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Healthcare information technology and economics

JOURNAL OF THE AMERICAN MEDICAL INFORMATICS ASSOCIATION 2013年第2期20卷 212-217页

作者： Payne, Thomas H. Bates, David W. Berner, Eta S. Bernstam, Elmer V. Covvey, H. Dominic Frisse, Mark E. Graf, Thomas Greenes, Robert A. Hoffer, Edward P. Kuperman, Gil Lehmann, Harold P. Liang, Louise Middleton, Blackford Omenn, Gilbert S. Ozbolt, Judy Univ Washington Dept Med Seattle WA USA Univ Washington Dept Hlth Serv Seattle WA 98195 USA Univ Washington Dept Biomed & Hlth Informat Seattle WA 98195 USA Brigham & Womens Hosp Div Gen Med & Primary Care Boston MA 02115 USA Univ Alabama Birmingham Dept Hlth Serv Adm Birmingham AL USA Univ Texas Houston Dept Biomed Informat Houston TX USA Univ Texas Houston Dept Internal Med Houston TX USA Natl Inst Hlth Waterloo ON Canada Vanderbilt Univ Sch Med Dept Biomed Informat Nashville TN 37212 USA Geisinger Hlth Syst Populat Hlth Danville PA USA Arizona State Univ Dept Biomed Informat Scottsdale AZ USA Mayo Clin Scottsdale AZ USA Harvard Univ Dept Med Boston MA 02115 USA New York Presbyterian Hosp New York NY USA Johns Hopkins Sch Med Dept Hlth Sci Informat Baltimore MD USA Johns Hopkins Sch Med Dept Pediat Baltimore MD USA Kaiser Permanente Qual & Clin Syst Support Issaquah WA USA Partners HealthCare Syst Clin Informat R&D Boston MA USA Univ Michigan Dept Internal Med Ann Arbor MI 48109 USA Univ Michigan Dept Human Genet Ann Arbor MI 48109 USA Univ Michigan Dept Publ Hlth Ann Arbor MI 48109 USA Univ Michigan Dept Computat Med & Bioinformat Ann Arbor MI 48109 USA Hlth & Biomed Informat Asheville NC USA

At the 2011 American College of Medical Informatics (ACMI) Winter Symposium we studied the overlap between health IT and economics and what leading healthcare delivery organizations are achieving today using IT that might offer paths for the nation to follow for using health IT in healthcare reform. We recognized that health IT by itself can improve health value, but its main contribution to health value may be that it can make possible new care delivery models to achieve much larger value. Health IT is a critically important enabler to fundamental healthcare system changes that may be a way out of our current, severe problem of rising costs and national deficit. We review the current state of healthcare costs, federal health IT stimulus programs, and experiences of several leading organizations, and offer a model for how health IT fits into our health economic future.

关键词： Electronic Health Records/economics Health care Reform/trends cost-benefit analysis information Systems/economics medical records systems Computerized/economics Developing/using computerized provider order entry Natural-language processing systems to support and improve diagnostic accuracy other specific EHR applications (results review) patient safety decision support data exchange system implementation and management issues improving the education and skills training of health professionals Developing/using clinical decision support (other than diagnostic) and guideline systems Measuring/improving patient safety and reducing medical errors clinical research informatics information Retrieval Collaborative technologies methods for integration of information from disparate sources Demonstrating return on IT investment distributed systems distributed systems

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methods and dimensions of electronic health record data quality assessment: enabling reuse for clinical research

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JOURNAL OF THE AMERICAN MEDICAL INFORMATICS ASSOCIATION 2013年第1期20卷 144-151页

作者： Weiskopf, Nicole Gray Weng, Chunhua Columbia Univ Dept Biomed Informat New York NY 10032 USA

Objective To review the methods and dimensions of data quality assessment in the context of electronic health record (EHR) data reuse for research. Materials and methods A review of the clinical research literature discussing data quality assessment methodology for EHR data was performed. Using an iterative process, the aspects of data quality being measured were abstracted and categorized, as well as the methods of assessment used. Results Five dimensions of data quality were identified, which are completeness, correctness, concordance, plausibility, and currency, and seven broad categories of data quality assessment methods: comparison with gold standards, data element agreement, data source agreement, distribution comparison, validity checks, log review, and element presence. Discussion Examination of the methods by which clinical researchers have investigated the quality and suitability of EHR data for research shows that there are fundamental features of data quality, which may be difficult to measure, as well as proxy dimensions. Researchers interested in the reuse of EHR data for clinical research are recommended to consider the adoption of a consistent taxonomy of EHR data quality, to remain aware of the task-dependence of data quality, to integrate work on data quality assessment from other fields, and to adopt systematic, empirically driven, statistically based methods of data quality assessment. Conclusion There is currently little consistency or potential generalizability in the methods used to assess EHR data quality. If the reuse of EHR data for clinical research is to become accepted, researchers should adopt validated, systematic methods of EHR data quality assessment.

