1
|
Dal Moro R, Helal L, Almeida L, Osório J, Schmidt MI, Mengue S, Duncan BB. The Development of the Municipal Registry of People with Diabetes in Porto Alegre, Brazil. J Clin Med 2024; 13:2783. [PMID: 38792326 PMCID: PMC11121854 DOI: 10.3390/jcm13102783] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/25/2024] [Revised: 04/19/2024] [Accepted: 05/07/2024] [Indexed: 05/26/2024] Open
Abstract
Background/Objective: Diabetes registries that enhance surveillance and improve medical care are uncommon in low- and middle-income countries, where most of the diabetes burden lies. We aimed to describe the methodological and technical aspects adopted in the development of a municipal registry of people with diabetes using local and national Brazilian National Health System databases. Methods: We obtained data between July 2018 and June 2021 based on eight databases covering primary care, specialty and emergency consultations, medication dispensing, outpatient exam management, hospitalizations, and deaths. We identified diabetes using the International Classification of Disease (ICD), International Classification of Primary Care (ICPC), medications for diabetes, hospital codes for the treatment of diabetes complications, and exams for diabetes management. Results: After data processing and database merging using deterministic and probabilistic linkage, we identified 73,185 people with diabetes. Considering that 1.33 million people live in Porto Alegre, the registry captured 5.5% of the population. Conclusions: With additional data processing, the registry can reveal information on the treatment and outcomes of people with diabetes who are receiving publicly financed care in Porto Alegre. It will provide metrics for epidemiologic surveillance, such as the incidence, prevalence, rates, and trends of complications and causes of mortality; identify inadequacies; and provide information. It will enable healthcare providers to monitor the quality of care, identify inadequacies, and provide feedback as needed.
Collapse
Affiliation(s)
- Rafael Dal Moro
- Postgraduate Program in Epidemiology, Universidade Federal do Rio Grande do Sul, Porto Alegre 90035-003, Brazil
- Secretaria Municipal de Saúde de Porto Alegre, Porto Alegre 90010-150, Brazil
| | - Lucas Helal
- Postgraduate Program in Epidemiology, Universidade Federal do Rio Grande do Sul, Porto Alegre 90035-003, Brazil
| | - Leonel Almeida
- Postgraduate Program in Epidemiology, Universidade Federal do Rio Grande do Sul, Porto Alegre 90035-003, Brazil
- Secretaria Municipal de Saúde de Porto Alegre, Porto Alegre 90010-150, Brazil
| | - Jorge Osório
- Secretaria Municipal de Saúde de Porto Alegre, Porto Alegre 90010-150, Brazil
| | - Maria Ines Schmidt
- Postgraduate Program in Epidemiology, Universidade Federal do Rio Grande do Sul, Porto Alegre 90035-003, Brazil
| | - Sotero Mengue
- Postgraduate Program in Epidemiology, Universidade Federal do Rio Grande do Sul, Porto Alegre 90035-003, Brazil
| | - Bruce B. Duncan
- Postgraduate Program in Epidemiology, Universidade Federal do Rio Grande do Sul, Porto Alegre 90035-003, Brazil
| |
Collapse
|
2
|
Wornow M, Xu Y, Thapa R, Patel B, Steinberg E, Fleming S, Pfeffer MA, Fries J, Shah NH. The shaky foundations of large language models and foundation models for electronic health records. NPJ Digit Med 2023; 6:135. [PMID: 37516790 PMCID: PMC10387101 DOI: 10.1038/s41746-023-00879-8] [Citation(s) in RCA: 29] [Impact Index Per Article: 29.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/21/2023] [Accepted: 07/13/2023] [Indexed: 07/31/2023] Open
Abstract
The success of foundation models such as ChatGPT and AlphaFold has spurred significant interest in building similar models for electronic medical records (EMRs) to improve patient care and hospital operations. However, recent hype has obscured critical gaps in our understanding of these models' capabilities. In this narrative review, we examine 84 foundation models trained on non-imaging EMR data (i.e., clinical text and/or structured data) and create a taxonomy delineating their architectures, training data, and potential use cases. We find that most models are trained on small, narrowly-scoped clinical datasets (e.g., MIMIC-III) or broad, public biomedical corpora (e.g., PubMed) and are evaluated on tasks that do not provide meaningful insights on their usefulness to health systems. Considering these findings, we propose an improved evaluation framework for measuring the benefits of clinical foundation models that is more closely grounded to metrics that matter in healthcare.
Collapse
Affiliation(s)
- Michael Wornow
- Department of Computer Science, Stanford University, Stanford, CA, USA.
| | - Yizhe Xu
- Center for Biomedical Informatics Research, Stanford University School of Medicine, Stanford, CA, USA
| | - Rahul Thapa
- Center for Biomedical Informatics Research, Stanford University School of Medicine, Stanford, CA, USA
| | - Birju Patel
- Center for Biomedical Informatics Research, Stanford University School of Medicine, Stanford, CA, USA
| | - Ethan Steinberg
- Department of Computer Science, Stanford University, Stanford, CA, USA
| | - Scott Fleming
- Center for Biomedical Informatics Research, Stanford University School of Medicine, Stanford, CA, USA
| | - Michael A Pfeffer
- Center for Biomedical Informatics Research, Stanford University School of Medicine, Stanford, CA, USA
- Technology and Digital Services, Stanford Health Care, Palo Alto, CA, USA
| | - Jason Fries
- Center for Biomedical Informatics Research, Stanford University School of Medicine, Stanford, CA, USA
| | - Nigam H Shah
- Center for Biomedical Informatics Research, Stanford University School of Medicine, Stanford, CA, USA
- Technology and Digital Services, Stanford Health Care, Palo Alto, CA, USA
- Department of Medicine, Stanford University School of Medicine, Stanford, CA, USA
- Clinical Excellence Research Center, Stanford University School of Medicine, Stanford, CA, USA
| |
Collapse
|
3
|
Fischer C, Alvarico SJ, Wildner B, Schindl M, Simon J. The relationship of hospital and surgeon volume indicators and post-operative outcomes in pancreatic surgery: a systematic literature review, meta-analysis and guidance for valid outcome assessment. HPB (Oxford) 2023; 25:387-399. [PMID: 36813680 DOI: 10.1016/j.hpb.2023.01.008] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/23/2022] [Revised: 01/12/2023] [Accepted: 01/18/2023] [Indexed: 02/24/2023]
Abstract
BACKGROUND Available evidence on the volume-outcome relationship after pancreatic surgery is limited due to the narrow focus of interventions, volume indicators and outcomes considered as well as due to methodological differences of the included studies. Therefore, we aim to evaluate the volume-outcome relationship following pancreatic surgery following strict study selection and quality criteria, to identify aspects of methodological variation and to define a set of key methodological indicators to consider when aiming for comparable and valid outcome assessment. METHODS Four electronic databases were searched to identify studies on the volume-outcome relationship in pancreatic surgery published between the years 2000-2018. Following a double-screening process, data extraction, quality appraisal, and subgroup analysis, results of included studies were stratified and pooled using random effects meta-analysis. RESULTS Consistent associations were found between high hospital volume and both postoperative mortality (OR 0.35, 95% CI: 0.29-0.44) and major complications (OR 0.87, 95% CI: 0.80-0.94). A significant decrease in the odds ratio was also found for high surgeon volume and postoperative mortality (OR 0.29, 95%CI: 0.22-0.37). DISCUSSION Our meta-analysis confirms a positive effect for both hospital and surgeon volume indicators for pancreatic surgery. Further harmonization (e.g. surgery types, volume cut-offs/definition, case-mix adjustment, reported outcomes) are recommended for future empirical studies.
Collapse
Affiliation(s)
- Claudia Fischer
- Department of Health Economics, Center for Public Health, Medical University of Vienna, Vienna, Austria.
| | - Stefanie J Alvarico
- Department of Health Economics, Center for Public Health, Medical University of Vienna, Vienna, Austria
| | - B Wildner
- University Library, Medical University of Vienna, Vienna, Austria
| | - Martin Schindl
- Department of Surgery, Comprehensive Cancer Center (CCC), Medical University and Pancreatic Cancer Unit, Vienna, Austria
| | - Judit Simon
- Department of Health Economics, Center for Public Health, Medical University of Vienna, Vienna, Austria; Department of Psychiatry, University of Oxford, Warneford Hospital, Oxford, United Kingdom; Ludwig Boltzmann Institute Applied Diagnostics, Vienna, Austria
| |
Collapse
|
4
|
Chima S, Martinez-Gutierrez J, Hunter B, Manski-Nankervis JA, Emery J. Optimization of a Quality Improvement Tool for Cancer Diagnosis in Primary Care: Qualitative Study. JMIR Form Res 2022; 6:e39277. [PMID: 35925656 PMCID: PMC9389376 DOI: 10.2196/39277] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/12/2022] [Revised: 07/07/2022] [Accepted: 07/07/2022] [Indexed: 11/17/2022] Open
Abstract
Background The most common route to a diagnosis of cancer is through primary care. Delays in diagnosing cancer occur when an opportunity to make a timely diagnosis is missed and is evidenced by patients visiting the general practitioner (GP) on multiple occasions before referral to a specialist. Tools that minimize prolonged diagnostic intervals and reduce missed opportunities to investigate patients for cancer are therefore a priority. Objective This study aims to explore the usefulness and feasibility of a novel quality improvement (QI) tool in which algorithms flag abnormal test results that may be indicative of undiagnosed cancer. This study allows for the optimization of the cancer recommendations before testing the efficacy in a randomized controlled trial. Methods GPs, practice nurses, practice managers, and consumers were recruited to participate in individual interviews or focus groups. Participants were purposively sampled as part of a pilot and feasibility study, in which primary care practices were receiving recommendations relating to the follow-up of abnormal test results for prostate-specific antigen, thrombocytosis, and iron-deficiency anemia. The Clinical Performance Feedback Intervention Theory (CP-FIT) was applied to the analysis using a thematic approach. Results A total of 17 interviews and 3 focus groups (n=18) were completed. Participant themes were mapped to CP-FIT across the constructs of context, recipient, and feedback variables. The key facilitators to use were alignment with workflow, recognized need, the perceived importance of the clinical topic, and the GPs’ perception that the recommendations were within their control. Barriers to use included competing priorities, usability and complexity of the recommendations, and knowledge of the clinical topic. There was consistency between consumer and practitioner perspectives, reporting language concerns associated with the word cancer, the need for more patient-facing resources, and time constraints of the consultation to address patients’ worries. Conclusions There was a recognized need for the QI tool to support the diagnosis of cancer in primary care, but barriers were identified that hindered the usability and actionability of the recommendations in practice. In response, the tool has been refined and is currently being evaluated as part of a randomized controlled trial. Successful and effective implementation of this QI tool could support the detection of patients at risk of undiagnosed cancer in primary care and assist in preventing unnecessary delays.
Collapse
Affiliation(s)
- Sophie Chima
- Centre for Cancer Research, Victorian Comprehensive Cancer Centre, University of Melbourne, Melbourne, Australia
- Department of General Practice, University of Melbourne, Melbourne, Australia
| | - Javiera Martinez-Gutierrez
- Centre for Cancer Research, Victorian Comprehensive Cancer Centre, University of Melbourne, Melbourne, Australia
- Department of General Practice, University of Melbourne, Melbourne, Australia
- Department of Family Medicine, School of Medicine, Pontificia Universidad Católica de Chile, Santiago, Chile
| | - Barbara Hunter
- Department of General Practice, University of Melbourne, Melbourne, Australia
| | | | - Jon Emery
- Centre for Cancer Research, Victorian Comprehensive Cancer Centre, University of Melbourne, Melbourne, Australia
- Department of General Practice, University of Melbourne, Melbourne, Australia
| |
Collapse
|
5
|
Data Quality of Automated Comorbidity Lists in Patients With Mental Health and Substance Use Disorders. Comput Inform Nurs 2022; 40:497-505. [PMID: 35234709 PMCID: PMC9262752 DOI: 10.1097/cin.0000000000000889] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/30/2022]
Abstract
EHRs provide an opportunity to conduct research on underrepresented oncology populations with mental health and substance use disorders. However, a lack of data quality may introduce unintended bias into EHR data. The objective of this article is describe our analysis of data quality within automated comorbidity lists commonly found in EHRs. Investigators conducted a retrospective chart review of 395 oncology patients from a safety-net integrated healthcare system. Statistical analysis included κ coefficients and a condition logistic regression. Subjects were racially and ethnically diverse and predominantly used Medicaid insurance. Weak κ coefficients ( κ = 0.2-0.39, P < .01) were noted for drug and alcohol use disorders indicating deficiencies in comorbidity documentation within the automated comorbidity list. Further, conditional logistic regression analyses revealed deficiencies in comorbidity documentation in patients with drug use disorders (odds ratio, 11.03; 95% confidence interval, 2.71-44.9; P = .01) and psychoses (odds ratio, 0.04; confidence interval, 0.02-0.10; P < .01). Findings suggest deficiencies in automatic comorbidity lists as compared with a review of provider narrative notes when identifying comorbidities. As healthcare systems increasingly use EHR data in clinical studies and decision making, the quality of healthcare delivery and clinical research may be affected by discrepancies in the documentation of comorbidities.
