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2016 Workshop on Health IT & Economics Data Set Catalog
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The Center for Health Information and Decision Systems compiled this catalog containing much of the data used in the papers presented at the 2016 Workshop on Health IT & Economics.
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PaperAuthorsData Set Name (put each data set on separate line)About DataSetDataSet OwnerDate RangeLink to datasetPaper KeywordsPublicly Available (y/n) (*for fee)
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Addressing the IT Functionality Gap in Healthcare: The Role of Lightweight AppsIdris Adjerid1, Corey Angst1, Ralph GrossEmergency Department dataDataset contains a number of data points per visit, most notably patient demographics (age, gender), acuity, mode of arrival, disposition, and in case of admission, the unit to which they were admittedN/AN/AN/AmHealth, apps, emergency departmentn
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Why Healthcare Should Stop Worrying and Learn to Love the Machine: Predicting Inpatient Admissions from Emergency Department DataIdris Adjerid, Sriram Somanchi, Ralph GrossED acute care dataData on 170,000 patient visits and 63,741 unique patients over 67 month periodN/AN/AN/Amachine learning, prediction, emergency departments, inpatientn
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Personal Goals, Social Comparison, and Health Outcomes in the Age of the Quantified SelfIdris AdjeridHealth band dataData from incoming students at a major North American university on 490 individualsN/ASeptember 2015-May 2016N/Aquantified self, social, wearablesn
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Just What the Doctor Ordered? Physician Mobility and the Adoption of Electronic Health RecordsAngstFlorida Agency for Healthcare AdministrationCensus of bed-level admissions in the state of FloridaAHCA2000 and 2010http://www.floridahealthfinder.gov/Researchers/OrderData/order-data.aspxEHR, physician behavior, adoptiony
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HIMSS Analytics Hospital level adoption of both basic and advanced EHRs HIMSShttp://www.himssanalytics.org/emramEHR, physician behavior, adoptiony
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Does Usage of Patient Portals improve Health Outcomes? An exploratory studyChenzhang Bao, Harpreet Singh, and Indranil Bardhan, Bruce Meyer and Kirk KirkseyEMR: academic medical center in DallasMatched patient interactions using the PWP and their corresponding hospital (inpatient) and clinic (outpatient) visitsN/A2002 to 2014N/Apatient portals, effectivenessn
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MyChartA patient web portals (PWP) with records of medication refills, appointment scheduling, viewing lab tests, and provider inquiriesmychart.orghttps://mychart.inova.org/mychart/patient portals, effectivenessn
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Classification in the Presence of Anchoring Bias: A Model and An Application to Breast Cancer DiagnosisMehmet E Ahsen, Mehmet U. S. Ayvaci, Srinivasan RaghunathanBreast cancer screening outcomes databaseA comprehensive and widely used Breast cancer screening outcomes databaseN/AN/AN/Amachine learning, breast cancer, preventionn
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Variation in EHR Documentation across Primary Care ProvidersGenna R. Cohen, Charles P. Friedman, Andrew Ryan, Julia AdlerMilstein,Data from national commercial EHR vendorWeb-based EHR vendor provide a record of granular clickstream data for all encounters in ambulatory primary care practicesN/AJune 2012N/AEHR, primary care, documentationn
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Awareness and Use of Comparative Provider Quality Information (Aka “Report Cards)
Among the Chronically Ill: Impact of Regional Dissemination and Media Coverage
Neeraj BhandariAligning Forces for Quality Consumer Survey (AF4QCS)Data on patient activation; consumer knowledge of publicly available performance reports that highlight quality differences among physicians, hospitals, and health plans; the ability to be an effective consumer in the context of a physician visit; patient knowledge about her/his illness; skills and willingness to self-manage one's illness; the impact of insurance and payment models; and the relationship between out-of-pocket costs and health care utilization.Robert Wood Johnson FoundationJune 2007-August 2008 and July 2011-November 2012http://www.icpsr.umich.edu/icpsrweb/HMCA/studies/35259Chronic illness, media, physician quality
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Using Advanced Data Analytics and Geographical Information Systems (GIS) to Improve Health, Care Accessibility for VeteransYan Li, Lemuria Carter, World Health Organization Global Health Expenditure databaseGlobal health expenditure database by WHO. Provides internationally comparable numbers on national health expenditures.World Health Organization14http://apps.who.int/nha/databaseData Analytics, HealthCare accessibility, Veterans, nationaly
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Caregivers’ Intention to Adopt Mobile Health Applications: Protection Motivation Theory PerspectiveFereshteh Ghahramani, Jingguo WangOnline SurveyData on US caregivers who provided at least more than 8 hours of care per week in the past year, and are not currently using any mobile application for caregiving purposes. 249 valid responses wereUniversity of Texas at ArlingtonN/AN/AmHealth, adoption, health applicationn