关键词： Clinical research clinical research informatics data quality electronic health records knowledge acquisition knowledge acquisition and knowledge management knowledge bases knowledge representations methods for integration of information from disparate sources secondary use

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A practical approach to achieve private medical record linkage in light of public resources

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JOURNAL OF THE AMERICAN MEDICAL INFORMATICS ASSOCIATION 2013年第2期20卷 285-292页

作者： Kuzu, Mehmet Kantarcioglu, Murat Durham, Elizabeth Ashley Toth, Csaba Malin, Bradley Univ Texas Dallas Dept Comp Sci Dallas TX 75230 USA Vanderbilt Univ Dept Biomed Informat Nashville TN 37203 USA Vanderbilt Univ Dept Elect Engn & Comp Sci Nashville TN 37203 USA

Objective integration of patients' records across resources enhances analytics. To address privacy concerns, emerging strategies such as Bloom filter encodings (BFEs), enable integration while obscuring identifiers. However, recent investigations demonstrate BFEs are, in theory, vulnerable to cryptanalysis when encoded identifiers are randomly selected from a public resource. This study investigates the extent to which cryptanalysis conditions hold for (1) real patient records and (2) a countermeasure that obscures the frequencies of the identifying values in encoded datasets. Design First, to investigate the strength of cryptanalysis for real patient records, we build BFEs from identifiers in an electronic medical record system and apply cryptanalysis using identifiers in a publicly available voter registry. Second, to investigate the countermeasure under ideal cryptanalysis conditions, we compose BFEs from the identifiers that are randomly selected from a public voter registry. Measurement We utilize precision (ie, rate of correct re-identified encodings) and computation efficiency (ie, time to complete cryptanalysis) to assess the performance of cryptanalysis in BFEs before and after application of the countermeasure. Results Cryptanalysis can achieve high precision when the encoded identifiers are composed of a random sample of a public resource (ie, a voter registry). However, we also find that the attack is less efficient and may not be practical for more realistic scenarios. By contrast, the proposed countermeasure made cryptanalysis impractical in terms of precision and efficiency. Conclusions Performance of cryptanalysis against BFEs based on patient data is significantly lower than theoretical estimates. The proposed countermeasure makes BFEs resistant to known practical attacks.

关键词： Languages and computational methods advanced algorithms personal health records and self-care systems privacy security record linkage ethical study methods statistical analysis of large datasets methods for integration of information from disparate sources distributed systems assuring information system security and personal privacy

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Reducing patient re-identification risk for laboratory results within research datasets

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JOURNAL OF THE AMERICAN MEDICAL INFORMATICS ASSOCIATION 2013年第1期20卷 95-101页

作者： Atreya, Ravi V. Smith, Joshua C. McCoy, Allison B. Malin, Bradley Miller, Randolph A. Vanderbilt Univ Sch Med Dept Biomed Informat Nashville TN 37212 USA Univ Texas Hlth Sci Ctr Sch Biomed Informat Houston TX USA Vanderbilt Univ Dept Elect Engn & Comp Sci Sch Engn Nashville TN USA Vanderbilt Univ Sch Med Dept Med Nashville TN 37212 USA Vanderbilt Univ Sch Nursing Nashville TN 37240 USA

Objective To try to lower patient re-identification risks for biomedical research databases containing laboratory test results while also minimizing changes in clinical data interpretation. Materials and methods In our threat model, an attacker obtains 5-7 laboratory results from one patient and uses them as a search key to discover the corresponding record in a de-identified biomedical research database. To test our models, the existing Vanderbilt TIME database of 8.5 million Safe Harbor de-identified laboratory results from 61 280 patients was used. The uniqueness of unaltered laboratory results in the dataset was examined, and then two data perturbation models were applied-simple random offsets and an expert-derived clinical meaning-preserving model. A rank-based re-identification algorithm to mimic an attack was used. The re-identification risk and the retention of clinical meaning for each model's perturbed laboratory results were assessed. Results Differences in re-identification rates between the algorithms were small despite substantial divergence in altered clinical meaning. The expert algorithm maintained the clinical meaning of laboratory results better (affecting up to 4% of test results) than simple perturbation (affecting up to 26%). Discussion and conclusion With growing impetus for sharing clinical data for research, and in view of healthcare-related federal privacy regulation, methods to mitigate risks of re-identification are important. A practical, expert-derived perturbation algorithm that demonstrated potential utility was developed. Similar approaches might enable administrators to select data protection scheme parameters that meet their preferences in the trade-off between the protection of privacy and the retention of clinical meaning of shared data.