Collapse
|
6
|
Zeng J, Gensheimer MF, Rubin DL, Athey S, Shachter RD. Uncovering interpretable potential confounders in electronic medical records. Nat Commun 2022; 13:1014. [PMID: 35197467 PMCID: PMC8866497 DOI: 10.1038/s41467-022-28546-8] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/25/2021] [Accepted: 01/28/2022] [Indexed: 12/25/2022] Open
Abstract
Randomized clinical trials (RCT) are the gold standard for informing treatment decisions. Observational studies are often plagued by selection bias, and expert-selected covariates may insufficiently adjust for confounding. We explore how unstructured clinical text can be used to reduce selection bias and improve medical practice. We develop a framework based on natural language processing to uncover interpretable potential confounders from text. We validate our method by comparing the estimated hazard ratio (HR) with and without the confounders against established RCTs. We apply our method to four cohorts built from localized prostate and lung cancer datasets from the Stanford Cancer Institute and show that our method shifts the HR estimate towards the RCT results. The uncovered terms can also be interpreted by oncologists for clinical insights. We present this proof-of-concept study to enable more credible causal inference using observational data, uncover meaningful insights from clinical text, and inform high-stakes medical decisions.
Collapse
Affiliation(s)
- Jiaming Zeng
- Department of Management Science and Engineering, Stanford University, Stanford, CA, 94305, USA.
| | - Michael F Gensheimer
- Department of Radiation Oncology, Stanford School of Medicine, Stanford, CA, 94305, USA
| | - Daniel L Rubin
- Department of Biomedical Data Science, Radiology, and Medicine, Stanford School of Medicine, Stanford, CA, 94305, USA
| | - Susan Athey
- Graduate School of Business, Stanford University, Stanford, CA, 94305, USA
| | - Ross D Shachter
- Department of Management Science and Engineering, Stanford University, Stanford, CA, 94305, USA
| |
Collapse
|
7
|
Logan MS, Myers LC, Salmasian H, Levine DM, Roy CG, Reynolds ME, Sato L, Keohane C, Frits ML, Volk LA, Akindele RN, Randazza JM, Dulgarian SM, Shahian DM, Bates DW, Mort E. Expert Consensus on Currently Accepted Measures of Harm. J Patient Saf 2021; 17:e1726-e1731. [PMID: 32769419 PMCID: PMC8612889 DOI: 10.1097/pts.0000000000000754] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/05/2022]
Abstract
BACKGROUND Twenty-five years after the seminal work of the Harvard Medical Practice Study, the numbers and specific types of health care measures of harm have evolved and expanded. Using the World Café method to derive expert consensus, we sought to generate a contemporary list of triggers and adverse event measures that could be used for chart review to determine the current incidence of inpatient and outpatient adverse events. METHODS We held a modified World Café event in March 2018, during which content experts were divided into 10 tables by clinical domain. After a focused discussion of a prepopulated list of literature-based triggers and measures relevant to that domain, they were asked to rate each measure on clinical importance and suitability for chart review and electronic extraction (very low, low, medium, high, very high). RESULTS Seventy-one experts from 9 diverse institutions attended (primary acceptance rate, 72%). Of 525 total triggers and measures, 67% of 391 measures and 46% of 134 triggers were deemed to have high or very high clinical importance. For those triggers and measures with high or very high clinical importance, 218 overall were deemed to be highly amenable to chart review and 198 overall were deemed to be suitable for electronic surveillance. CONCLUSIONS The World Café method effectively prioritized measures/triggers of high clinical importance including those that can be used in chart review, which is considered the gold standard. A future goal is to validate these measures using electronic surveillance mechanisms to decrease the need for chart review.
Collapse
Affiliation(s)
- Merranda S. Logan
- From the Division of Nephrology, Massachusetts General Hospital
- Edward P. Lawrence Center for Quality and Safety, Massachusetts General Hospital
- Harvard Medical School
| | - Laura C. Myers
- Edward P. Lawrence Center for Quality and Safety, Massachusetts General Hospital
- Harvard Medical School
- Division of Pulmonary and Critical Care, Massachusetts General Hospital
| | | | - David Michael Levine
- Harvard Medical School
- Division of General Internal Medicine, Brigham and Women’s Hospital, Boston
| | - Christopher G. Roy
- Harvard Medical School
- Division of General Internal Medicine, Mt Auburn Hospital, Cambridge
| | - Mark E. Reynolds
- Risk Management Foundation of the Harvard Medical Institutions (CRICO)
| | - Luke Sato
- Harvard Medical School
- Risk Management Foundation of the Harvard Medical Institutions (CRICO)
| | - Carol Keohane
- Risk Management Foundation of the Harvard Medical Institutions (CRICO)
| | - Michelle L. Frits
- Division of General Internal Medicine, Brigham and Women’s Hospital, Boston
| | - Lynn A. Volk
- Clinical and Quality Analysis, Mass General Brigham
| | - Ruth N. Akindele
- Division of General Internal Medicine, Brigham and Women’s Hospital, Boston
| | | | - Sevan M. Dulgarian
- Division of General Internal Medicine, Brigham and Women’s Hospital, Boston
| | - David M. Shahian
- Edward P. Lawrence Center for Quality and Safety, Massachusetts General Hospital
- Harvard Medical School
- Department of Surgery, Massachusetts General Hospital
| | - David Westfall Bates
- Harvard Medical School
- Division of General Internal Medicine, Brigham and Women’s Hospital, Boston
- Clinical and Quality Analysis, Mass General Brigham
- Harvard T. H. Chan School of Public Health
| | - Elizabeth Mort
- Edward P. Lawrence Center for Quality and Safety, Massachusetts General Hospital
- Division of Internal Medicine, Massachusetts General Hospital
- Department of Health Care Policy, Harvard Medical School, Boston, MA
| |
Collapse
|
8
|
D'Amore JD, McCrary LK, Denson J, Li C, Vitale CJ, Tokachichu P, Sittig DF, McCoy AB, Wright A. Clinical data sharing improves quality measurement and patient safety. J Am Med Inform Assoc 2021; 28:1534-1542. [PMID: 33712850 PMCID: PMC8279795 DOI: 10.1093/jamia/ocab039] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/13/2020] [Revised: 01/23/2021] [Accepted: 02/15/2021] [Indexed: 02/05/2023] Open
Abstract
OBJECTIVE Accurate and robust quality measurement is critical to the future of value-based care. Having incomplete information when calculating quality measures can cause inaccuracies in reported patient outcomes. This research examines how quality calculations vary when using data from an individual electronic health record (EHR) and longitudinal data from a health information exchange (HIE) operating as a multisource registry for quality measurement. MATERIALS AND METHODS Data were sampled from 53 healthcare organizations in 2018. Organizations represented both ambulatory care practices and health systems participating in the state of Kansas HIE. Fourteen ambulatory quality measures for 5300 patients were calculated using the data from an individual EHR source and contrasted to calculations when HIE data were added to locally recorded data. RESULTS A total of 79% of patients received care at more than 1 facility during the 2018 calendar year. A total of 12 994 applicable quality measure calculations were compared using data from the originating organization vs longitudinal data from the HIE. A total of 15% of all quality measure calculations changed (P < .001) when including HIE data sources, affecting 19% of patients. Changes in quality measure calculations were observed across measures and organizations. DISCUSSION These results demonstrate that quality measures calculated using single-site EHR data may be limited by incomplete information. Effective data sharing significantly changes quality calculations, which affect healthcare payments, patient safety, and care quality. CONCLUSIONS Federal, state, and commercial programs that use quality measurement as part of reimbursement could promote more accurate and representative quality measurement through methods that increase clinical data sharing.
Collapse
Affiliation(s)
- John D D'Amore
- Informatics Department, Diameter Health, Farmington, Connecticut, USA
| | | | - Jody Denson
- Kansas Health Information Network, Topeka, Kansas, USA
| | - Chun Li
- Informatics Department, Diameter Health, Farmington, Connecticut, USA
| | | | | | - Dean F Sittig
- School of Biomedical Informatics, University of Texas Health Science Center at Houston, Houston, Texas, USA
| | - Allison B McCoy
- Department of Biomedical Informatics, Vanderbilt University Medical Center, Nashville, Tennessee, USA
| | - Adam Wright
- Department of Biomedical Informatics, Vanderbilt University Medical Center, Nashville, Tennessee, USA
| |
Collapse
|
9
|
Lucas JA, Marino M, Fankhauser K, Bazemore A, Giebultowicz S, Cowburn S, Kaufmann J, Ezekiel-Herrera D, Heintzman J. Role of social deprivation on asthma care quality among a cohort of children in US community health centres. BMJ Open 2021; 11:e045131. [PMID: 34162640 PMCID: PMC8230996 DOI: 10.1136/bmjopen-2020-045131] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 11/04/2022] Open
Abstract
OBJECTIVE Social deprivation is associated with worse asthma outcomes. The Social Deprivation Index is a composite measure of social determinants of health used to identify neighbourhood-level disadvantage in healthcare. Our objective was to determine if higher neighbourhood-level social deprivation is associated with documented asthma care quality measures among children treated at community health centres (CHCs). METHODS SETTING, PARTICIPANTS, OUTCOME MEASURES We used data from CHCs in 15 states in the Accelerating Data Value Across a National Community Health Center Network (ADVANCE). The sample included 34 266 children with asthma from 2008 to 2017, aged 3-17 living in neighbourhoods with differing levels of social deprivation measured using quartiles of the Social Deprivation Index score. We conducted logistic regression to examine the odds of problem list documentation of asthma and asthma severity, and negative binomial regression for rates of albuterol, inhaled steroid and oral steroid prescription adjusted for patient-level covariates. RESULTS Children from the most deprived neighbourhoods had increased rates of albuterol (rate ratio (RR)=1.22, 95% CI 1.13 to 1.32) compared with those in the least deprived neighbourhoods, while the point estimate for inhaled steroids was higher, but fell just short of significance at the alpha=0.05 level (RR=1.16, 95% CI 0.99 to 1.34). We did not observe community-level differences in problem list documentation of asthma or asthma severity. CONCLUSIONS Higher neighbourhood-level social deprivation was associated with more albuterol and inhaled steroid prescriptions among children with asthma, while problem list documentation of asthma and asthma severity varied little across neighbourhoods with differing deprivation scores. While the homogeneity of the CHC safety net setting studied may mitigate variation in diagnosis and documentation of asthma, enhanced clinician awareness of differences in community risk could help target paediatric patients at risk of lower quality asthma care.
Collapse
Affiliation(s)
- Jennifer A Lucas
- Family Medicine, Oregon Health & Science University, Portland, Oregon, USA
| | - Miguel Marino
- Family Medicine, Oregon Health & Science University, Portland, Oregon, USA
- School of Public Health, Oregon Health & Science University, Portland, Oregon, USA
| | - Katie Fankhauser
- Family Medicine, Oregon Health & Science University, Portland, Oregon, USA
| | | | | | | | - Jorge Kaufmann
- Family Medicine, Oregon Health & Science University, Portland, Oregon, USA
| | | | - John Heintzman
- Family Medicine, Oregon Health & Science University, Portland, Oregon, USA
- OCHIN, Portland, Oregon, USA
| |
Collapse
|
10
|
Kharrazi H, Ma X, Chang HY, Richards TM, Jung C. Comparing the Predictive Effects of Patient Medication Adherence Indices in Electronic Health Record and Claims-Based Risk Stratification Models. Popul Health Manag 2021; 24:601-609. [PMID: 33544044 DOI: 10.1089/pop.2020.0306] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/13/2022] Open
Abstract
Multiple indices are available to measure medication adherence behaviors. Medication adherence measures, however, have rarely been extracted from electronic health records (EHRs) for population-level risk predictions. This study assessed the value of medication adherence indices in improving predictive models of cost and hospitalization. This study included a 2-year retrospective cohort of patients younger than age 65 years with linked EHR and insurance claims data. Three medication adherence measures were calculated: medication regimen complexity index (MRCI), medication possession ratio (MPR), and prescription fill rate (PFR). The authors examined the effects of adding these measures to 3 predictive models of utilization: a demographics model, a conventional model (Charlson index), and an advanced diagnosis-based model. Models were trained using EHR and claims data. The study population had an overall MRCI, MPR, and PFR of 14.6 ± 17.8, .624 ± .310, and .810 ± .270, respectively. Adding MRCI and MPR to the demographic and the morbidity models using claims data improved forecasting of next-year hospitalization substantially (eg, AUC of the demographic model increased from .605 to .656 using MRCI). Nonetheless, such boosting effects were attenuated for the advanced diagnosis-based models. Although EHR models performed inferior to claims models, adding adherence indices improved EHR model performances at a larger scale (eg, adding MRCI increased AUC by 4.4% for the Charlson model using EHR data compared to 3.8% using claims). This study shows that medication adherence measures can modestly improve EHR- and claims-derived predictive models of cost and hospitalization in non-elderly patients; however, the improvements are minimal for advanced diagnosis-based models.
