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2015 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 2015 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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The Adoption of Home Healthcare Robots:
Investigating the Moderating Effect of Demographic Characteristics		Ahmad Alaiad, Lina Zhou,
Gunes Koru		An online survey was posted to the online communities of home healthcare agencies, and forwarded to their mailing list.
Paper-based copies of the survey were also distributed physically to the target population.		A multiple-item method was used to construct the survey instrument based on the literature (Venkatesh et al., 2003).UnavailableN/AN/AHHRs, Robot Technology, Preferred Tasks and Applications of HHRsN
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The Impact of Patient Portals on Patient Care:
Evidence from Alert Closures for Diabetes Patients		AlnsourAn archival data setan archival data set for 1,490 diabetes patients, who received, viewed and closed a focal alerts through Kaiser Permanente’s patient portals (KP.org).KP.orgN/AN/APatient Portals, Alerts, Digital Health, Diabetes, Comorbid AlertsN
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Health Information Exchange and Reduced Healthcare SpendingIdris Adjerid, Julia Adler-Milstein,
Corey M. Angst		HIE SurveyNationwide survey of HIEs measuring stage of development, organization structure, and other attributeseHealth Initiative (eHI) 2003-2010https://www.ehidc.org/articles/surveysHealth Information Exchange, HIE, Interoperability, Survey, SustainabilityN
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US Census dataDemographic informationUS Census Bureau1998-2010N/AEducation, Age, Race, Population of the CountyY
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THE IMPACT OF PATIENT HEALTH INSURANCE
COVERAGE AND LATENT HEALTH STATUS ON HOSPITAL READMISSIONS		Sezgin Ayabakan, Indranil Bardhan,
Eric Zheng		UnavailableCongestive Heart Failure (CHF) patient visits across 68 hospitals in North TexasDallas Fort Worth Hospital Council (DFWHC) Foundation2005-2011N/AIther private insurance, Self-pay, or MedicareN
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Mobile phone use and willingness to pay for SMS for diabetes in BangladeshShariful Islam, Andreas Lechner, Dewan Alam,
Uta Ferrari, Jochen Seissler, Rolf Holle and Louis Niessen		Face-to-face interviews using a structured questionnaireUnavailableUnavailableUnavailableN/AWillingness to Pay (WTP), Mobile Phone SMS, Diabetes, mHealth, BangladeshN
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Matching Social Support and Health Outcome in an Online Weight Loss CommunityLu Yan, Yong TanUnavailableUnavailablea free online obesity community2006-2013N/ASocial Support, Support Balance, Support Adequacy, Obesity, Social Media, Health 2.0N
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Health IT and Ambulatory Care QualityCarole Roan Gresenz, Scott Laughery, Amalia R. Miller, Catherine E. TuckerMedicare Inpatient Limited Data Set (LDS)Contains information on all hospitalizations among Medicare fee-for-service (approximately 13 million records per year)Centers for Medicare & Medicaid Serivces2003-2012https://www.cms.gov/Research-Statistics-Data-and-Systems/Files-for-Order/LimitedDataSets/Health IT, Ambulatory Care, MedicareY
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Nationwide Inpatient Sample (NIS)Largest publicly available all-payer inpatient health care database in the United States, yielding national estimates of hospital inpatient staysHealthcare Cost and Utilization Project (HCUP)1988-2010https://www.hcup-us.ahrq.gov/tech_assist/centdist.jspY*
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Healthcare Information and Management Systems Society
(HIMSS) Analytics Database (HADB)		Contains information on health IT adoption on over 30,000 providers,and includes both hospitals and ambulatory providersHIMSS Foundation
N/Ahttp://www.himssanalytics.orgY*
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Heterogeneous Treatment Effect of Electronic Medical Records on Hospital EfficiencyRuirui SunHIMSS Analytics
Annual survey about Health IT adoption
HIMSS Foundation
2008http://www.himssanalytics.orgHospital, Electronic Medical Records, Length of stay, Finite Mixture ModelY*
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Nationwide Inpatient Sample (NIS)Largest publicly available all-payer inpatient health care database in the United States, yielding national estimates of hospital inpatient staysHealthcare Cost and Utilization Project (HCUP)2002 - 2008https://www.hcup-us.ahrq.gov/tech_assist/centdist.jspY*