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Investing in Post-Acute Care Transitions: Electronic Information Exchange between Hospitals and Long-term Care FacilitiesDori A. Cross and Julia Adler-Milstein2014 AHA IT Supplement survey collected (31% male, average Age= 45.4)American Hospital Associationhttp://www.aha.org/research/rc/stat-studies/data-and-directories.shtmlpost acute, care transitions, hospitals, HIEy
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Using Health Information Exchange in the Emergency Department: A Trade-off of Volume and DurationPoliti, Liran, Codish, Shlomi, Sagy, Iftach,Fink, LiorOFEK HIE DataIntegrates medical data for over 50% of the Israeli population and is regularly used by ED physiciansOFEK Israel2005http://clalitresearch.org/about-us/our-data/HIE, emergency departmentn
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Factors Determining Patients' Online Ratings for General Practitioners – An Exploratory StudyGewaldAnonymous Survey DataData collected from structured interview from 103 interview-based questionnaires collected during a six week period in the middle of 2016Ulm and Neu-Ulm2016N/Aonline ratings, social median
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Forecasting Adoption of EMR CapabilitiesClaudia Patricia González,Eric W. Ford, Timothy R. HuertaHIMSS Analytics Electronic Medical Record Adoption Model Contains eight-stage (0-7) model that measures the adoption and utilization of electronic medical record (EMR) functions in hospitals HMISS2005 to 2016http://www.himssanalytics.org/emramEMR, Adoptiony
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Catching Them Red-handed: Optimizing the Nursing Homes’ Rating SystemXu Han, Niam Yaraghi, Ram GopalCMS 5-star nursing home ratingRates nursing homes based on three measures: on-site Inspection, Staffing and Quality MeasuresCenters for Medicare & Medicaid Services (CMS)2008 to 2016 https://www.cms.gov/medicare/provider-enrollment-and-certification/certificationandcomplianc/fsqrs.htmlAudit, Graph-based Method, Inflation Detection, Nursing Home, Rating System, Simulationy
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Assessing the Relationship between Certified EHRs and Hospital Performance on Stage 2 Meaningful Use: Are All Certified EHRs Created Equal?A Jay Holmgren,Julia Adler-Milstein, Jeffrey S. McCulloughEHR Products Used for Meaningful Use Attestation Public UseDataset combines meaningful use attestations from the Medicare EHR Incentive Program and certified health IT product data from the ONC Certified Health IT Product List (CHPL) to identify the unique vendors, products, and product types of each certified health IT product used to attest to meaningful use. The dataset also includes important provider-specific data, related to the provider's participation and status in the program, unique provider identifiers, and other characteristics unique to each provider, like geography and provider type.ONC2014 to 2016https://dashboard.healthit.gov/datadashboard/documentation/ehr-products-mu-attestation-data-documentation.phpEHR, CHPL, meaningful use, Medicare, EHR, Medicaid, Incentivey
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CMS Meaningful Use Stage 2 Attestation dataData on Eligible Professionals (EPs) in the Medicare EHR Incentive ProgramCenters for Medicare & Medicaid Serviceshttps://www.cms.gov/Regulations-and-Guidance/Legislation/EHRIncentivePrograms/PUF.htmly
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2014 American Hospital Association SurveyCensus of United States hospitals based on the AHA Annual Survey of Hospitals.American Hospital Associationhttps://www.ahadataviewer.com/additional-data-products/AHA-Survey/y
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2014 AHA Survey – IT SupplementThe data from 3,500 hospitals include electronic clinical documentation, results viewing, decision support, bar coding and more. American Hospital Associationhttp://www.aha.org/research/rc/stat-studies/data-and-directories.shtml
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Smart Card Adoption in Healthcare: An Experimental Survey Design using Message FramingPamella Howell, Mohamed Abdelhamid, Raj Sharman, Sanjukta SmithAnonymized SurveyThe sample includes 331 urban adult patients, however, due to missing values the final sample was 277. Participants are 65% female, 61 % AfricanAmerican, 22% Hispanic, 11% White-Non-Hispanic and 6% other racesBuffalo.eduN/AN/AAdoption, Healthcare, smartcardn
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Does Hospital Electronic Medical Record Adoption Lead To Upcoding or More Accurate Coding?Gautam Gowrisankaran, Keith Joinery, Jianjing LinMedicare Provider Analysis and Review (MedPAR) Information on all inpatient hospital stays for Medicare beneficiaries like the hospital, the beneficiary’s home zip code, age, gender, dates of service, reimbursement amount, dates of admission and discharge, Diagnostic Related Group (DRG), and principal and secondary diagnosis and procedure codesCenters for Medicare & Medicaid Services2008 to 2013https://www.healthdata.gov/dataset/medicare-provider-analysis-and-review-medparDRGs, hospital billing, upcoding, Medicarey
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Technology Adoption Data
National source of hospital IT adoption
data.