关键词： Privacy information dissemination laboratory marker electronic health records confidentiality/legislation and jurisprudence biomedical informatics nlp data-mining clinical decision support error management and prevention evaluation monitoring and surveillance ADEs ethical study methods Statistical analysis of large datasets methods for integration of information from disparate sources distributed systems assuring information system security and personal privacy CDSS internal medicine

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Hiding in plain sight: use of realistic surrogates to reduce exposure of protected health information in clinical text

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JOURNAL OF THE AMERICAN MEDICAL INFORMATICS ASSOCIATION 2013年第2期20卷 342-348页

作者： Carrell, David Malin, Bradley Aberdeen, John Bayer, Samuel Clark, Cheryl Wellner, Ben Hirschman, Lynette Grp Hlth Res Inst Seattle WA 98101 USA Vanderbilt Univ Dept Biomed Informat Nashville TN USA Vanderbilt Univ Dept Elect Engn & Comp Sci Nashville TN USA Mitre Corp Bedford MA 01730 USA

Objective Secondary use of clinical text is impeded by a lack of highly effective, low-cost de-identification methods. Both, manual and automated methods for removing protected health information, are known to leave behind residual identifiers. The authors propose a novel approach for addressing the residual identifier problem based on the theory of Hiding In Plain Sight (HIPS). Materials and methods HIPS relies on obfuscation to conceal residual identifiers. According to this theory, replacing the detected identifiers with realistic but synthetic surrogates should collectively render the few 'leaked' identifiers difficult to distinguish from the synthetic surrogates. The authors conducted a pilot study to test this theory on clinical narrative, de-identified by an automated system. Test corpora included 31 oncology and 50 family practice progress notes read by two trained chart abstractors and an informaticist. Results Experimental results suggest approximately 90% of residual identifiers can be effectively concealed by the HIPS approach in text containing average and high densities of personal identifying information. Discussion This pilot test suggests HIPS is feasible, but requires further evaluation. The results need to be replicated on larger corpora of diverse origin under a range of detection scenarios. Error analyses also suggest areas where surrogate generation techniques can be refined to improve efficacy. Conclusions If these results generalize to existing high-performing de-identification systems with recall rates of 94e98%, HIPS could increase the effective de-identification rates of these systems to levels above 99% without further advancements in system recall. Additional and more rigorous assessment of the HIPS approach is warranted.

关键词： Patient data privacy natural language processing data sharing HIPAA de-identification clinical text ethical study methods statistical analysis of large datasets methods for integration of information from disparate sources assuring information system security and personal privacy machine translation medical informatics

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Clinical research informatics: a conceptual perspective

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JOURNAL OF THE AMERICAN MEDICAL INFORMATICS ASSOCIATION 2012年第E1期19卷 E36-E42页

作者： Kahn, Michael G. Weng, Chunhua Univ Colorado Dept Pediat Aurora CO USA Columbia Univ Dept Biomed Informat New York NY USA

Clinical research informatics is the rapidly evolving sub-discipline within biomedical informatics that focuses on developing new informatics theories, tools, and solutions to accelerate the full translational continuum: basic research to clinical trials (T1), clinical trials to academic health center practice (T2), diffusion and implementation to community practice (T3), and 'real world' outcomes (T4). We present a conceptual model based on an informatics-enabled clinical research workflow, integration across heterogeneous data sources, and core informatics tools and platforms. We use this conceptual model to highlight 18 new articles in the JAMIA special issue on clinical research informatics.

关键词： Clinical research informatics clinical and translational research visualization of data and knowledge knowledge representations methods for integration of information from disparate sources data models data exchange knowledge bases knowledge acquisition knowledge acquisition and knowledge management

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The use of count data models in biomedical informatics evaluation research

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JOURNAL OF THE AMERICAN MEDICAL INFORMATICS ASSOCIATION 2012年第1期19卷 39-44页

作者： Du, Jing Park, Young-Taek Theera-Ampornpunt, Nawanan McCullough, Jeffrey S. Speedie, Stuart M. Univ Minnesota Div Hlth Policy & Management Minneapolis MN 55455 USA Univ Minnesota Inst Hlth Informat Minneapolis MN 55455 USA