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Healthcare Outcomes, Information Technology, and Medicare Reimbursements: A hospital-level analyses.Indranil Bardhan, Danish SaifeeMedicare Inpatient Prospective Payment Systems(IPPS) dataHospital-specific charges for the more than 3,000 U.S. hospitals that receive Medicare Inpatient Prospective Payment System (IPPS) payments for the top 100 most frequently billed discharges, paid under Medicare based on a rate per discharge using the Medicare Severity Diagnosis Related Group (MS-DRG)Centers for Medicare & Medicaid Serivces2013https://www.cms.gov/research-statistics-data-and-systems/statistics-trends-and-reports/medicare-provider-charge-data/inpatient.htmlHealthcare Cost, Reimbursememt, Medicare, Fee-for-serviceY
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CMS Hospital CompareData on hospital performance, and quality information from consumer perspectives.Centers for Medicare & Medicaid SerivcesN/Ahttps://data.medicare.gov/Y
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HIMSS AnalyticsHIMSS Analytics is a global healthcare advisor, providing guidance and market intelligence solutions that move the industry forward with insight to enable better health through the use of ITHIMSS AnalyticsN/Ahttp://www.himssanalytics.org/Y*
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Does the Adoption of EMR Systems Inflate Medicare Reimbursements?Kartik K Ganju, Hilal Atasoy, Paul A PavlouHIMSS AnalyticsHIMSS Analytics is a global healthcare advisor, providing guidance and market intelligence solutions that move the industry forward with insight to enable better health through the use of ITHIMSS Analytics2004-2011http://www.himssanalytics.org/Clinical Physician Order Entry (CPOE), EMR Adoption, UpcodingY*
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Medicare Inpatient and Prospective Payment System (IPPS) filesInformation on the complexity of cases that the hospital treats under Medicare cases. Based on the proportion of patients that belong to different DRGs that are inpatients in the hospital in a particular yearCenters for Medicare & Medicaid Serivces2004-2011https://www.cms.gov/Medicare/Medicare-Fee-for-Service-Payment/AcuteInpatientPPS/index.html?redirect=/acuteinpatientpps/Y
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Does IT Enable Revenue Management in Hospitals?Kangkang Qi, Ranjani Krishnan, Matt Wimble, Jonas HeeseHIMSS Analytics database
HIMSS Analytics is a global healthcare advisor, providing guidance and market intelligence solutions that move the industry forward with insight to enable better health through the use of ITHIMSS Analytics2002-2012http://www.himssanalytics.org/Clinical IT, Revenue Management, Hospital RevenueY*
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California Office of Statewide Health Planning and Development (OSHPD)Hospital and patient-level data on financial performance and other financial and nonfinancial metrics are collected from the OSHPDOSHPD2002-2012http://www.oshpd.ca.gov/hid/Y
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Do Hospitals Value Interoperability? Evidence from Health IT Vendor ChoiceSunita DesaiNational hospital-level panel data setInformation on health IT vendor choice, participation in health information exchange, system membership, and location. The sample consists of non-federal, general medical and surgical hospitals.Author2008-2012N/AHealth IT, Network Effects, Technology PolicyN
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HIMSS AnalyticsHIMSS Analytics is a global healthcare advisor, providing guidance and market intelligence solutions that move the industry forward with insight to enable better health through the use of ITHIMSS Analytics2008-2012http://www.himssanalytics.org/Y*
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American Hospital Association (AHA) IT SupplementProvides data on HIE participation for hospitals not covered in the HIMSS Analytics databaseAHA2008-2012http://www.ahadataviewer.com/about/it-database/Y*
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Testing Theories of Innovation Diffusion: Analysis of Physicians’ Adoption of Electronic Health RecordMarty CohenPhysician Workflow Supplement to the National Ambulatory Medical Care SurveyNational survey designed to meet the need for objective, reliable information about the provision and use of ambulatory medical care services in the United States. Findings are based on a sample of visits to non-federal employed office-based physicians who are primarily engaged in direct patient care.National Center for Health Statistics2011, 2012N/AAmbulatory Medical Care, Electronic Health Record, Health IT, AdaptationN