Collapse
Affiliation(s)
- Hadi Kharrazi
- Center for Population Health IT, Department of Health Policy and Management, Johns Hopkins Bloomberg School of Public Health, Baltimore, Maryland, USA.,Division of Health Sciences Informatics, Johns Hopkins School of Medicine, Baltimore Maryland, USA
| | - Xiaomeng Ma
- Dalla Lana School of Public Health, Institute of Health Policy Management and Evaluations, University of Toronto, Toronto, Canada
| | - Hsien-Yen Chang
- Center for Population Health IT, Department of Health Policy and Management, Johns Hopkins Bloomberg School of Public Health, Baltimore, Maryland, USA
| | - Thomas M Richards
- Center for Population Health IT, Department of Health Policy and Management, Johns Hopkins Bloomberg School of Public Health, Baltimore, Maryland, USA
| | - Changmi Jung
- Carey Business School, Johns Hopkins University, Baltimore, Maryland, USA
| |
Collapse
|
11
|
Andreano A, Valsecchi MG, Russo AG, Siena S. Indicators of guideline-concordant care in lung cancer defined with a modified Delphi method and piloted in a cohort of over 5,800 cases. Arch Public Health 2021; 79:12. [PMID: 33494836 PMCID: PMC7830847 DOI: 10.1186/s13690-021-00528-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/06/2020] [Accepted: 01/05/2021] [Indexed: 11/29/2022] Open
Abstract
BACKGROUND To identify indicators of guideline-concordant care in lung cancer, to implement such indicators with cancer registry data linked to health databases, and to pilot them in a cohort of patients from the cancer registry of the Milan Province. METHODS Thirty-four indicators were selected by revision of main guidelines by cancer epidemiologists, and then evaluated by a multidisciplinary panel of clinicians involved in lung cancer care and working on the pathway of lung cancer diagnosis and treatment in the Lombardy region, Italy. With a modified Delphi method, they assessed for each indicator the content validity as a quality measure of the care pathway, the degree of modifiability from the health professional, and the relevance to the health professional. Feasibility was assessed using the cancer registry and the routine health records of the Lombardy region. Feasible indicators were then calculated in the cohort of lung cancer patients diagnosed in 2007-2012 derived from the cancer registry of the Milan Province. Criterion validity was assessed reviewing clinical records of a random sample of 114 patients (threshold for acceptable discordance ≤20%). Finally, reliability was evaluated at the provider level. RESULTS Initially, 34 indicators were proposed for evaluation in the first Delphi round. Of the finally 22 selected indicators, 3 were not feasible because the required information was actually not available. The remaining 19 were calculated on the pilot cohort. After assessment of criterion validity (3 eliminated), 16 indicators were retained in the final set and evaluated for reliability. CONCLUSION The developed and piloted set of indicators is now available to implement and monitor, over time, quality initiatives for lung cancer care in the studied health system.
Collapse
Affiliation(s)
- Anita Andreano
- Epidemiology Unit, Agency for Health Protection of Milan, C.so Italia 19 -, 20122, Milan, Italy
| | - Maria Grazia Valsecchi
- Center of Biostatistic for Clinical Epidemiology, School of Medicine and Surgery, University of Milan Bicocca, Monza, Italy
| | - Antonio Giampiero Russo
- Epidemiology Unit, Agency for Health Protection of Milan, C.so Italia 19 -, 20122, Milan, Italy.
| | - Salvatore Siena
- Niguarda Cancer Center, Grande Ospedale Metropolitano Niguarda and Department of Oncology and Hemato-Oncology, Università degli Studi di Milano, Milan, Italy
| |
Collapse
|
12
|
Personalized treatment options for chronic diseases using precision cohort analytics. Sci Rep 2021; 11:1139. [PMID: 33441956 PMCID: PMC7806725 DOI: 10.1038/s41598-021-80967-5] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/21/2020] [Accepted: 12/31/2020] [Indexed: 12/15/2022] Open
Abstract
To support point-of-care decision making by presenting outcomes of past treatment choices for cohorts of similar patients based on observational data from electronic health records (EHRs), a machine-learning precision cohort treatment option (PCTO) workflow consisting of (1) data extraction, (2) similarity model training, (3) precision cohort identification, and (4) treatment options analysis was developed. The similarity model is used to dynamically create a cohort of similar patients, to inform clinical decisions about an individual patient. The workflow was implemented using EHR data from a large health care provider for three different highly prevalent chronic diseases: hypertension (HTN), type 2 diabetes mellitus (T2DM), and hyperlipidemia (HL). A retrospective analysis demonstrated that treatment options with better outcomes were available for a majority of cases (75%, 74%, 85% for HTN, T2DM, HL, respectively). The models for HTN and T2DM were deployed in a pilot study with primary care physicians using it during clinic visits. A novel data-analytic workflow was developed to create patient-similarity models that dynamically generate personalized treatment insights at the point-of-care. By leveraging both knowledge-driven treatment guidelines and data-driven EHR data, physicians can incorporate real-world evidence in their medical decision-making process when considering treatment options for individual patients.
Collapse
|
13
|
Macinski SE, Gunn JKL, Goyal M, Neighbors C, Yerneni R, Anderson BJ. Validation of an Optimized Algorithm for Identifying Persons Living With Diagnosed HIV From New York State Medicaid Data, 2006-2014. Am J Epidemiol 2020; 189:470-480. [PMID: 31612200 PMCID: PMC7306686 DOI: 10.1093/aje/kwz225] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/27/2019] [Revised: 09/20/2019] [Accepted: 09/23/2019] [Indexed: 11/14/2022] Open
Abstract
Algorithms are regularly used to identify persons living with diagnosed human immunodeficiency virus (HIV) (PLWDH) in Medicaid data. To our knowledge, there are no published reports of an HIV algorithm from Medicaid claims codes that have been compared with an HIV surveillance system to assess its sensitivity, specificity, positive predictive value, and negative predictive value in identifying PLWDH. Therefore, our aims in this study were to 1) develop an algorithm that could identify PLWDH in New York State Medicaid data from 2006-2014 and 2) validate this algorithm using the New York State HIV surveillance system. Classification and regression tree analysis identified 16 nodes that we combined to create a case-finding algorithm with 5 criteria. This algorithm identified 86,930 presumed PLWDH, 88.0% of which were verified by matching to the surveillance system. The algorithm yielded a sensitivity of 94.5%, a specificity of 94.4%, a positive predictive value of 88.0%, and a negative predictive value of 97.6%. This validated algorithm has the potential to improve the utility of Medicaid data for assessing health outcomes and programmatic interventions.
Collapse
Affiliation(s)
- Sarah E Macinski
- Correspondence to Sarah E. Macinski, Bureau of HIV/AIDS Epidemiology, AIDS Institute, New York State Department of Health, Empire State Plaza, Corning Tower, Room 717, Albany, NY 12237-0627 (e-mail: )
| | | | | | | | | | | |
Collapse
|
14
|
Greiver M, Kalia S, Moineddin R, Chen S, Duchen R, Rigobon A. Impact of the diabetes Canada guideline dissemination strategy on dispensed vascular protective medications for older patients in Ontario, Canada: a linked EMR and administrative data study. BMC Health Serv Res 2020; 20:370. [PMID: 32357891 PMCID: PMC7195730 DOI: 10.1186/s12913-020-05232-3] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/05/2019] [Accepted: 04/15/2020] [Indexed: 11/24/2022] Open
Abstract
BACKGROUND The 2013 Diabetes Canada guidelines recommended routinely using vascular protective medications for most patients with diabetes. These medications included statins and angiotensin-converting enzyme inhibitors (ACEIs) or angiotensin receptor blockers (ARBs). Antiplatelet agents were only recommended for secondary prevention of cardiovascular disease. Using Electronic Medical Record (EMR) data, we previously found that guideline dissemination efforts were not associated with an increase in the rate of primary care prescriptions of these medications. However, this needs confirmation: patients can receive prescriptions from different sources including specialists and they may not always fill these prescriptions. Using both EMR and administrative health data, we examined whether guideline dissemination impacted the dispensing of vascular protective medications to patients. METHODS The study population included patients with diabetes aged 66 or over in Ontario, Canada. We created two cohorts using two different approaches: an Electronic Medical Record (EMR) algorithm for diabetes using linked EMR-administrative data and an administrative algorithm using population level administrative data. We examined data from January 2010 to December 2016. Patients with diabetes were deemed to be likely taking a medication (or covered) during a quarter if the daily amount for a dispensed medication would last for at least 75% of days in any given quarter. An interrupted time series analysis was used to assess the proportion of patients covered by each medication class. Proton pump inhibitors (PPIs) were used as a reference. RESULTS There was no increase in the rate of change for medication coverage following guideline release in either the EMR or the administrative diabetes cohorts. For statins, the change in trend was - 0.03, p = 0.7 (EMR) and - 0.12, p = 0.04(administrative). For ACEI/ARBs, this was 0.03, p = 0.6 (EMR) and 0, p = 1(administrative). For antiplatelets, this was 0.001, P = .97 (EMR) and - 0.03, p = 0.03 (administrative). The comparator PPI was - 0.07, p = 0.4 (EMR) and - 0.11, p = 0.002 (administrative). CONCLUSIONS Using both EMR and administrative health data, we confirmed that the Diabetes Canada 2013 guideline dissemination strategy did not lead to an increased rate of coverage for vascular protective medications. Alternative strategies are needed to effect change in practice.
Collapse
Affiliation(s)
- Michelle Greiver
- Gordon F. Cheesbrough Chair in Family and Community Medicine Research, North York General Hospital, 4001 Leslie Street, LE-140, Toronto, Ontario M2K 1E1 Canada
- Department of Family and Community Medicine, Faculty of Medicine, University of Toronto, 500 University Avenue, Toronto, Ontario M5G 1V7 Canada
- ICES, G1 06, 2075 Bayview Avenue, Toronto, Ontario M4N 3M5 Canada
- Institute of Health Policy, Management and Evaluation, University of Toronto, Toronto, Canada
| | - Sumeet Kalia
- Department of Family and Community Medicine, Faculty of Medicine, University of Toronto, 500 University Avenue, Toronto, Ontario M5G 1V7 Canada
| | - Rahim Moineddin
- Department of Family and Community Medicine, Faculty of Medicine, University of Toronto, 500 University Avenue, Toronto, Ontario M5G 1V7 Canada
- ICES, G1 06, 2075 Bayview Avenue, Toronto, Ontario M4N 3M5 Canada
| | - Simon Chen
- ICES, G1 06, 2075 Bayview Avenue, Toronto, Ontario M4N 3M5 Canada
| | - Raquel Duchen
- ICES, G1 06, 2075 Bayview Avenue, Toronto, Ontario M4N 3M5 Canada
| | - Alanna Rigobon
- Faculty of Medicine, University of Toronto, 1 King’s College Cir, Toronto, Ontario M5S 1A8 Canada
| |
Collapse
|
15
|
Cordasco KM, Yuan AH, Danz MJ, Jackson L, Yee EF, Tcheung LS, Washington DL. Veterans Health Administration Primary Care Provider Adherence to Prescribing Guidelines for Systemic Hormone Therapy in Menopausal Women. J Healthc Qual 2020; 41:99-109. [PMID: 30839493 DOI: 10.1097/jhq.0000000000000183] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/26/2022]
Abstract
BACKGROUND Systemic hormone therapy (HT) is effective for treating menopausal symptoms but also confers risks. Therefore, experts have developed clinical guidelines for its use. PURPOSE We assessed primary care guideline adherence in prescribing systemic HT, and associations between adherence and provider characteristics, in four Veterans Health Administration (VA) facilities. METHODS We abstracted medical records associated with new and renewal systemic HT prescriptions examining adherence to guidelines for documenting indications and contraindications; prescribing appropriate dosages; and prescribing progesterone. RESULTS Average guideline adherence was 58%. Among new prescriptions, 74% documented a guideline-adherent indication and 28% documented absence of contraindications. Among renewals, 39% documented a guideline-adherent indication. In prescribing an appropriate dose, 45% of new prescriptions were guideline-adherent. Among renewal prescriptions with conjugated equine estrogen doses ≥0.625 mg or equivalent, 16% documented the dosing rationale. Among 116 prescriptions for systemic estrogen in women with a uterus, progesterone was not prescribed in 8. CONCLUSIONS Guideline adherence in prescribing systemic HT was low among VA primary care providers. Failures to coprescribe progesterone put women at increased risk for endometrial cancer. IMPLICATIONS Intervention development is urgently needed to improve guideline adherence among primary care prescribers of systemic HT for menopause. Similar assessments should be conducted in community settings.
Collapse
|
16
|
Badgery-Parker T, Pearson SA, Elshaug AG. Estimating misclassification error in a binary performance indicator: case study of low value care in Australian hospitals. BMJ Qual Saf 2020; 29:992-999. [PMID: 32165412 DOI: 10.1136/bmjqs-2019-010564] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/30/2019] [Revised: 02/17/2020] [Accepted: 02/23/2020] [Indexed: 12/15/2022]
Abstract
OBJECTIVE Indicators based on hospital administrative data have potential for misclassification error, especially if they rely on clinical detail that may not be well recorded in the data. We applied an approach using modified logistic regression models to assess the misclassification (false-positive and false-negative) rates of low-value care indicators. DESIGN AND SETTING We applied indicators involving 19 procedures to an extract from the New South Wales Admitted Patient Data Collection (1 January 2012 to 30 June 2015) to label episodes as low value. We fit four models (no misclassification, false-positive only, false-negative only, both false-positive and false-negative) for each indicator to estimate misclassification rates and used the posterior probabilities of the models to assess which model fit best. RESULTS False-positive rates were low for most indicators-if the indicator labels care as low value, the care is most likely truly low value according to the relevant recommendation. False-negative rates were much higher but were poorly estimated (wide credible intervals). For most indicators, the models allowing no misclassification or allowing false-negatives but no false-positives had the highest posterior probability. The overall low-value care rate from the indicators was 12%. After adjusting for the estimated misclassification rates from the highest probability models, this increased to 35%. CONCLUSION Binary performance indicators have a potential for misclassification error, especially if they depend on clinical information extracted from administrative data. Indicators should be validated by chart review, but this is resource-intensive and costly. The modelling approach presented here can be used as an initial validation step to identify and revise indicators that may have issues before continuing to a full chart review validation.