Healthcare Information and Management Systems Society (HIMSS) Analytics Databasehttps://data.medicare.gov/Hospital-Compare/Patient-survey-HCAHPS-Hospital/dgck-syfz/datay
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Leveraging Analytics, EHRs and Management to Mitigate Risk for Healthcare Providers and Increase Patient SafetyStephan Kudyba, Dean F. SittigNPDB Public Use Data FileData File contains selected variables from medical malpractice payment and adverse licensure, clinical privileges, professional society membership, and Drug Enforcement Administration (DEA) reports (adverse actions) received by the NPDB National Practioner Data Bank1990 to 2017https://www.npdb.hrsa.gov/resources/publicData.jspEHR, Analytics, Patient Safety, Risky
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Market Concentration of Online Physician ConsultationJia Li, Bin GuGood Doctor OnlineDemographic information, clinic information, service fees, user feedback, contributed articles, and service records from 397,587 registered physicians from 5,332 hospitals.haodf.com2016http://www.haodf.com/Online Doctors, Cosultation, Market, Concentrationn
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Dynamics of Social Influence on New Employees’ Use of Volitional IS: m-EHR Case in Hospital SettingLimLongitudinal m-EHR usage dataData on 156 new doctors usage data and their coworkers who cared for at least one patient in common in a given month
South Korean Hospital
2015N/Am-EHR, Social influence, hospitalsn
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What has been the impact of federal programs to promote the adoption of electronic health records?PalmerTexas EHR Adoption Data
The level of adoption and use of EHRs by health care practitioners and hospitals in Texas. Texas Health and Human Services Commission2010 to 2012https://dashboard.healthit.gov/apps/health-information-technology-data-summaries.phpEHR, federal, adoptiony
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Incentives in a Real-World Decision Aid for Promoting Physical ActivityYash Babar, Shawn Curley, Zhihong Ke, De Liu, Zach ShefflerWellTrain employee dataData on employee wellness containing demographic information activity logs, cash payments, text messages, social interaction data, and user information like the type, duration, intensity, and distance of workoutWellTrain.comJan 1, 2014 to February 22, 2016N/APhysical Activity, Incentive, Wellnessn
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Walking the Tightrope: Balancing Patient Engagement, Service Quality, and Hospital Efficiency with Health ITMenonHealthcare Information and Management Systems Society (HIMSS)Data on whether an application is live and operational at the hospital, whether being implemented, is planned for, or not planned forHIMSS foundation2011 to 2013https://data.medicare.gov/Hospital-Compare/Patient-survey-HCAHPS-Hospital/dgck-syfz/dataPatient Engagement, Service Quality, Health IT, Efficiencyy
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Hospital CompareData on patients’ hospital service experiences such as, the perception of patients of the level of responsiveness and communicativeness of care provider, how often each hospital gives ecommended treatments for certain conditions like heart attack, heart failure, pneumonia, children’s asthma, stroke, influenza, and blood clots, and follows best practices to prevent surgical complications, complications, readmissions & deathsCenters for medicare and medicaid serviceshttps://data.medicare.gov/data/hospital-comparey
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Healthcare Predictive Analytics for Patient Time Series with Non-ignorable Missingness: The Case of Early ICU Mortality PredictionMohammad Amin Morid, Olivia R. Liu Sheng, Samir Abdelrahman
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Do Patients Provide Quality Advice in Online Health Social Networks? An Analysis of Diabetes Related ThreadsVenkatesanHealth social networking site dataData from a popular health social networking site. Total, 2586 posts from 120 threads (topics) relating to diabetes mellitus which had total of 152 questions. average 22 responses for each thread and a total of 588 users.N/AN/AN/ADiabetes,Social Networks, Online, Quality Advicen