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Vendor choice in Physician EMR Adoption: The Role of Network EffectsMariano Irace, Frank LimbrockCMS Medicare EHR Incentive ProgramUsed to analyze the determinants of EMR vendor choice by physicians in Florida under the programCenters for Medicare & Medicaid Services2013N/AEMR, Vendor Choice, Network EffectsY
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Florida Medicaid EHR Incentive ProgramUsed to analyze the determinants of EMR vendor choice by physicians in Florida under the programCenters for Medicare & Medicaid Services2013N/AY
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HIMSS databaseHIMSS Analytics is a global healthcare advisor, providing guidance and market intelligence solutions that move the industry forward with insight to enable better health through the use of ITHIMSS Analytics2013http://www.himssanalytics.org/Y*
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AHCA’s Inpatient Discharge DatabaseUsed to determine physician's main hospitalAgency for Health Care Administration2013http://healthdatastore.com/data/florida-hospital-data/ahca-inpatient-discharge-data/Y (requires email)
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CMS Provider databaseUsed to obtain additional data on physiciansCenters for Medicare & Medicaid Services2013https://www.cms.gov/research-statistics-data-and-systems/statistics-trends-and-reports/medicare-provider-charge-data/physician-and-other-supplier.htmlY
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Examining Integrated Performance in Healthcare Using Data-driven Clinical PathwaysYiye Zhang, Rema PadmanPatients’ longitudinal records from structured electronic health records (EHR) dataData summarized as their clinical pathways, where at each point in time multiple clinical interventions, such medication prescriptions, medical orders, procedures, and clinical visits, interact with one another to drive patients’ clinical conditions over time.AuthorN/AN/AClinical Pathways, Affordable Care Act, Healthcare, Accountable Care OrganizationN
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Predicting Inpatient Admissions from Triage Data: A Machine Learning ApproachRalph Gross, Idris Adjerid, R CoulterInformation on Emergency Department patient visits at a community-sized acute care hospital in western PennsylvaniaInformation collected on 104,000 patient visits over a course of 42 monthsAuthorN/AN/APredicting Inpatient Admission, Triage, Support Vector Machine, Hospital EfficiencyN
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Telemedicine in Humanitarian Assistance and Disaster ReliefJoyce Byrne, Brendan Smith, Emaan OsmanAfter Action Reports (AAR)AARs written by the U.S. Government, the United Nations, and other agencies for both domestic and international humanitarian and disaster relief effortsMultiple domestic and international humanitarian and relief agenciesN/AN/ADisaster Relief, Humanitarian, Telemedicine, After Action ReportsN
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Leveraging Data Analytics to Improve Home Care Processes and Utilization OutcomesGunes Koru, Pooja Parameshwarappa, Dari AlHuwailMedicare Home Health Compare data repositoryA number of quality measures including twelve clinical processvariables and two utilization outcome measures at the HHA level.Centers for Medicare & Medicaid Services2014https://www.medicare.gov/homehealthcompare/Home Health Agency, Home Care, Hospital Admission RatesY
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Visual Social Media Analytics for Patient Centric CareXiao Liu, Bin Zhang, Anjana Susarla, Rema Padman, Hsinchun ChenYouTube data APIMetadata for YouTube videos were stored and analyzed for medical knowledge extraction. Videos were top 100 results using search query keywords from www.dailystrength.orgYouTubeN/AN/AMedical Knowledge Extraction, YouTube, Visual Social Media, Metadata, APIY
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A Retrospective Review of the State and Trends of PDA, Smartphone and Tablet Use in US HospitalsRaymonde Charles Y. Uy., Fabricio S. P. Kury, Paul A. FonteloHIMSSHIMSS Analytics is a global healthcare advisor, providing guidance and market intelligence solutions that move the industry forward with insight to enable better health through the use of ITHIMSS Analytics2005-2012http://www.himssanalytics.org/Mobile Operating System, Technology Trends, Health IT, Mobile DevicesY*