Objectives Studies on the impact and value of health information technology (HIT) have often focused on outcome measures that are counts of such things as hospital admissions or the number of laboratory tests per patient. These measures with their highly skewed distributions (high frequency of 0s and 1s) are more appropriately analyzed with count data models than the much more frequently used variations of ordinary least squares (OLS). Use of a statistical procedure that does not properly fit the distribution of the data can result in significant findings being overlooked. The objective of this paper is to encourage greater use of count data models by demonstrating their utility with an example based on the authors' current work. Target audience Researchers conducting impact and outcome studies related to HIT. Scope We review and discuss count data models and illustrate their value in comparison to OLS using an example from a study of the impact of an electronic health record (EHR) on laboratory test orders. The best count data model reveals significant relationships that OLS does not detect. We conclude that comprehensive model checking is highly recommended to identify the most appropriate analytic model when the dependent variable being examined contains count data. This strategy can lead to more valid and precise findings in HIT evaluation studies.

关键词： Poisson regression negative binomial regression hurdle regression zero-inflated regression count data model phone number YT health IT adoption people and organizational issues measuring/improving patient safety and reducing medical errors classical experimental and quasi-experimental study methods (lab and field) methods for integration of information from disparate sources supporting practice at a distance (telehealth) data models data exchange communication and integration across care settings (inter- and intra-enterprise) human-computer interaction and human-centered computing

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The impact of electronic health records on care of heart failure patients in the emergency room

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JOURNAL OF THE AMERICAN MEDICAL INFORMATICS ASSOCIATION 2012年第3期19卷 334-340页

作者： Connelly, Donald P. Park, Young-Taek Du, Jing Theera-Ampornpunt, Nawanan Gordon, Bradley D. Bershow, Barry A. Gensinger, Raymond A., Jr. Shrift, Michael Routhe, Daniel T. Speedie, Stuart M. Univ Minnesota Dept Lab Med & Pathol Minneapolis MN 55455 USA Univ Minnesota Inst Hlth Informat Minneapolis MN 55455 USA Hlth Insurance Review & Assessment Serv Div Healthcare Finance Anal & Res Seoul South Korea Univ Minnesota Sch Publ Hlth Div Hlth Policy & Management Minneapolis MN 55455 USA Mahidol Univ Ramathibodi Hosp Fac Med Hlth Informat Div Bangkok 10400 Thailand Reg Hosp Dept Emergency Med St Paul MN USA HealthPartners Med Grp & Clin St Paul MN USA Bershow Consulting LLC Chanhassen MN USA Fairview Hlth Serv Corp Serv Minneapolis MN USA Univ Minnesota Dept Med Minneapolis MN 55455 USA Univ Minnesota Dept Nursing Minneapolis MN 55455 USA Allina Hosp & Clin Minneapolis MN USA

Objective To evaluate if electronic health records (EHR) have observable effects on care outcomes, we examined quality and efficiency measures for patients presenting to emergency departments (ED). Materials and methods We conducted a retrospective study of 5166 adults with heart failure in three metropolitan EDs. Patients were termed internal if prior information was in the EHR upon ED presentation, otherwise external. Associations of internality with hospitalization, mortality, length of stay (LOS), and numbers of tests, procedures, and medications ordered in the ED were examined after adjusting for age, gender, race, marital status, comorbidities and hospitalization as a proxy for acuity level where appropriate. Results At two EDs internals had lower odds of mortality if hospitalized (OR 0.55;95% CI 0.38 to 0.81 and 0.45;0.21 to 0.96), fewer laboratory tests during the ED visit (-4.6%;-8.9% to -0.1% and -14.0%;-19.5% to -8.1%) as well as fewer medications (-33.6%;-38.4% to -28.4% and -21.3%;-33.2% to -7.3%). At one of these two EDs, internals had lower odds of hospitalization (0.37;0.22 to 0.60). At the third ED, internal patients only experienced a prolonged ED LOS (32.3%;6.3% to 64.8%) but no other differences. There was no association with hospital LOS or number of procedures ordered. Discussion EHR availability was associated with salutary outcomes in two of three ED settings and prolongation of ED LOS at a third, but evidence was mixed and causality remains to be determined. Conclusions An EHR may have the potential to be a valuable adjunct in the care of heart failure patients.