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DOES MEANINGFUL USE OF ELECTRONIC HEALTH RECORD SYSTEMS IMPROVE PATIENT EXPERIENCE?Yunfeng Shi, Verónica Fuentes Cáceres, Joel E. Segel, Guodong Liu, Naleef Fareed, Wei-Fan ChenHospital Consumer Assessment of Healthcare Providers and Systems (HCAHPS) It is a survey which contains 18 core questions about critical aspects of patients' hospital experiences (communication with nurses and doctors, the responsiveness of hospital staff, the cleanliness and quietness of the hospital environment, pain management, communication about medicines, discharge information, overall rating of hospital, and would they recommend the hospital)CMS (the Centers for Medicare and Medicaid Services) 2007 to 2014https://data.medicare.gov/Hospital-Compare/Patient-survey-HCAHPS-Hospital/dgck-syfz/dataEHR, Patient Experiencey
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Healthcare Information and Management Systems Society (HIMSS)Contains comprehensive information on HIT, as well as a set of important hospital characteristics.HIMSS foundationhttp://www.himss.org/news/available-research-us-hospital-it-data-1y
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Hospital Compare DataData on hospital performance, and quality information from consumer perspectives.Centers for Medicare & Medicaid Serviceshttps://data.medicare.gov/data/hospital-comparey
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Using Machine Learning to Detect Fraudulent ReviewsAishwarya Shukla, Weiguang Wang, Gordon Gao, Ritu AgarwalOnline Dcotor review dataDoctor review data containing 105,612 entries from one of the biggest online doctor platforms in India.N/AN/AN/AMachine Learning, Fraud, Reviews, Onlinen
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Detecting Anomalous Patterns of Care using Health Insurance ClaimsSriram Somanchi, Edward McFowland III, Daniel B. NeillzHighmark health insurance claim dataHealth insurance claims from Highmark 1 for approximately 6 million patients across the United States. The information includes demographic, diagnostic and medication information for all the patients across all disease categories.Highmark Health2008 to 2014N/Ahealth insurancen
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Embedding Cost Information with Data-Driven Clinical Pathways for Chronic Care DeliveryYiye Zhang, Rema PadmanEHR data for chronic kidney disease (CKD)Clinical data extracted from the EHR of a community nephrology practice for chronic kidney disease (CKD)Unknown2009 to 2011N/AChronic Care, EHR, Costn
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Measuring the Impact of Recent Industry and Regulatory Efforts to Increase Automation of Routine Healthcare Administrative TransactionsWashingtonCAQH IndexSurveys of commerical medical and dental health plans and healthcare providers nationwideCAQH2015https://www.caqh.org/sites/default/files/explorations/index/report/index_guide.pdfHealthcare Automationy
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Entry Regulation and the Effect of Public Reporting: Evidence from Home Health CompareBingxiao Wu, Jeah Kyoungrae Jung,Hyunjee Kim, Daniel PolskyOutcome and Assessment Information Set (OASIS)Contains comprehensive information on risk factors and outcome measures, including both ADL and IADLUS National Library of Medicine, National Institutes of Health 2001 to 2006https://www.cms.gov/Medicare/Quality-Initiatives-Patient-Assessment-Instruments/OASIS/DataSet.htmlRisk, ADL, IADL, Regulation, Health
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The Effect of Online Reviews on Physician Demand: A Structural Model of Patient ChoiceYuqian Xu, Mor Armony,Anindya GhoseUser Generated Content (UGC) informationObservations from 872 doctors in the United States from one of the leading online appointment booking platformsN/ANovember 27, 2014, to April 12, 2015NAdoctor, physician, patient choice, quality, social media, structural model, text mining, sentiment analysis, rating, review, operational characteristic, outpatient care, healthcaren
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City-level health status data from the Kaiser Family FoundationKaiser Family Foundationhttps://www.kff.org/statedata/y
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consumer income data from U.S. CensusIncome and Poverty in the United StatesU.S Census1960 - 2016https://www.census.gov/data/tables/2017/demo/income-poverty/p60-259.htmly
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