Collapse
Affiliation(s)
- Tim Badgery-Parker
- Faculty of Medicine and Health, School of Public Health, Menzies Centre for Health Policy, Charles Perkins Centre, The University of Sydney, Sydney, New South Wales, Australia
| | - Sallie-Anne Pearson
- Faculty of Medicine and Health, School of Public Health, Menzies Centre for Health Policy, Charles Perkins Centre, The University of Sydney, Sydney, New South Wales, Australia.,Centre for Big Data Research in Health, University of New South Wales, Sydney, New South Wales, Australia
| | - Adam G Elshaug
- Faculty of Medicine and Health, School of Public Health, Menzies Centre for Health Policy, Charles Perkins Centre, The University of Sydney, Sydney, New South Wales, Australia.,The Brookings Institution, Washington, DC, USA
| |
Collapse
|
17
|
Al-Azazi S, Singer A, Rabbani R, Lix LM. Combining population-based administrative health records and electronic medical records for disease surveillance. BMC Med Inform Decis Mak 2019; 19:120. [PMID: 31266516 PMCID: PMC6604278 DOI: 10.1186/s12911-019-0845-5] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/22/2019] [Accepted: 06/20/2019] [Indexed: 11/30/2022] Open
Abstract
BACKGROUND Administrative health records (AHRs) and electronic medical records (EMRs) are two key sources of population-based data for disease surveillance, but misclassification errors in the data can bias disease estimates. Methods that combine information from error-prone data sources can build on the strengths of AHRs and EMRs. We compared bias and error for four data-combining methods and applied them to estimate hypertension prevalence. METHODS Our study included rule-based OR and AND methods that identify disease cases from either or both data sources, respectively, rule-based sensitivity-specificity adjusted (RSSA) method that corrects for inaccuracies using a deterministic rule, and probabilistic-based sensitivity-specificity adjusted (PSSA) method that corrects for error using a statistical model. Computer simulation was used to estimate relative bias (RB) and mean square error (MSE) under varying conditions of population disease prevalence, correlation amongst data sources, and amount of misclassification error. AHRs and EMRs for Manitoba, Canada were used to estimate hypertension prevalence using validated case definitions and multiple disease markers. RESULTS The OR method had the lowest RB and MSE when population disease prevalence was 10%, and the RSSA method had the lowest RB and MSE when population prevalence increased to 20%. As the correlation between data sources increased, the OR method resulted in the lowest RB and MSE. Estimates of hypertension prevalence for AHRs and EMRs alone were 30.9% (95% CI: 30.6-31.2) and 24.9% (95% CI: 24.6-25.2), respectively. The estimates were 21.4% (95% CI: 21.1-21.7), for the AND method, 34.4% (95% CI: 34.1-34.8) for the OR method, 32.2% (95% CI: 31.8-32.6) for the RSSA method, and ranged from 34.3% (95% CI: 34.1-34.5) to 35.9% (95% CI, 35.7-36.1) for the PSSA method, depending on the statistical model. CONCLUSIONS The OR and AND methods are influenced by correlation amongst the data sources, while the RSSA method is dependent on the accuracy of prior sensitivity and specificity estimates. The PSSA method performed well when population prevalence was high and average correlations amongst disease markers was low. This study will guide researchers to select a data-combining method that best suits their data characteristics.
Collapse
Affiliation(s)
- Saeed Al-Azazi
- Department of Community Health Sciences, University of Manitoba, S113-750 Bannatyne Avenue, Winnipeg, MB R3E 0W3 Canada
- George & Fay Yee Centre for Healthcare Innovation, University of Manitoba, Winnipeg, MB Canada
| | - Alexander Singer
- Department of Family Medicine, University of Manitoba, Winnipeg, MB Canada
| | - Rasheda Rabbani
- Department of Community Health Sciences, University of Manitoba, S113-750 Bannatyne Avenue, Winnipeg, MB R3E 0W3 Canada
- George & Fay Yee Centre for Healthcare Innovation, University of Manitoba, Winnipeg, MB Canada
| | - Lisa M. Lix
- Department of Community Health Sciences, University of Manitoba, S113-750 Bannatyne Avenue, Winnipeg, MB R3E 0W3 Canada
- George & Fay Yee Centre for Healthcare Innovation, University of Manitoba, Winnipeg, MB Canada
| |
Collapse
|
18
|
Visualizing the Opioid Epidemic: Lessons Learned From a Dozen Years of Engaging Communities With Data. JOURNAL OF PUBLIC HEALTH MANAGEMENT AND PRACTICE 2019; 25:398-401. [DOI: 10.1097/phh.0000000000001037] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/27/2022]
|
19
|
|
20
|
Three Different Databases, Three Different Complication Rates for Knee and Hip Arthroplasty: Comparing the National Inpatient Sample, National Hospital Discharge Survey, and National Surgical Quality Improvement Program, 2006 to 2010. J Am Acad Orthop Surg 2019; 27:e568-e576. [PMID: 30461517 DOI: 10.5435/jaaos-d-17-00789] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 02/01/2023] Open
Abstract
BACKGROUND National databases are increasingly used to research complication rates, risk factors, and the role of comorbidities. Three commonly used databases are the Healthcare Cost and Utilization Program's National Inpatient Sample (NIS), the National Hospital Discharge Survey (NHDS), and the American College of Surgeons National Surgical Quality Improvement Program (NSQIP). Despite many publications, the accuracy of results from these databases remains unclear. METHODS We compared demographics and complication rates of primary total hip arthroplasty (THA) and primary total knee arthroplasty (TKA) across three national databases from 2006 to 2010. Using International Classification of Diseases, Ninth Revision, Clinical Modification and Current Procedural Terminology codes to identify cases, we calculated postoperative inpatient complication rates in all three databases and 30-day complication rates in the NSQIP. RESULTS We identified a total of 607,322 TKAs and 279,428 THAs. Overall complication rates varied greatly between the databases. For TKA, the overall complication rates were the highest in the NIS (17.3% [16.6 to 18.0]), followed by the NHDS (14.9% [14.0 to 15.8]), and then the NSQIP 30 days (10.20% [9.73 to 10.70]) and the NSQIP until discharge (7.34% [6.95 to 7.75]). Similarly, for THA, the NIS was the highest (24.09% [23.05 to 25.16]), and then the NHDS (21.5% [19.8 to 23.2]), followed by the NSQIP 30 days (12.00% [11.31 to 12.72]), and the NSQIP until discharge (9.25% [8.64 to 9.90]). Breakdown and comparison of individual adverse events further revealed different complication rates. CONCLUSION The estimated complication rates from THA and TKA depend on which data source is used because of differences in data collection and sampling methodology. Similar differences may exist in other publications that use such secondary data sources.
Collapse
|
21
|
Coppersmith NA, Sarkar IN, Chen ES. Quality Informatics: The Convergence of Healthcare Data, Analytics, and Clinical Excellence. Appl Clin Inform 2019; 10:272-277. [PMID: 31018233 DOI: 10.1055/s-0039-1685221] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/24/2022] Open
Affiliation(s)
- Nathan A Coppersmith
- Warren Alpert Medical School and Center for Biomedical Informatics, Brown University, Rhode Island, United States
| | - Indra Neil Sarkar
- Warren Alpert Medical School and Center for Biomedical Informatics, Brown University, Rhode Island, United States
| | - Elizabeth S Chen
- Warren Alpert Medical School and Center for Biomedical Informatics, Brown University, Rhode Island, United States
| |
Collapse
|
22
|
Abstract
Research on interoperability and information exchange between information technology systems touts the use of secondary data for a variety of purposes, including research, management, quality improvement, and accountability. However, many studies have pointed out that this is difficult to achieve in practice. Hence, this article aims to examine the causes for this by reporting an ethnographic study of the data work performed by medical records coders and birth certificate clerks working in a hospital system to uncover the practices of creating administrative data (e.g. secondary data). The article illustrates that clerks and coders use situated qualitative judgments of the accuracy and authority of different primary medical accounts. Coders and clerks also employ their understandings of the importance of different future uses of data as they make crucial decisions about how much discretion to exercise in producing accurate data and how much effort to put toward clarifying problematic medical data. These findings suggest that information technology systems designed for interoperability and secondary data also need to be designed in ways that support the qualculative practices of data workers in order to succeed, including making future uses of data clear to data workers and finding ways to minimize conflicting data before data workers encounter it.
Collapse
|
23
|
Wells SY, Morland LA, Torres EM, Kloezeman K, Mackintosh MA, Aarons GA. The Development of a Brief Version of the Posttraumatic Cognitions Inventory (PTCI-9). Assessment 2019; 26:193-208. [PMID: 28092974 DOI: 10.1177/1073191116685401] [Citation(s) in RCA: 25] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
Abstract
Negative posttraumatic cognitions lead to the development and maintenance of posttraumatic stress disorder symptoms. There is a need for a brief measure to assess these cognitions. Participants were administered the Posttraumatic Cognitions Inventory (PTCI) and measures of mental health symptomatology. These data were used to develop a brief version of the PTCI (PTCI-9) in 223 male and female veterans, which was then examined in a sample of 117 female civilians. Confirmatory factor analyses demonstrated an acceptable fit in both samples. The PTCI-9 total and subscale scores showed strong internal consistencies (Cronbach's αs = .80-.87) and strong correlations with the PTCI in veterans ( rs = .90-.96) and civilians ( rs = .91-.96). Measurement invariance testing demonstrated partial invariance between the two samples. The PTCI-9 significantly correlated with measures of PTSD, depression, and quality of life. These findings demonstrate that the PTCI-9 is a reliable and valid measure of posttraumatic cognitions that can reduce patient and provider burden.
Collapse
Affiliation(s)
- Stephanie Y Wells
- 1 San Diego VA Healthcare System, San Diego, CA, USA.,2 San Diego State University/University of California Joint Doctoral Program in Clinical Psychology.,3 Department of Psychiatry, University of California, San Diego, CA, USA
| | - Leslie A Morland
- 1 San Diego VA Healthcare System, San Diego, CA, USA.,3 Department of Psychiatry, University of California, San Diego, CA, USA
| | - Elisa M Torres
- 3 Department of Psychiatry, University of California, San Diego, CA, USA
| | - Karen Kloezeman
- 4 National Center for PTSD-Pacific Islands Division, Honolulu, HI, USA
| | | | - Gregory A Aarons
- 3 Department of Psychiatry, University of California, San Diego, CA, USA
| |
Collapse
|
24
|
Cordasco KM, Yuan AH, Danz MJ, Farmer MM, Jackson L, Yee EF, Washington DL. Guideline Adherence of Veterans Health Administration Primary Care for Abnormal Uterine Bleeding. Womens Health Issues 2019; 29:144-152. [PMID: 30723059 DOI: 10.1016/j.whi.2018.12.004] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/27/2018] [Revised: 12/18/2018] [Accepted: 12/21/2018] [Indexed: 11/28/2022]
Abstract
BACKGROUND Abnormal uterine bleeding (AUB) is common among primary care patients. We assessed the extent to which Veterans Health Administration (VA) primary care patients with AUB are receiving guideline-adherent primary care. METHODS We identified women with AUB presenting to primary care providers across four VA health care systems from June 2013 to September 2015. We performed a structured abstraction of electronic medical record data for 15 indicators of guideline-adherent AUB care. We determined whether documented care was guideline-adherent and compared adherence of care by primary care providers by VA Designated Women's Health Provider status and by volume of clinical encounters with women veterans. RESULTS Across 305 episodes of AUB, 53% of the care was guideline adherent. There was high adherence with documenting menopausal status (98%), ordering diagnostic studies and referrals for postmenopausal women (92%), and documenting bleeding patterns (87%). There was lower adherence with documenting whether there was active bleeding (55%), performing thyroid testing (47%), performing a pelvic examination (42%), ordering diagnostic studies and referrals in younger women with increased endometrial cancer risk (40%), assessing for pregnancy (32%), assessing for cervical motion, uterine, or adnexal tenderness in patients with intrauterine devices (30%), and assessing for elevated endometrial cancer risk (6%). There were no significant differences in overall guideline adherence between primary care providers who were, versus were not, VA Designated Women's Health Providers, or by provider volume of encounters with women veterans. CONCLUSIONS VA primary care has high guideline adherence when caring for postmenopausal women with AUB. Quality improvement and educational initiatives are needed to improve primary care for AUB in younger women veterans.
Collapse
Affiliation(s)
- Kristina M Cordasco
- VA Health Services Research & Development (HSR&D), Center for the Study of Healthcare Innovation, Implementation and Policy, VA Greater Los Angeles Healthcare System, Los Angeles, California; Department of Medicine, University of California Los Angeles (UCLA) Geffen School of Medicine, Los Angeles, California.
| | - Anita H Yuan
- VA Health Services Research & Development (HSR&D), Center for the Study of Healthcare Innovation, Implementation and Policy, VA Greater Los Angeles Healthcare System, Los Angeles, California
| | - Marjorie J Danz
- VA Health Services Research & Development (HSR&D), Center for the Study of Healthcare Innovation, Implementation and Policy, VA Greater Los Angeles Healthcare System, Los Angeles, California; RAND Corporation, Santa Monica, California
| | - Melissa M Farmer
- VA Health Services Research & Development (HSR&D), Center for the Study of Healthcare Innovation, Implementation and Policy, VA Greater Los Angeles Healthcare System, Los Angeles, California
| | - LaShawnta Jackson
- VA Health Services Research & Development (HSR&D), Center for the Study of Healthcare Innovation, Implementation and Policy, VA Greater Los Angeles Healthcare System, Los Angeles, California
| | - Ellen F Yee
- New Mexico VA Healthcare System, Albuquerque, New Mexico; Department of Medicine, University of New Mexico, Albuquerque, New Mexico
| | - Donna L Washington
- VA Health Services Research & Development (HSR&D), Center for the Study of Healthcare Innovation, Implementation and Policy, VA Greater Los Angeles Healthcare System, Los Angeles, California; Department of Medicine, University of California Los Angeles (UCLA) Geffen School of Medicine, Los Angeles, California
| |
Collapse
|
25
|
Smith PK, Amster A. An Organization-Specific and Modifiable Inpatient Safety Composite Measure. Jt Comm J Qual Patient Saf 2019; 45:304-314. [PMID: 30642774 DOI: 10.1016/j.jcjq.2018.11.005] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/12/2017] [Revised: 11/21/2018] [Accepted: 11/21/2018] [Indexed: 11/16/2022]
Abstract
In early 2013, seeking to apply the principles of value-based purchasing to all Kaiser Permanente hospitals as part of an existing organizationwide value-based performance incentive plan, Kaiser Permanente developed an inpatient safety composite measure that tracks hospital-level performance improvement related to 10 key inpatient safety events. The elements of the composite are weighted equally, and the tool draws on scoring methodologies used by the National Committee for Quality Assurance and the Centers for Medicare & Medicaid Services Hospital Inpatient Value-Based Purchasing Program. Two years after implementation of the composite measure, hospitals experienced improvement across 9 of the 10 adverse events assessed, though only one improvement achieved statistical significance. The measure successfully distinguishes four levels of improvement and is broadly applicable to hospitals and hospital systems.