关键词： Electronic health records emergency department evaluation heart failure clinical outcomes YT health IT adoption people and organizational issues emergency medicine medical informatics measuring/improving patient safety and reducing medical errors classical experimental and quasi-experimental study methods (lab and field) methods for integration of information from disparate sources supporting practice at a distance (telehealth) data models data exchange communication aintegration across care settings (inter- and intra-enterprise) human-computer interaction and human-centered computing

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Using ontology-based annotation to profile disease research

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JOURNAL OF THE AMERICAN MEDICAL INFORMATICS ASSOCIATION 2012年第E1期19卷 E177-E186页

作者： Liu, Yi Coulet, Adrien LePendu, Paea Shah, Nigam H. Stanford Univ Stanford Ctr Biomed Informat Res Stanford CA 94305 USA LORIA INRIA Nancy Vandoeuvre Les Nancy France

Background Profiling the allocation and trend of research activity is of interest to funding agencies, administrators, and researchers. However, the lack of a common classification system hinders the comprehensive and systematic profiling of research activities. This study introduces ontology-based annotation as a method to overcome this difficulty. Analyzing over a decade of funding data and publication data, the trends of disease research are profiled across topics, across institutions, and over time. Results This study introduces and explores the notions of research sponsorship and allocation and shows that leaders of research activity can be identified within specific disease areas of interest, such as those with high mortality or high sponsorship. The funding profiles of disease topics readily cluster themselves in agreement with the ontology hierarchy and closely mirror the funding agency priorities. Finally, four temporal trends are identified among research topics. Conclusions This work utilizes disease ontology (DO)based annotation to profile effectively the landscape of biomedical research activity. By using DO in this manner a use-case driven mechanism is also proposed to evaluate the utility of classification hierarchies.

关键词： Applications that link biomedical knowledge from diverse primary sources (includes automated indexing) bioinformatics collaborative technologies informatics knowledge discovery knowledge representations machine learning medical records methods for integration of information from disparate sources natural language processing ontologies ontology pharmacogenomics statistical analysis of large datasets text mining

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Patient characteristics associated with venous thromboembolic events: a cohort study using pooled electronic health record data

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JOURNAL OF THE AMERICAN MEDICAL INFORMATICS ASSOCIATION 2012年第6期19卷 965-972页

作者： Kaelber, David C. Foster, Wendy Gilder, Jason Love, Thomas E. Jain, Anil K. MetroHlth Syst Dept Informat Serv Cleveland OH USA Case Western Reserve Univ Dept Internal Med Cleveland OH 44106 USA Case Western Reserve Univ Dept Pediat Cleveland OH 44106 USA Case Western Reserve Univ Dept Epidemiol Cleveland OH 44106 USA MetroHlth Syst Ctr Clin Informat Res & Educ Cleveland OH USA Explorys Inc Cleveland OH USA Case Western Reserve Univ Sch Med Dept Med Cleveland OH 44106 USA Case Western Reserve Univ Sch Med Dept Epidemiol Cleveland OH USA Case Western Reserve Univ Ctr Hlth Care Res & Policy MetroHlth Syst Cleveland OH 44106 USA Cleveland Clin Dept Internal Med Cleveland OH 44106 USA Case Western Reserve Univ Sch Med Dept Biostat Cleveland OH USA

Objective To demonstrate the potential of de-identified clinical data from multiple healthcare systems using different electronic health records (EHR) to be efficiently used for very large retrospective cohort studies. Materials and methods Data of 959 030 patients, pooled from multiple different healthcare systems with distinct ERR, were obtained. Data were standardized and normalized using common ontologies, searchable through a HIPAA-compliant, patient de-identified web application (Explore;Explorys Inc). Patients were 26 years or older seen in multiple healthcare systems from 1999 to 2011 with data from EHR. Results Comparing obese, tall subjects with normal body mass index, short subjects, the venous thromboembolic events (VIE) OR was 1.83 (95% CI 1.76 to 1.91) for women and 1.21 (1.10 to 1.32) for men. Weight had more effect then height on VIE. Compared with Caucasian, Hispanic/Latino subjects had a much lower risk of VIE (female OR 0.47, 0.41 to 0.55;male OR 0.24, 0.20 to 0.28) and African-Americans a substantially higher risk (female OR 1.83, 1.76 to 1.91;male OR 1.58, 1.50 to 1.66). This 13-year retrospective study of almost one million patients was performed over approximately 125 h in 11 weeks, part time by the five authors. Discussion As research informatics tools develop and more clinical data become available in ERR, it is important to study and understand unique opportunities for clinical research informatics to transform the scale and resources needed to perform certain types of clinical research. Conclusions With the right clinical research informatics tools and EHR data, some types of very large cohort studies can be completed with minimal resources.

关键词： Clinical informatics demonstrating return on IT investment developing/using clinical decision support (other than diagnostic) and guideline systems developing/using computerized provider order entry enhancing the conduct of biological/clinical research and trials methods for integration of information from disparate sources monitoring the health of populations personal health records and self-care systems

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