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There’s an App for that: Addressing the Handoff Problem in Healthcare using MobileIdris Adjerid, Ralph Gross,R Craig CoulterEmergency Department visits for a community-sized acute care hospital in western PennsylvaniaDataset contains a number of data points per visit most notably patient demographics (age, gender), acuity, mode of arrival, disposition, and in case of admission which unit they were admitted toAuthor54 monthsN/AEmergency Department, Length of Stay, Mobile apps, Health ITN
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Lack of Comprehensive Cost Evaluations in Mobile Technology Integration Studies is a Barrier to Value Creation in Cancer Care DeliveryJohn D. Calhoun, Sriram Iyengar, Thomas FeeleyReview of publications addressing the application of mobile technology solutions to cancer care816 papers completed in the 15 year period from January 1, 2000 through December 31, 2014Author2000-2014N/AMHealth, EHealth, Mobile Technology, TelemedicineN
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An Empirical Analysis of the Financial Benefits of Health Information Exchange in Emergency DepartmentsNiam YaraghiStudy conducted by authorPatient care using HIE was compared to patient care without use of HIE at a western New York hospital. The former group consists of 698 patients, and the latter of 1275 patients.AuthorMarch 27, 2014 to May 24, 2014N/AHealth Information Exchange (HIE), Emergency Department, Patient CareN
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The Impact of Health Information Exchanges on Emergency Department Length of StayTurgay Ayer, Mehmet Ayvaci, Zeynal Karaca, Jan Vlachy, and Herbert S. WongState Emergency Department Databases through Healthcare Cost and Utilization Project (HCUP)The SEDD capture emergency visits at hospital-affiliated emergency departments (EDs) that do not result in hospitalization. Information about patients initially seen in the ED and then admitted to the hospital is included in the State Inpatient Databases (SID). The SEDD files include all patients, regardless of payer, providing a unique view of ED care in a State or in a defined market over time.AHRQvaries by statehttps://www.hcup-us.ahrq.gov/seddoverview.jspHealth Information Exchange (HIE), Emergency Department, Healthcare Operations, Care CoordinationY*
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American Hospital Association (AHA) DataNational source of proprietary hospital and health system data collected and verified by the American Hospital Association. 6,400 hospitals from the AHA Annual Survey
More than 1,000 data fields including hospital contact information, characteristics and services.		AHAN/Ahttp://www.aha.org/research/rc/stat-studies/data-and-directories.shtmlHopsitals, Membership, Financial Data, Medicare Cost ReportsY*
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Impact of Organizational Usage Experience on Service Operation Efficiency: A Study of Online Care DeliveryChangmi Jung, Rema Padman, Linda Argote, Ateev MehrotraeVisit records1,977 eVisits submitted by 1,303 unique patients during a 47-month period are included in this study, and 29 physicians from four practices provided eVisit serviceseVisit.com47 months, unspecifiedN/APatient Wait Time, Knowledge Transfer, ProductivityN
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Workload Reduction Through Usability Improvement of Hospital Information, Systems – The Case of Order Set OptimizationDaniel Gartner, Yiye Zhang and Rema PadmanMajor U.S. university hospital, focusing on ‘Asthma major’ patients15 patients who were prescribed 1,150 order items within 24 hours before and after admissionAuthorN/AN/AHealthcare Information Systems, Health Informatics, Health Information Systems, Medical IS, Analytical Modeling, HeuristicsN
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Fostering Patient Informed Consent to Sharing Personal Health Information: A Field ExperimentMohamed Abdelhamid, Raj Sharman, Ram BezawadaOnline surveySurvey administered to 309 people through Amazon Mechanical TurkAuthorN/AN/AHIPAA, Consent, Health Information Exchange (HIE), Healthcare, Message Framing, Sharing Personal Health Information.N
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Impediments to the adoption and scalability of mHealth interventions in BurundiPatrick NdayizigamiyeSurvey of primary healthcare workers in Burundi212 primary healthcare workers acceptance of eight mHealth capabilitiesAuthorN/AN/APrimary Healthcare Workers, mHealth N