Collapse
|
26
|
Diallo AO, Krishnaswamy A, Shapira SK, Oster ME, George MG, Adams JC, Walker ER, Weiss P, Ali MK, Book W. Detecting moderate or complex congenital heart defects in adults from an electronic health records system. J Am Med Inform Assoc 2018; 25:1634-1642. [DOI: 10.1093/jamia/ocy127] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/22/2018] [Accepted: 09/10/2018] [Indexed: 11/13/2022] Open
Abstract
Abstract
Background
The prevalence of moderate or complex (moderate-complex) congenital heart defects (CHDs) among adults is increasing due to improved survival, but many patients experience lapses in specialty care or their CHDs are undocumented in the medical system. There is, to date, no efficient approach to identify this population.
Objective
To develop and assess the performance of a risk score to identify adults aged 20-60 years with undocumented specific moderate-complex CHDs from electronic health records (EHR).
Methods
We used a case-control study (596 adults with specific moderate-complex CHDs and 2384 controls). We extracted age, race/ethnicity, electrocardiogram (EKG), and blood tests from routine outpatient visits (1/2009 through 12/2012). We used multivariable logistic regression models and a split-sample (4: 1 ratio) approach to develop and internally validate the risk score, respectively. We generated receiver operating characteristic (ROC) c-statistics and Brier scores to assess the ability of models to predict the presence of specific moderate-complex CHDs.
Results
Out of six models, the non-blood biomarker model that included age, sex, and EKG parameters offered a high ROC c-statistic of 0.96 [95% confidence interval: 0.95, 0.97] and low Brier score (0.05) relative to the other models. The adult moderate-complex congenital heart defect risk score demonstrated good accuracy with 96.4% sensitivity and 80.0% specificity at a threshold score of 10.
Conclusions
A simple risk score based on age, sex, and EKG parameters offers early proof of concept and may help accurately identify adults with specific moderate-complex CHDs from routine EHR systems who may benefit from specialty care.
Collapse
Affiliation(s)
- Alpha Oumar Diallo
- Department of Epidemiology, Rollins School of Public Health, Emory University, Atlanta, GA, USA
| | - Asha Krishnaswamy
- National Center on Birth Defects and Developmental Disabilities, Centers for Disease Control and Prevention, Atlanta, GA, USA
| | - Stuart K Shapira
- National Center on Birth Defects and Developmental Disabilities, Centers for Disease Control and Prevention, Atlanta, GA, USA
| | - Matthew E Oster
- Department of Epidemiology, Rollins School of Public Health, Emory University, Atlanta, GA, USA
- National Center on Birth Defects and Developmental Disabilities, Centers for Disease Control and Prevention, Atlanta, GA, USA
- Sibley Heart Center Cardiology, Children’s Healthcare of Atlanta, Atlanta, GA, USA
- Emory University School of Medicine, Atlanta, GA, USA
| | - Mary G George
- National Center for Chronic Disease Prevention and Health Promotion, Centers for Disease Control and Prevention, Atlanta, GA, USA
| | - Jenna C Adams
- Emory University School of Medicine, Atlanta, GA, USA
| | | | - Paul Weiss
- Department of Biostatistics and Bioinformatics, Rollins School of Public Health, Emory University, Atlanta, GA, USA
| | - Mohammed K Ali
- Department of Epidemiology, Rollins School of Public Health, Emory University, Atlanta, GA, USA
- Hubert Department of Global Health, Rollins School of Public Health, Emory University, Atlanta, GA, USA
- Department of Family and Preventive Medicine, School of Medicine, Emory University, Atlanta, GA, USA
| | - Wendy Book
- Emory University School of Medicine, Atlanta, GA, USA
| |
Collapse
|
27
|
Anhang Price R, Sloss EM, Cefalu M, Farmer CM, Hussey PS. Comparing Quality of Care in Veterans Affairs and Non-Veterans Affairs Settings. J Gen Intern Med 2018; 33:1631-1638. [PMID: 29696561 PMCID: PMC6153237 DOI: 10.1007/s11606-018-4433-7] [Citation(s) in RCA: 68] [Impact Index Per Article: 11.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/09/2017] [Revised: 12/18/2017] [Accepted: 03/30/2018] [Indexed: 11/28/2022]
Abstract
BACKGROUND Congress, veterans' groups, and the press have expressed concerns that access to care and quality of care in Department of Veterans Affairs (VA) settings are inferior to access and quality in non-VA settings. OBJECTIVE To assess quality of outpatient and inpatient care in VA at the national level and facility level and to compare performance between VA and non-VA settings using recent performance measure data. MAIN MEASURES We assessed Patient Safety Indicators (PSIs), 30-day risk-standardized mortality and readmission measures, and ORYX measures for inpatient safety and effectiveness; Healthcare Effectiveness Data and Information Set (HEDIS®) measures for outpatient effectiveness; and Consumer Assessment of Healthcare Providers and Systems Hospital Survey (HCAHPS) and Survey of Healthcare Experiences of Patients (SHEP) survey measures for inpatient patient-centeredness. For inpatient care, we used propensity score matching to identify a subset of non-VA hospitals that were comparable to VA hospitals. KEY RESULTS VA hospitals performed on average the same as or significantly better than non-VA hospitals on all six measures of inpatient safety, all three inpatient mortality measures, and 12 inpatient effectiveness measures, but significantly worse than non-VA hospitals on three readmission measures and two effectiveness measures. The performance of VA facilities was significantly better than commercial HMOs and Medicaid HMOs for all 16 outpatient effectiveness measures and for Medicare HMOs, it was significantly better for 14 measures and did not differ for two measures. High variation across VA facilities in the performance of some quality measures was observed, although variation was even greater among non-VA facilities. CONCLUSIONS The VA system performed similarly or better than the non-VA system on most of the nationally recognized measures of inpatient and outpatient care quality, but high variation across VA facilities indicates a need for targeted quality improvement.
Collapse
|
28
|
LaBonte CT, Payne P, Rollow W, Smith MW, Nissar A, Holtz P, Sessums LL. Performance on Electronic Clinical Quality Measures in the Comprehensive Primary Care Initiative. Am J Med Qual 2018; 34:119-126. [PMID: 30132334 DOI: 10.1177/1062860618794868] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
Abstract
Electronic clinical quality measures (eCQMs) that capture data from electronic health records promise accurate and timely measurement, but their use has been limited in payment. The Comprehensive Primary Care initiative sponsored by the Centers for Medicare & Medicaid Services used eCQMs as part of a shared savings incentive. To assess performance, the authors developed benchmarks for 11 measures and compared performance of initiative and benchmark practices. Initiative practices outperformed benchmark practices on 8 measures in 2015 (between 6.3 and 17.7 percentage points) and 9 measures in 2016 (between 1.7 and 20 percentage points). Initiative practices improved significantly on 7 measures from 2015 to 2016 (between 3.3 and 8.6 percentage points). For 3 measures, the improvement was greater than benchmark practices that reported the same measures in a 2-year period (between 1 and 8.9 percentage points). The authors conclude that eCQMs can be used for payment.
Collapse
Affiliation(s)
| | - Perry Payne
- 1 Centers for Medicare & Medicaid Services, Baltimore, MD
| | | | | | | | - Patrice Holtz
- 1 Centers for Medicare & Medicaid Services, Baltimore, MD
| | | |
Collapse
|
29
|
Kumar SV, Bangar S, Neumann A, Kookal KK, Yansane A, Tokede O, Obadan-Udoh E, Mertz E, Simmons K, Even J, Mullins J, White J, Kalenderian E, Walji M. Assessing the validity of existing dental sealant quality measures. J Am Dent Assoc 2018; 149:756-764.e1. [PMID: 29980245 DOI: 10.1016/j.adaj.2018.05.001] [Citation(s) in RCA: 11] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/08/2017] [Revised: 04/13/2018] [Accepted: 05/02/2018] [Indexed: 02/07/2023]
Abstract
BACKGROUND Although sealants are highly effective in preventing caries in children, placement rates continue to be low. The authors' goals were to implement and assess the performance of 2 existing sealant quality measures against a manual audit of charts at 4 dental institutions and to identify measurement gaps that may be filled by using data from electronic health records. METHODS The authors evaluated the performance of 2 quality measures designed for claims-based data: the Dental Quality Alliance (DQA) sealant measure, which includes patients at risk of developing elevated caries, and the Oregon Health Authority (OHA) sealant measure (irrespective of caries risk). The authors adapted and validated these measures at 4 sites: 3 dental schools and 1 large dental accountable care organization. RESULTS The overall modified DQA and modified OHA measure scores in the 6- through 9-year-old age group were 37.0% and 31.6% and in the 10- through 14-year-old age group were 15.8% and 6.6%, respectively. Results from the manual review of charts showed that 67.6% of children who did not receive sealants did not have any teeth to seal because their molars had not yet erupted, had been extracted, had been sealed previously, or had existing caries or restorations. CONCLUSIONS Both the DQA and OHA measures, which rely mainly on Current Dental Terminology procedure codes, led to underestimation of the care delivered from a practice perspective. Future sealant quality measures should exclude patients whose teeth cannot be sealed. PRACTICAL IMPLICATIONS This study's results support the suitability of using electronic health record data for assessing the quality of oral health care, particularly for measuring sealant placement in children.
Collapse
|
30
|
Mapping the Gaps: Gender Differences in Preventive Cardiovascular Care among Managed Care Members in Four Metropolitan Areas. Womens Health Issues 2018; 28:446-455. [PMID: 29929865 DOI: 10.1016/j.whi.2018.04.008] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/14/2017] [Revised: 03/21/2018] [Accepted: 04/20/2018] [Indexed: 11/21/2022]
Abstract
BACKGROUND Prior research documents gender gaps in cardiovascular risk management, with women receiving poorer quality routine care on average, even in managed care systems. Although population health management tools and quality improvement efforts have led to better overall care quality and narrowing of racial/ethnic gaps for a variety of measures, we sought to quantify persistent gender gaps in cardiovascular risk management and to assess the performance of routinely used commercial population health management tools in helping systems narrow gender gaps. METHODS Using 2013 through 2014 claims and enrollment data from more than 1 million members of a large national health insurance plan, we assessed performance on seven evidence-based quality measures for the management of coronary artery disease and diabetes mellitus, a cardiac risk factor, across and within four metropolitan areas. We used logistic regression to adjust for region, demographics, and risk factors commonly tracked in population health management tools. FINDINGS Low-density lipoprotein (LDL) cholesterol control (LDL < 100 mg/dL) rates were 5 and 15 percentage points lower for women than men with diabetes mellitus (p < .0001), and coronary artery disease (p < .0001), respectively. Adjusted analyses showed women were more likely to have gaps in LDL control, with an odds ratio of 1.31 (95% confidence interval, 1.27-1.38) in diabetes mellitus and 1.88 (95% confidence interval, 1.65-2.10) in coronary artery disease. CONCLUSIONS Given our findings that gender gaps persist across both clinical and geographic variation, we identified additional steps health plans can take to reduce disparities. For measures where gaps have been consistently identified, we recommend that gender-stratified quality reporting and analysis be used to complement widely used algorithms to identify individuals with unmet needs for referral to population health and wellness behavior support programs.
Collapse
|
31
|
Cordasco KM, Katzburg JR, Katon JG, Zephyrin LC, Chrystal JG, Yano EM. Care coordination for pregnant veterans: VA’s Maternity Care Coordinator Telephone Care Program. Transl Behav Med 2018; 8:419-428. [DOI: 10.1093/tbm/ibx081] [Citation(s) in RCA: 14] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/12/2022] Open
Affiliation(s)
- Kristina M Cordasco
- VA Center for The Study of Healthcare Innovation, Implementation and Policy (CSHIIP), North Hills, CA, USA
- VA Greater Los Angeles Healthcare System, Los Angeles, CA, USA
- Department of Medicine, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA, USA
| | - Judith R Katzburg
- VA Center for The Study of Healthcare Innovation, Implementation and Policy (CSHIIP), North Hills, CA, USA
- VA Veterans Emergency Management Evaluation Center (VEMEC), North Hills, CA, USA
| | - Jodie G Katon
- Health Services Research and Development Center of Innovation for Veteran-Centered and Value Driven Care, Department of Veterans Affairs Puget Sound Health Care System, Seattle, WA, USA
- Department of Health Services, University of Washington School of Public Health, Seattle, WA, USA
| | - Laurie C Zephyrin
- Women’s Health Services, Office of Patient Care Services, Veterans Health Administration, Washington, DC, USA
- VA New York Harbor Healthcare System, New York, NY, USA
- Department of Obstetrics and Gynecology, New York University Langone School of Medicine, New York, NY, USA
| | - Joya G Chrystal
- VA Center for The Study of Healthcare Innovation, Implementation and Policy (CSHIIP), North Hills, CA, USA
| | - Elizabeth M Yano
- VA Center for The Study of Healthcare Innovation, Implementation and Policy (CSHIIP), North Hills, CA, USA
- Department of Health Policy & Management, UCLA Fielding School of Public Health, Los Angeles, CA, USA
| |
Collapse
|
32
|
Hernandez-Boussard T, Kourdis PD, Seto T, Ferrari M, Blayney DW, Rubin D, Brooks JD. Mining Electronic Health Records to Extract Patient-Centered Outcomes Following Prostate Cancer Treatment. AMIA ... ANNUAL SYMPOSIUM PROCEEDINGS. AMIA SYMPOSIUM 2018; 2017:876-882. [PMID: 29854154 PMCID: PMC5977629] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Subscribe] [Scholar Register] [Indexed: 06/08/2023]
Abstract
The clinical, granular data in electronic health record (EHR) systems provide opportunities to improve patient care using informatics retrieval methods. However, it is well known that many methodological obstacles exist in accessing data within EHRs. In particular, clinical notes routinely stored in EHR are composed from narrative, highly unstructured and heterogeneous biomedical text. This inherent complexity hinders the ability to perform automated large-scale medical knowledge extraction tasks without the use of computational linguistics methods. The aim of this work was to develop and validate a Natural Language Processing (NLP) pipeline to detect important patient-centered outcomes (PCOs) as interpreted and documented by clinicians in their dictated notes for male patients receiving treatment for localized prostate cancer at an academic medical center.
