, evaluation and validation, presentation of results, and generalizability of results was developed. We found many heterogeneous approaches to the reporting on the development and evaluation of NLP algorithms that map clinical text to ontology concepts. Over one-fourth of the identified publications did not perform an evaluation. In addition, over one-fourth of the included studies did not perform a validation, and 88% did not perform external validation. We believe that our recommendations, alongside an existing reporting standard, will increase the reproducibility and reusability of future studies and NLP algorithms in medicine.

42 citations

Journal ArticleDOI
05 Feb 2018
TL;DR: Two different predictive models for frailty are proposed by exploiting 12 socioclinical databases based on the whole elderly population of the Municipality of Bologna, Italy, demonstrating a good predictive ability of the models.
Abstract: Smart cities face the challenge of combining sustainable national welfare with high living standards In the last decades, life expectancy increased globally, leading to various age-related issues in almost all developed countries Frailty affects elderly who are experiencing daily life limitations due to cognitive and functional impairments and represents a remarkable burden for national health systems In this paper, we proposed two different predictive models for frailty by exploiting 12 socioclinical databases Emergency hospitalization or all-cause mortality within a year were used as surrogates of frailty The first model was able to assign a frailty risk score to each subject older than 65 years old, identifying five different classes for tailor made interventions The second prediction model assigned a worsening risk score to each subject in the first nonfrail class, namely the probability to move in a higher frailty class within the year We conducted a retrospective cohort study based on the whole elderly population of the Municipality of Bologna, Italy We created a baseline cohort of 95 368 subjects for the frailty risk model and a baseline cohort of 58 789 subjects for the worsening risk model, respectively To evaluate the predictive ability of our models through calibration and discrimination estimates, we used, respectively, a six-year and a four-year observation period Good discriminatory power and calibration were obtained, demonstrating a good predictive ability of the models

33 citations


Cites background from "Use of a data warehouse at an acade..."

  • ...In conclusion, data warehousing techniques have proven to be promising for healthcare information systems in several clinical fields, like intensive care [38] and clinical pathology [39]....

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Journal ArticleDOI
TL;DR: Research is examined that provides solutions to unlock barriers and accelerate translational research: structured electronic health records and free-text search engines to find patients, data warehouses and natural language processing to extract phenotypes, machine learning algorithms to classify patients, and similarity metrics to diagnose patients.

27 citations

References
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Journal ArticleDOI
TL;DR: The potential for furthering medical research and clinical care using EHR data and the challenges that must be overcome before this is a reality are considered.
Abstract: The adoption of electronic health records will provide a rich resource for biomedical researchers. This Review discusses the potential for their use in informed decision making in the clinic, for a finer understanding of genotype–phenotype relationships and for selection of research cohorts, along with the current challenges for their mining and use. Clinical data describing the phenotypes and treatment of patients represents an underused data source that has much greater research potential than is currently realized. Mining of electronic health records (EHRs) has the potential for establishing new patient-stratification principles and for revealing unknown disease correlations. Integrating EHR data with genetic data will also give a finer understanding of genotype–phenotype relationships. However, a broad range of ethical, legal and technical reasons currently hinder the systematic deposition of these data in EHRs and their mining. Here, we consider the potential for furthering medical research and clinical care using EHR data and the challenges that must be overcome before this is a reality.

1,376 citations

Journal ArticleDOI
TL;DR: The very low levels of adoption of electronic health records in U.S. hospitals suggest that policymakers face substantial obstacles to the achievement of health care performance goals that depend on health information technology.
Abstract: We surveyed all acute care hospitals that are members of the American Hospital Association for the presence of specific electronic-record functionalities. Using a definition of electronic health records based on expert consensus, we determined the proportion of hospitals that had such systems in their clinical areas. We also examined the relationship of adoption of electronic health records to specific hospital characteristics and factors that were reported to be barriers to or facilitators of adoption. Results On the basis of responses from 63.1% of hospitals surveyed, only 1.5% of U.S. hospitals have a comprehensive electronic-records system (i.e., present in all clinical units), and an additional 7.6% have a basic system (i.e., present in at least one clinical unit). Computerized provider-order entry for medications has been implemented in only 17% of hospitals. Larger hospitals, those located in urban areas, and teaching hospitals were more likely to have electronic-records systems. Respondents cited capital requirements and high maintenance costs as the primary barriers to implementation, although hospitals with electronic-records systems were less likely to cite these barriers than hospitals without such systems. Conclusions The very low levels of adoption of electronic health records in U.S. hospitals suggest that policymakers face substantial obstacles to the achievement of health care performance goals that depend on health information technology. A policy strategy focused on financial support, interoperability, and training of technical support staff may be necessary to spur adoption of electronic-records systems in U.S. hospitals.

1,326 citations

Journal ArticleDOI
TL;DR: The level of serum angiotensin-converting enzyme (ACE) was elevated in 15 of 17 patients with active sarcoidosis as mentioned in this paper, whereas levels in patients with sarcolidosis not receiving steroids were greater than 2 standard deviations above the mean for the adult control subjects.

842 citations

Journal ArticleDOI
01 Jan 2008
TL;DR: Pagmatic concepts summarized in this article should minimize the potential risks of misinterpreting urine drug screens and the potential pitfalls, such as adulteration, substitution, and dilution of urine samples, which can lead to serious medical or social consequences.
Abstract: Drug testing, commonly used in health care, workplace, and criminal settings, has become widespread during the past decade. Urine drug screens have been the most common method for analysis because of ease of sampling. The simplicity of use and access to rapid results have increased demand for and use of immunoassays; however, these assays are not perfect. Falsepositive results of immunoassays can lead to serious medical or social consequences if results are not confirmed by secondary analysis, such as gas chromatography‐mass spectrometry. The Department of Health and Human Services’ guidelines for the workplace require testing for the following 5 substances: amphetamines, cannabinoids, cocaine, opiates, and phencyclidine. This article discusses potential false-positive results and false-negative results that occur with immunoassays of these substances and with alcohol, benzodiazepines, and tricyclic antidepressants. Other pitfalls, such as adulteration, substitution, and dilution of urine samples, are discussed. Pragmatic concepts summarized in this article should minimize the potential risks of misinterpreting urine drug screens.

452 citations

Journal ArticleDOI
TL;DR: There are a variety of approaches for classifying patients into a particular phenotype, and good performance is reported on datasets at respective institutions, however, no system makes comprehensive use of electronic medical records addressing all of their known weaknesses.

430 citations

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Can a software developer become data analyst?

Software that can access data warehouse using a straightforward visual interface can be incorporated into pathology training programs.