Collapse
Affiliation(s)
| | | | - Tina Seto
- Stanford University, School of Medicine, Stanford, CA
| | | | | | - Daniel Rubin
- Stanford University, School of Medicine, Stanford, CA
| | | |
Collapse
|
33
|
Utility of Electronic Medical Records in Community Rheumatology Practice for Assessing Quality of Care Indicators for Gout. J Clin Rheumatol 2018; 24:75-79. [DOI: 10.1097/rhu.0000000000000621] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/25/2022]
|
34
|
Laws MB, Michaud J, Shield R, McQuade W, Wilson IB. Comparison of Electronic Health Record-Based and Claims-Based Diabetes Care Quality Measures: Causes of Discrepancies. Health Serv Res 2017; 53 Suppl 1:2988-3006. [PMID: 29282723 DOI: 10.1111/1475-6773.12819] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.1] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/01/2022] Open
Abstract
OBJECTIVE To investigate magnitude and sources of discrepancy in quality metrics using claims versus electronic health record (EHR) data. STUDY DESIGN Assessment of proportions of HbA1c and LDL testing for people ascertained as diabetic from the respective sources. Qualitative interviews and review of EHRs of discrepant cases. DATA COLLECTION/EXTRACTION Claims submitted to Rhode Island Medicaid by three practice sites in 2013; program-coded EHR extraction; manual review of selected EHRs. PRINCIPAL FINDINGS Of 21,030 adult Medicaid beneficiaries attributed to a primary care patient at a site by claims or EHR data, concordance on assignment ranged from 0.30 to 0.41. Of patients with concordant assignment, the ratio of patients ascertained as diabetic by EHR versus claims ranged from 1.06 to 1.14. For patients with concordant assignment and diagnosis, the ratio based on EHR versus claims ranged from 1.08 to 18.34 for HbA1c testing, and from 1.29 to 14.18 for lipid testing. Manual record review of 264 patients discrepant on diagnosis or testing identified problems such as misuse of ICD-9 codes, failure to submit claims, and others. CONCLUSIONS Claims data underestimate performance on these metrics compared to EHR documentation, by varying amounts. Use of claims data for these metrics is problematic.
Collapse
Affiliation(s)
- Michael Barton Laws
- Department of Health Services, Policy and Practice, Brown University School of Public Health, Providence, RI
| | - Joanne Michaud
- Department of Health Services, Policy and Practice, Brown University School of Public Health, Providence, RI
| | - Renee Shield
- Department of Health Services, Policy and Practice, Brown University School of Public Health, Providence, RI
| | - William McQuade
- Center for Primary Care and Prevention, Warren Alpert Medical School, and Rhode Island Executive Office of Health and Human Services, Warwick, RI
| | - Ira B Wilson
- Department of Health Services, Policy and Practice, Brown University School of Public Health, Providence, RI
| |
Collapse
|
35
|
Nissen F, Quint JK, Wilkinson S, Mullerova H, Smeeth L, Douglas IJ. Validation of asthma recording in electronic health records: a systematic review. Clin Epidemiol 2017; 9:643-656. [PMID: 29238227 PMCID: PMC5716672 DOI: 10.2147/clep.s143718] [Citation(s) in RCA: 25] [Impact Index Per Article: 3.6] [Reference Citation Analysis] [Abstract] [Key Words] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/23/2022] Open
Abstract
Objective To describe the methods used to validate asthma diagnoses in electronic health records and summarize the results of the validation studies. Background Electronic health records are increasingly being used for research on asthma to inform health services and health policy. Validation of the recording of asthma diagnoses in electronic health records is essential to use these databases for credible epidemiological asthma research. Methods We searched EMBASE and MEDLINE databases for studies that validated asthma diagnoses detected in electronic health records up to October 2016. Two reviewers independently assessed the full text against the predetermined inclusion criteria. Key data including author, year, data source, case definitions, reference standard, and validation statistics (including sensitivity, specificity, positive predictive value [PPV], and negative predictive value [NPV]) were summarized in two tables. Results Thirteen studies met the inclusion criteria. Most studies demonstrated a high validity using at least one case definition (PPV >80%). Ten studies used a manual validation as the reference standard; each had at least one case definition with a PPV of at least 63%, up to 100%. We also found two studies using a second independent database to validate asthma diagnoses. The PPVs of the best performing case definitions ranged from 46% to 58%. We found one study which used a questionnaire as the reference standard to validate a database case definition; the PPV of the case definition algorithm in this study was 89%. Conclusion Attaining high PPVs (>80%) is possible using each of the discussed validation methods. Identifying asthma cases in electronic health records is possible with high sensitivity, specificity or PPV, by combining multiple data sources, or by focusing on specific test measures. Studies testing a range of case definitions show wide variation in the validity of each definition, suggesting this may be important for obtaining asthma definitions with optimal validity.
Collapse
Affiliation(s)
- Francis Nissen
- Department of Non-Communicable Disease Epidemiology, London School of Hygiene and Tropical Medicine, London, UK
| | | | - Samantha Wilkinson
- Department of Non-Communicable Disease Epidemiology, London School of Hygiene and Tropical Medicine, London, UK
| | | | - Liam Smeeth
- Department of Non-Communicable Disease Epidemiology, London School of Hygiene and Tropical Medicine, London, UK
| | - Ian J Douglas
- Department of Non-Communicable Disease Epidemiology, London School of Hygiene and Tropical Medicine, London, UK
| |
Collapse
|
36
|
Bowen ME, Bhat D, Fish J, Moran B, Howell-Stampley T, Kirk L, Persell SD, Halm EA. Improving Performance on Preventive Health Quality Measures Using Clinical Decision Support to Capture Care Done Elsewhere and Patient Exceptions. Am J Med Qual 2017; 33:237-245. [PMID: 29034685 DOI: 10.1177/1062860617732830] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/21/2022]
Abstract
Preventive services required for performance measurement often are completed in outside health systems and not captured in electronic medical records (EMRs). A before-after study was conducted to examine the ability of clinical decision support (CDS) to improve performance on preventive quality measures, capture clinician-reported services completed elsewhere, and patient/medical exceptions and to describe their impact on quality measurement. CDS improved performance on colorectal cancer screening, osteoporosis screening, and pneumococcal vaccination measures ( P < .05) but not breast or cervical cancer screening. CDS captured clinician-reported services completed elsewhere (2% to 10%) and patient/medical exceptions (<3%). Compared to measures using only within-system data, including services completed elsewhere in the numerator improved performance: pneumococcal vaccine (73% vs 82%); breast (69% vs 75%), colorectal (58% vs 70%), and cervical cancer (53% vs 62%); and osteoporosis (72% vs 75%) screening ( P < .05). Visit-based CDS can capture clinician-reported preventive services, and accounting for services completed elsewhere improves performance on quality measures.
Collapse
Affiliation(s)
- Michael E Bowen
- 1 University of Texas Southwestern Medical Center, Dallas, TX
| | - Deepa Bhat
- 1 University of Texas Southwestern Medical Center, Dallas, TX
| | - Jason Fish
- 1 University of Texas Southwestern Medical Center, Dallas, TX
| | - Brett Moran
- 1 University of Texas Southwestern Medical Center, Dallas, TX.,2 Parkland Health and Hospital System, Dallas, TX
| | | | - Lynne Kirk
- 1 University of Texas Southwestern Medical Center, Dallas, TX
| | | | - Ethan A Halm
- 1 University of Texas Southwestern Medical Center, Dallas, TX
| |
Collapse
|
37
|
Atiemo K, Skaro A, Maddur H, Zhao L, Montag S, VanWagner L, Goel S, Kho A, Ho B, Kang R, Holl JL, Abecassis MM, Levitsky J, Ladner DP. Mortality Risk Factors Among Patients With Cirrhosis and a Low Model for End-Stage Liver Disease Sodium Score (≤15): An Analysis of Liver Transplant Allocation Policy Using Aggregated Electronic Health Record Data. Am J Transplant 2017; 17:2410-2419. [PMID: 28226199 PMCID: PMC5769449 DOI: 10.1111/ajt.14239] [Citation(s) in RCA: 35] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/20/2016] [Revised: 02/07/2017] [Accepted: 02/12/2017] [Indexed: 01/25/2023]
Abstract
Although the Model for End-Stage Liver Disease sodium (MELD Na) score is now used for liver transplant allocation in the United States, mortality prediction may be underestimated by the score. Using aggregated electronic health record data from 7834 adult patients with cirrhosis, we determined whether the cause of cirrhosis or cirrhosis complications was associated with an increased risk of death among patients with a MELD Na score ≤15 and whether patients with the greatest risk of death could benefit from liver transplantation (LT). Over median follow-up of 2.3 years, 3715 patients had a maximum MELD Na score ≤15. Overall, 3.4% were waitlisted for LT. Severe hypoalbuminemia, hepatorenal syndrome, and hepatic hydrothorax conferred the greatest risk of death independent of MELD Na score with 1-year predicted mortality >14%. Approximately 10% possessed these risk factors. Of these high-risk patients, only 4% were waitlisted for LT, despite no difference in nonliver comorbidities between waitlisted patients and those not listed. In addition, risk factors for death among waitlisted patients were the same as those for patients not waitlisted, although the effect of malnutrition was significantly greater for waitlisted patients (hazard ratio 8.65 [95% CI 2.57-29.11] vs. 1.47 [95% CI 1.08-1.98]). Using the MELD Na score for allocation may continue to limit access to LT.
Collapse
Affiliation(s)
- K Atiemo
- Northwestern University Transplant Research Collaborative (NUTORC), Comprehensive Transplant Center (CTC), Feinberg School of Medicine, Chicago, IL
| | - A Skaro
- Department of Transplantation, London Health Sciences Centre, Western University, London, Ontario, Canada
| | - H Maddur
- Northwestern University Transplant Research Collaborative (NUTORC), Comprehensive Transplant Center (CTC), Feinberg School of Medicine, Chicago, IL
- Division of Hepatology, Department of Medicine, Chicago, IL
| | - L Zhao
- Northwestern University Transplant Research Collaborative (NUTORC), Comprehensive Transplant Center (CTC), Feinberg School of Medicine, Chicago, IL
- Department of Preventive Medicine, Feinberg School of Medicine, Chicago, IL
| | - S Montag
- Northwestern University Transplant Research Collaborative (NUTORC), Comprehensive Transplant Center (CTC), Feinberg School of Medicine, Chicago, IL
- Department of Preventive Medicine, Feinberg School of Medicine, Chicago, IL
| | - L VanWagner
- Northwestern University Transplant Research Collaborative (NUTORC), Comprehensive Transplant Center (CTC), Feinberg School of Medicine, Chicago, IL
- Division of Hepatology, Department of Medicine, Chicago, IL
- Department of Preventive Medicine, Feinberg School of Medicine, Chicago, IL
| | - S Goel
- Center for Health Information Partnerships, Institute for Public Health and Medicine, Chicago, IL
| | - A Kho
- Center for Health Information Partnerships, Institute for Public Health and Medicine, Chicago, IL
| | - B Ho
- Northwestern University Transplant Research Collaborative (NUTORC), Comprehensive Transplant Center (CTC), Feinberg School of Medicine, Chicago, IL
| | - R Kang
- Northwestern University Transplant Research Collaborative (NUTORC), Comprehensive Transplant Center (CTC), Feinberg School of Medicine, Chicago, IL
- Center for Healthcare Studies, Institute for Public Health and Medicine, Chicago, IL
| | - J L Holl
- Northwestern University Transplant Research Collaborative (NUTORC), Comprehensive Transplant Center (CTC), Feinberg School of Medicine, Chicago, IL
- Center for Healthcare Studies, Institute for Public Health and Medicine, Chicago, IL
- Department of Pediatrics, Feinberg School of Medicine, Chicago, IL
| | - M M Abecassis
- Northwestern University Transplant Research Collaborative (NUTORC), Comprehensive Transplant Center (CTC), Feinberg School of Medicine, Chicago, IL
| | - J Levitsky
- Northwestern University Transplant Research Collaborative (NUTORC), Comprehensive Transplant Center (CTC), Feinberg School of Medicine, Chicago, IL
- Division of Hepatology, Department of Medicine, Chicago, IL
| | - D P Ladner
- Northwestern University Transplant Research Collaborative (NUTORC), Comprehensive Transplant Center (CTC), Feinberg School of Medicine, Chicago, IL
- Center for Healthcare Studies, Institute for Public Health and Medicine, Chicago, IL
| |
Collapse
|
38
|
Schmit C, Sunshine G, Pepin D, Ramanathan T, Menon A, Penn M. Transitioning From Paper to Digital: State Statutory and Regulatory Frameworks for Health Information Technology. Public Health Rep 2017; 132:585-592. [PMID: 28813620 PMCID: PMC5593240 DOI: 10.1177/0033354917722994] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/17/2022] Open
Abstract
OBJECTIVES In all health system sectors, electronic health information (EHI) is created, used, released, and reused. We examined states' efforts to address EHI uses in law to provide an understanding of the EHI legal environment. METHODS Attorney researchers used WestlawNext to search for EHI-related statutes and regulations of the US states, US territories, and the District of Columbia in effect as of January 2014. The researchers independently catalogued provisions by the EHI use described in the law. Researchers resolved discrepancies through peer review meetings and recorded the consensus codes for each law. RESULTS This study identified 2364 EHI-related laws representing 49 EHI uses in 54 jurisdictions. A total of 18 EHI uses were regulated by ≥10 jurisdictions. More than 750 laws addressed 2 or more EHI uses. Jurisdictions varied by the number of EHI laws in effect, with a mean of 44 laws. Texas had the most EHI laws (n = 145). Hawaii and South Carolina had the fewest (n = 14 each). CONCLUSIONS The EHI legal landscape is complex. The large quantity and diversity of laws complicate legal analysis, likely delay implementation of public health solutions, and might be detrimental to the development of emerging health information technology. Research is needed to understand the effect of EHI-related laws.
Collapse
Affiliation(s)
- Cason Schmit
- Chenega Professional and Technical Services, LLC for Public Health Law Program, Office for State, Tribal, Local and Territorial Support, Centers for Disease Control and Prevention, Atlanta, GA, USA
- Department of Health Policy & Management, Texas A&M University School of Public Health, College Station, TX, USA
| | - Gregory Sunshine
- Chenega Professional and Technical Services, LLC for Public Health Law Program, Office for State, Tribal, Local and Territorial Support, Centers for Disease Control and Prevention, Atlanta, GA, USA
| | - Dawn Pepin
- Chenega Professional and Technical Services, LLC for Public Health Law Program, Office for State, Tribal, Local and Territorial Support, Centers for Disease Control and Prevention, Atlanta, GA, USA
| | - Tara Ramanathan
- Public Health Law Program, Office for State, Tribal, Local and Territorial Support, Centers for Disease Control and Prevention, Atlanta, GA, USA
| | - Akshara Menon
- Chenega Professional and Technical Services, LLC for Public Health Law Program, Office for State, Tribal, Local and Territorial Support, Centers for Disease Control and Prevention, Atlanta, GA, USA
| | - Matthew Penn
- Public Health Law Program, Office for State, Tribal, Local and Territorial Support, Centers for Disease Control and Prevention, Atlanta, GA, USA
| |
Collapse
|
39
|
Neumann A, Kalenderian E, Ramoni R, Yansane A, Tokede B, Etolue J, Vaderhobli R, Simmons K, Even J, Mullins J, Kumar S, Bangar S, Kookal K, White J, Walji M. Evaluating quality of dental care among patients with diabetes: Adaptation and testing of a dental quality measure in electronic health records. J Am Dent Assoc 2017. [PMID: 28624074 DOI: 10.1016/j.adaj.2017.04.017] [Citation(s) in RCA: 17] [Impact Index Per Article: 2.4] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/18/2022]
Abstract
BACKGROUND Patients with diabetes are at increased risk of developing oral complications, and annual dental examinations are an endorsed preventive strategy. The authors evaluated the feasibility and validity of implementing an automated electronic health record (EHR)-based dental quality measure to determine whether patients with diabetes received such evaluations. METHODS The authors selected a Dental Quality Alliance measure developed for claims data and adapted the specifications for EHRs. Automated queries identified patients with diabetes across 4 dental institutions, and the authors manually reviewed a subsample of charts to evaluate query performance. After assessing the initial EHR measure, the authors defined and tested a revised EHR measure to capture better the oral care received by patients with diabetes. RESULTS In the initial and revised measures, the authors used EHR automated queries to identify 12,960 and 13,221 patients with diabetes, respectively, in the reporting year. Variations in the measure scores across sites were greater with the initial measure (range, 36.4-71.3%) than with the revised measure (range, 78.8-88.1%). The automated query performed well (93% or higher) for sensitivity, specificity, and positive and negative predictive values for both measures. CONCLUSIONS The results suggest that an automated EHR-based query can be used successfully to measure the quality of oral health care delivered to patients with diabetes. The authors also found that using the rich data available in EHRs may help estimate the quality of care better than can relying on claims data. PRACTICAL IMPLICATIONS Detailed clinical patient-level data in dental EHRs may be useful to dentists in evaluating the quality of dental care provided to patients with diabetes.
Collapse
|
40
|
Tamang SR, Hernandez-Boussard T, Ross EG, Gaskin G, Patel MI, Shah NH. Enhanced Quality Measurement Event Detection: An Application to Physician Reporting. EGEMS 2017; 5:5. [PMID: 29881731 PMCID: PMC5983066 DOI: 10.13063/2327-9214.1270] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 11/24/2022]
Abstract
The wide-scale adoption of electronic health records (EHR)s has increased the availability of routinely collected clinical data in electronic form that can be used to improve the reporting of quality of care. However, the bulk of information in the EHR is in unstructured form (e.g., free-text clinical notes) and not amenable to automated reporting. Traditional methods are based on structured diagnostic and billing data that provide efficient, but inaccurate or incomplete summaries of actual or relevant care processes and patient outcomes. To assess the feasibility and benefit of implementing enhanced EHR- based physician quality measurement and reporting, which includes the analysis of unstructured free- text clinical notes, we conducted a retrospective study to compare traditional and enhanced approaches for reporting ten physician quality measures from multiple National Quality Strategy domains. We found that our enhanced approach enabled the calculation of five Physician Quality and Performance System measures not measureable in billing or diagnostic codes and resulted in over a five-fold increase in event at an average precision of 88 percent (95 percent CI: 83–93 percent). Our work suggests that enhanced EHR-based quality measurement can increase event detection for establishing value-based payment arrangements and can expedite quality reporting for physician practices, which are increasingly burdened by the process of manual chart review for quality reporting.
Collapse
|
41
|
Nissen F, Quint JK, Wilkinson S, Mullerova H, Smeeth L, Douglas IJ. Validation of asthma recording in electronic health records: protocol for a systematic review. BMJ Open 2017; 7:e014694. [PMID: 28554919 PMCID: PMC5729974 DOI: 10.1136/bmjopen-2016-014694] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 11/03/2022] Open
Abstract
BACKGROUND Asthma is a common, heterogeneous disease with significant morbidity and mortality worldwide. It can be difficult to define in epidemiological studies using electronic health records as the diagnosis is based on non-specific respiratory symptoms and spirometry, neither of which are routinely registered. Electronic health records can nonetheless be valuable to study the epidemiology, management, healthcare use and control of asthma. For health databases to be useful sources of information, asthma diagnoses should ideally be validated. The primary objectives are to provide an overview of the methods used to validate asthma diagnoses in electronic health records and summarise the results of the validation studies. METHODS EMBASE and MEDLINE will be systematically searched for appropriate search terms. The searches will cover all studies in these databases up to October 2016 with no start date and will yield studies that have validated algorithms or codes for the diagnosis of asthma in electronic health records. At least one test validation measure (sensitivity, specificity, positive predictive value, negative predictive value or other) is necessary for inclusion. In addition, we require the validated algorithms to be compared with an external golden standard, such as a manual review, a questionnaire or an independent second database. We will summarise key data including author, year of publication, country, time period, date, data source, population, case characteristics, clinical events, algorithms, gold standard and validation statistics in a uniform table. ETHICS AND DISSEMINATION This study is a synthesis of previously published studies and, therefore, no ethical approval is required. The results will be submitted to a peer-reviewed journal for publication. Results from this systematic review can be used to study outcome research on asthma and can be used to identify case definitions for asthma. PROSPERO REGISTRATION NUMBER CRD42016041798.
Collapse
Affiliation(s)
- Francis Nissen
- Department of Non-Communicable Disease Epidemiology, London School of Hygiene and Tropical Medicine, London, UK
| | | | - Samantha Wilkinson
- Department of Non-Communicable Disease Epidemiology, London School of Hygiene and Tropical Medicine, London, UK
| | | | - Liam Smeeth
- Department of Non-Communicable Disease Epidemiology, London School of Hygiene and Tropical Medicine, London, UK
| | - Ian J Douglas
- Department of Non-Communicable Disease Epidemiology, London School of Hygiene and Tropical Medicine, London, UK
| |
Collapse
|
42
|
Triantafillou P. Making electronic health records support quality management: A narrative review. Int J Med Inform 2017; 104:105-119. [PMID: 28599812 DOI: 10.1016/j.ijmedinf.2017.03.003] [Citation(s) in RCA: 12] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/07/2017] [Accepted: 03/05/2017] [Indexed: 11/16/2022]
Abstract
BACKGROUND Since the 1990s many hospitals in the OECD countries have introduced electronic health record (EHR) systems. A number of studies have examined the factors impinging on EHR implementation. Others have studied the clinical efficacy of EHR. However, only few studies have explored the (intermediary) factors that make EHR systems conducive to quality management (QM). OBJECTIVE Undertake a narrative review of existing studies in order to identify and discuss the factors conducive to making EHR support three dimensions of QM: clinical outcomes, managerial monitoring and cost-effectiveness. METHOD A narrative review of Web of Science, Cochrane, EBSCO, ProQuest, Scopus and three Nordic research databases. LIMITATION most studies do not specify the type of EHR examined. RESULTS 39 studies were identified for analysis. 10 factors were found to be conducive to make EHR support QM. However, the contribution of EHR to the three specific dimensions of QM varied substantially. Most studies (29) included clinical outcomes. However, only half of these reported EHR to have a positive impact. Almost all the studies (36) dealt with the ability of EHR to enhance managerial monitoring of clinical activities, the far majority of which showed a positive relationship. Finally, only five dealt with cost-effectiveness of which two found positive effects. DISCUSSION AND CONCLUSION The findings resonates well with previous reviews, though two factors making EHR support QM seem new, namely: political goals and strategies, and integration of guidelines for clinical conduct. Lacking EHR type specification and diversity in study method imply that there is a strong need for further research on the factors that may make EHR may support QM.
Collapse
|
43
|
Ivory CH, Freytsis M, Lagrew DC, Magee D, Vallejo M, Hasley S. Standardizing Maternity Care Data to Improve Coordination of Care. J Obstet Gynecol Neonatal Nurs 2017; 46:284-291. [DOI: 10.1016/j.jogn.2016.07.013] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 07/01/2016] [Indexed: 10/20/2022] Open
|
44
|
Bahr NJ, Nelson SD, Winnenburg R, Bodenreider O. Eliciting the Intension of Drug Value Sets - Principles and Quality Assurance Applications. Stud Health Technol Inform 2017; 245:843-847. [PMID: 29295218 PMCID: PMC5881398] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/29/2022]
Abstract
Value sets (VSs) used in electronic clinical quality measures are lists of codes from standard terminologies ("extensional" VSs), whose purpose ("intension") is not always explicitly stated. We elicited the intension for the 09/01/2014 release of extensional medication value sets by comparison to drug classes from the October 2014 release of RxClass. Value sets matched drug classes if they shared common ingredients, as evidenced by Jaccard similarity score. We elicited the intension of 80 extensional value sets. The average Jaccard similarity was 0.65 for single classes and 0.80 for combination classes, with 34% (27/80) of the value sets having high similarity scores. Manual review by a pharmacist indicated 51% (41/80) of the drug classes selected as the best mapping for a value set matched the intension reflected in that value set name. This approach has the potential for facilitating the development and maintenance of medication value sets.
Collapse
Affiliation(s)
- Nathan J. Bahr
- Department of Medical Informatics and Clinical Epidemiology, Oregon Health & Science University, Portland, OR, USA
| | - Scott D. Nelson
- Department of Biomedical Informatics, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Rainer Winnenburg
- Stanford Center for Biomedical Informatics Research, Stanford, CA, USA
| | | |
Collapse
|
45
|
Nguyen TN, Trocio J, Kowal S, Ferrufino CP, Munakata J, South D. Leveraging Real-World Evidence in Disease-Management Decision-Making with a Total Cost of Care Estimator. AMERICAN HEALTH & DRUG BENEFITS 2016; 9:475-485. [PMID: 28465775 PMCID: PMC5394558] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Subscribe] [Scholar Register] [Received: 03/10/2016] [Accepted: 08/15/2016] [Indexed: 06/07/2023]
Abstract
BACKGROUND Health management is becoming increasingly complex, given a range of care options and the need to balance costs and quality. The ability to measure and understand drivers of costs is critical for healthcare organizations to effectively manage their patient populations. Healthcare decision makers can leverage real-world evidence to explore the value of disease-management interventions in shifting total cost trends. OBJECTIVE To develop a real-world, evidence-based estimator that examines the impact of disease-management interventions on the total cost of care (TCoC) for a patient population with nonvalvular atrial fibrillation (NVAF). METHODS Data were collected from a patient-level real-world evidence data set that uses the IMS PharMetrics Health Plan Claims Database. Pharmacy and medical claims for patients meeting the inclusion or exclusion criteria were combined in longitudinal cohorts with a 180-day preindex and 360-day follow-up period. Descriptive statistics, such as mean and median patient costs and event rates, were derived from a real-world evidence analysis and were used to populate the base-case estimates within the TCoC estimator, an exploratory economic model that was designed to estimate the potential impact of several disease-management activities on the TCoC for a patient population with NVAF. Using Microsoft Excel, the estimator is designed to compare current direct costs of medical care to projected costs by varying assumptions on the impact of disease-management activities and applying the associated changes in cost trends to the affected populations. Disease-management levers are derived from literature-based concepts affecting costs along the NVAF disease continuum. The use of the estimator supports analyses across 4 US geographic regions, age, cost types, and care settings during 1 year. RESULTS All patients included in the study were continuously enrolled in their health plan (within the IMS PharMetrics Health Plan Claims Database) between July 1, 2010, and June 30, 2012. Patients were included in the final analytic file and were indexed based on (1) the service date of the first claim within the selection window (December 28, 2010-July 11, 2011) with a diagnosis of NVAF, or (2) the service date of the second claim for an NVAF medication of interest during the same selection window. The model estimates the current trends in national benchmark data for a hypothetical health plan with 1 million covered lives. The annual total direct healthcare costs (allowable and patient out-of-pocket costs) of managing patients with NVAF in this hypothetical plan are estimated at $184,981,245 ($25,754 per patient, for 7183 patients). A potential 25% improvement from the base-case disease burden and disease management could translate into TCoC savings from reducing the excess costs related to hypertension (-5.3%) and supporting the use of an appropriate antithrombotic treatment that prevents ischemic stroke (-0.7%) and reduces bleeding events (-0.1%). CONCLUSIONS The use of the TCoC estimator supports population health management by providing real-world evidence benchmark data on NVAF disease burden and by quantifying the potential value of disease-management activities in shifting cost trends.
Collapse
Affiliation(s)
| | - Jeffrey Trocio
- Director, Global Health & Value-Health Outcomes and Evidence, Pfizer Innovative Health
| | - Stacey Kowal
- Principal, Health Economics & Outcomes Research, IMS Health, Fairfax, VA
| | | | - Julie Munakata
- Senior Principal, Health Economics & Outcomes Research, IMS Health
| | - Dell South
- Executive Director, Medical Affairs, Pfizer Innovative Health
| |
Collapse
|
46
|
Scholte M, van Dulmen SA, Neeleman-Van der Steen CWM, van der Wees PJ, Nijhuis-van der Sanden MWG, Braspenning J. Data extraction from electronic health records (EHRs) for quality measurement of the physical therapy process: comparison between EHR data and survey data. BMC Med Inform Decis Mak 2016; 16:141. [PMID: 27825333 PMCID: PMC5101697 DOI: 10.1186/s12911-016-0382-4] [Citation(s) in RCA: 15] [Impact Index Per Article: 1.9] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/18/2016] [Accepted: 11/02/2016] [Indexed: 11/10/2022] Open
Abstract
BACKGROUND With the emergence of the electronic health records (EHRs) as a pervasive healthcare information technology, new opportunities and challenges for use of clinical data for quality measurements arise with respect to data quality, data availability and comparability. The objective of this study is to test whether data extracted from electronic health records (EHRs) was of comparable quality as survey data for the calculation of quality indicators. METHODS Data from surveys describing patient cases and filled out by physiotherapists in 2009-2010 were used to calculate scores on eight quality indicators (QIs) to measure the quality of physiotherapy care. In 2011, data was extracted directly from EHRs. The data collection methods were evaluated for comparability. EHR data was compared to survey data on completeness and correctness. RESULTS Five of the eight QIs could be extracted from the EHRs. Three were omitted from the indicator set, as they proved too difficult to be extracted from the EHRs. Another QI proved incomparable due to errors in the extraction software of some of the EHRs. Three out of four comparable QIs performed better (p < 0.001) in EHR data on completeness. EHR data also proved to be correct; the relative change in indicator scores between EHR and survey data were small (<5 %) in three out of four QIs. CONCLUSION Data quality of EHRs was sufficient to be used for the calculation of QIs, although comparability to survey data was problematic. Standardization is needed, not only to be able to compare different data collection methods properly, but also to compare between practices with different EHRs. EHRs have the option to administrate narrative data, but natural language processing tools are needed to quantify these text boxes. Such development, can narrow the comparability gap between scoring QIs based on EHR data and based on survey data. EHRs have the potential to provide real time feedback to professionals and quality measurements for research, but more effort is needed to create unambiguous and uniform information and to unlock written text in a standardized manner.
Collapse
Affiliation(s)
- Marijn Scholte
- Radboud Institute for Health Sciences, Scientific Institute for Quality of Healthcare, Radboud University Medical Center, Geert Grooteplein 21, 6525 EZ, Nijmegen, The Netherlands. .,Present address: Faculty of Social Sciences, Department of Sociology, Radboud University, Thomas van Aquinostraat 6, 6525 GD, Nijmegen, The Netherlands.
| | - Simone A van Dulmen
- Research Institute for Health Sciences, Scientific Institute for Quality of Healthcare, Radboud University Medical Center, Nijmegen, The Netherlands
| | | | - Philip J van der Wees
- Research Institute for Health Sciences, Scientific Institute for Quality of Healthcare, Radboud University Medical Center, Nijmegen, The Netherlands
| | - Maria W G Nijhuis-van der Sanden
- Research Institute for Health Sciences, Scientific Institute for Quality of Healthcare, Radboud University Medical Center, Nijmegen, The Netherlands
| | - Jozé Braspenning
- Research Institute for Health Sciences, Scientific Institute for Quality of Healthcare, Radboud University Medical Center, Nijmegen, The Netherlands
| |
Collapse
|
47
|
Molony DA. Cognitive Bias and the Creation and Translation of Evidence Into Clinical Practice. Adv Chronic Kidney Dis 2016; 23:346-350. [PMID: 28115076 DOI: 10.1053/j.ackd.2016.11.018] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/21/2016] [Accepted: 11/23/2016] [Indexed: 11/11/2022]
Abstract
The optimal translation of evidence into the clinical practice of nephrology follows validated evidence-based medicine (EBM) principles. Most importantly, the evidence-based medicine practitioner requires that the evidence, as much as possible, addresses in an unbiased manner clinical questions of importance to patients and reflects the truth. In this chapter, we evaluate how cognitive biases that affect medical decision making might systematically bias the overall management of patients with kidney disease and, thus, distort the observations about disease causation, prognosis, diagnosis, and management that are derived from analysis of administrative databases or electronic medical records of health care systems. These cognitive biases influence how questions are framed, how risk factors and health status are measured, how decisions are made, and what actions are implemented. We explore the nature of these biases and the possible places where such biases are most likely to distort clinical care and the collection of the data that is used to inform clinical practice in nephrology. To the extent that cognitive biases selectively modify diagnostic interrogations and therapeutic interventions in some patients, the resultant databases may provide a skewed picture of the health of the nephrology patient population and should be considered with some skepticism when exploited to achieve best practice.
Collapse
|
48
|
Bailey SR, Heintzman JD, Marino M, Hoopes MJ, Hatch BA, Gold R, Cowburn SC, Nelson CA, Angier HE, DeVoe JE. Measuring Preventive Care Delivery: Comparing Rates Across Three Data Sources. Am J Prev Med 2016; 51:752-761. [PMID: 27522472 PMCID: PMC5067199 DOI: 10.1016/j.amepre.2016.07.004] [Citation(s) in RCA: 20] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/26/2016] [Revised: 06/08/2016] [Accepted: 07/07/2016] [Indexed: 11/26/2022]
Abstract
INTRODUCTION Preventive care delivery is an important quality outcome, and electronic data reports are being used increasingly to track these services. It is highly informative when electronic data sources are compared to information manually extracted from medical charts to assess validity and completeness. METHODS This cross-sectional study used a random sample of Medicaid-insured patients seen at 43 community health centers in 2011 to calculate standard measures of correspondence between manual chart review and two automated sources (electronic health records [EHRs] and Medicaid claims), comparing documentation of orders for and receipt of ten preventive services (n=150 patients/service). Data were analyzed in 2015. RESULTS Using manual chart review as the gold standard, automated EHR extraction showed near-perfect to perfect agreement (κ=0.96-1.0) for services received within the primary care setting (e.g., BMI, blood pressure). Receipt of breast and colorectal cancer screenings, services commonly referred out, showed moderate (κ=0.42) to substantial (κ=0.62) agreement, respectively. Automated EHR extraction showed near-perfect agreement (κ=0.83-0.97) for documentation of ordered services. Medicaid claims showed near-perfect agreement (κ=0.87) for hyperlipidemia and diabetes screening, and substantial agreement (κ=0.67-0.80) for receipt of breast, cervical, and colorectal cancer screenings, and influenza vaccination. Claims showed moderate agreement (κ=0.59) for chlamydia screening receipt. Medicaid claims did not capture ordered or unbilled services. CONCLUSIONS Findings suggest that automated EHR and claims data provide valid sources for measuring receipt of most preventive services; however, ordered and unbilled services were primarily captured via EHR data and completed referrals were more often documented in claims data.
Collapse
Affiliation(s)
- Steffani R Bailey
- Department of Family Medicine, Oregon Health & Science University, Portland, Oregon.
| | - John D Heintzman
- Department of Family Medicine, Oregon Health & Science University, Portland, Oregon
| | - Miguel Marino
- Department of Family Medicine, Oregon Health & Science University, Portland, Oregon; Department of Public Health and Preventive Medicine, Division of Biostatistics, Oregon Health & Science University, Portland, Oregon
| | | | - Brigit A Hatch
- Department of Family Medicine, Oregon Health & Science University, Portland, Oregon; OCHIN, Inc., Portland, Oregon
| | - Rachel Gold
- OCHIN, Inc., Portland, Oregon; Kaiser Permanente Northwest Center for Health Research, Portland, Oregon
| | | | | | - Heather E Angier
- Department of Family Medicine, Oregon Health & Science University, Portland, Oregon
| | - Jennifer E DeVoe
- Department of Family Medicine, Oregon Health & Science University, Portland, Oregon; OCHIN, Inc., Portland, Oregon
| |
Collapse
|
49
|
Constructing Episodes of Inpatient Care: How to Define Hospital Transfer in Hospital Administrative Health Data? Med Care 2016; 55:74-78. [PMID: 27479600 DOI: 10.1097/mlr.0000000000000624] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/26/2022]
Abstract
BACKGROUND Hospital administrative health data create separate records for each hospital stay of patients. Treating a hospital transfer as a readmission could lead to biased results in health service research. METHODS This is a cross-sectional study. We used the hospital discharge abstract database in 2013 from Alberta, Canada. Transfer cases were defined by transfer institution code and were used as the reference standard. Four time gaps between 2 hospitalizations (6, 9, 12, and 24 h) and 2 day gaps between hospitalizations [same day (up to 24 h), ≤1 d (up to 48 h)] were used to identify transfer cases. We compared the sensitivity and positive predictive value (PPV) of 6 definitions across different categories of sex, age, and location of residence. Readmission rates within 30 days were compared after episodes of care were defined at the different time gaps. RESULTS Among the 6 definitions, sensitivity ranged from 93.3% to 98.7% and PPV ranged from 86.4% to 96%. The time gap of 9 hours had the optimal balance of sensitivity and PPV. The time gaps of same day (up to 24 h) and 9 hours had comparable 30-day readmission rates as the transfer indicator after defining episode of care. CONCLUSIONS We recommend the use of a time gap of 9 hours between 2 hospitalizations to define hospital transfer in inpatient databases. When admission or discharge time is not available in the database, a time gap of same day (up to 24 h) can be used to define hospital transfer.
Collapse
|
50
|
Russo E, Sittig DF, Murphy DR, Singh H. Challenges in patient safety improvement research in the era of electronic health records. HEALTHCARE-THE JOURNAL OF DELIVERY SCIENCE AND INNOVATION 2016; 4:285-290. [PMID: 27473472 DOI: 10.1016/j.hjdsi.2016.06.005] [Citation(s) in RCA: 16] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/07/2015] [Revised: 06/06/2016] [Accepted: 06/18/2016] [Indexed: 02/08/2023]
Abstract
Electronic health record (EHR) data repositories contain large volumes of aggregated, longitudinal clinical data that could allow patient safety researchers to identify important safety issues and conduct comprehensive evaluations of health care delivery outcomes. However, few health systems have successfully converted this abundance of data into useful information or knowledge for safety improvement. In this paper, we use a case study involving a project on missed/delayed follow-up of test results to discuss real-world challenges in using EHR data for patient safety research. We identify three types of challenges that pose as barriers to advance patient safety improvement research: 1) gaining approval to access/review EHR data; 2) interpreting EHR data; 3) working with local IT/EHR personnel. We discuss the complexity of these challenges, all of which are unlikely to be unique to this project, and outline some key next steps that must be taken to support research that uses EHR data to improve safety. We recognize that all organizations face competing priorities between clinical operations and research. However, to leverage EHRs and their abundant data for patient safety improvement research, many current data access and security policies and procedures must be rewritten and standardized across health care organizations. These efforts are essential to help make EHRs and EHR data useful for progress in our journey to safer health care.
Collapse
Affiliation(s)
- Elise Russo
- Center for Innovations in Quality, Effectiveness and Safety, Michael E. DeBakey VA Medical Center, Houston, TX, United States; Section of Health Services Research, Department of Medicine, Baylor College of Medicine, Houston, TX, United States
| | - Dean F Sittig
- University of Texas Health Science Center at Houston's School of Biomedical Informatics and the UT-Memorial Hermann Center for Healthcare Quality & Safety, Houston, TX, United States
| | - Daniel R Murphy
- Center for Innovations in Quality, Effectiveness and Safety, Michael E. DeBakey VA Medical Center, Houston, TX, United States; Section of Health Services Research, Department of Medicine, Baylor College of Medicine, Houston, TX, United States
| | - Hardeep Singh
- Center for Innovations in Quality, Effectiveness and Safety, Michael E. DeBakey VA Medical Center, Houston, TX, United States; Section of Health Services Research, Department of Medicine, Baylor College of Medicine, Houston, TX, United States.
| |
Collapse
|