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The Effect of Previous Work Experience on eHealth Adoption of the ElderlyRobert Rockmann, Heiko GewaldQuantitative studyStudy is in progress -- a paper-based field survey among elderly individuals (aged 65+) in Germany who have been in contact with the Internet at least once.AuthorN/AN/AeHealth, Computer self-efficacy (CSE), Elderly, Internet AdoptionN
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The Impact of Online Ratings and Reviews on New Patient ReferralsAnton Ivanov, Ram Bezawada, Raj SharmanVitals.comIdentification of U.S. oncologists with at least 1 user review and ratingMDx Medical, Inc.2009-2013Vitals.comPhysician-rating Websites, Patient Referrals, Online User Ratings, Physician ReviewsY
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Centers for Medicare and Medicaid Services (CMS)Data on referral patterns for a sample of oncologistsCenters for Medicare and Medicaid Services2009-2013https://www.cms.gov/Y
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HealthgradesData ononcologists’ characteristicsHealthgrades Operating Company2009-2013http://www.healthgrades.com/Y
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Exploring Financial Incentives to Improve Medication AdherenceAlan Yang, Upkar VarshneyWeb of KnowledgeScienctific citation indexing service. Keywords were used to identify some of 28 articlesThomson ReutersN/Ahttp://ipscience.thomsonreuters.com/product/web-of-science/Financial Intervention for Medication Adherence (FIMA), Behavioral Intervention, Financial IncentivesY*
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JSTORDigital library containing academic journals, books, and primary sources. Keywords were used to identify some of 28 articlesITHAKAN/Ahttp://www.jstor.org/Y*
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Using Mobile Messaging to Leverage Social Connections for the Social Good: Evidence from a Large-scale Randomized Field ExperimentTianshu Sun, Gordon Gao, Ginger Zhe JinField experiment with blood bank located in provincial Chinese capital city80,000 donors split into seven test groupsAuthorDecember 2014 (course of 15 days)N/ABlood Shortage, Mobile Messaging, Blood BankN
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Show Me the Way To Go Home: An Empirical Investigation of Ride Sharing and Alcohol Related Motor Vehicle HomicideBrad Greenwood, Sunil WattalCalifornia Highway Patrol’s Statewide Integrated Traffic Report System (SWITRS)Information of number of crashes within each California township, blood alcohol content of driver, number of parties involved, weather, speed, and other environmental factors. 12420 observations spanning 23 quarters over 540 townships in CaliforniaCalifornia Highway PatrolJanuary 2009 – September 2014http://iswitrs.chp.ca.gov/Reports/jsp/CollisionReports.jspUber, Drunk Driving, Vehicular Homicide, Difference in Difference, Natural
Experiment, Platforms		Y
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A Hidden Markov Model of Mental Health Dynamics of Breast, Cancer Patients using Data from m-Health ApplicationsSanghee Lim, Juntae Kim, Byungtae Lee, Jongwon LeeBreast cancer patients1,167 daily mental health logs for 78 breast cancer patients gathered via a mobile mental health tracker called “Pit-a-Pat” across three dimensions (sleep satisfaction, mood, and anxiety) levels) in the largest Hospital in South KoreaAuthorApril 2013 - March 2015N/AHidden Markov Model (HMM), Mental Health, Mobile Mental Health Trackers N
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Identifying Novel Adverse Drug Events from Health Social Media Using Distant SupervisionXiao Liu, Hsinchun ChenHealth social media discussion forums,Information extraction system for mining patient-reported adverse drug events in online patient forumsVarious health forumsN/AN/AHealth Social Media, Diabetes, Twitter, Adverse Drug EventY
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TwitterTwitter is an online social networking service that enables users to send and read short 140-character messages called "tweets".Twitterhttp://www.twitter.comN/AY
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Drug event reports from FAERSSource for known drug indication (the medical condition a drug is prescribed for) and adverse drug event relations.U.S. Food and Drug AdministrationN/Ahttp://www.fda.gov/Drugs/GuidanceComplianceRegulatoryInformation/Surveillance/AdverseDrugEffects/ucm082193.htmY
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Privacy Concerns and Information Revelation in Online Patient CommunitiesAdel Yazdanmehr, Fereshteh Ghahramani, Jie (Jennifer) ZhangPatientsLikeMe Online health community containing disease-specific forums. Used to collect profile and communication data of Lung Cancer and Seasonal Allergy patients.PatientsLikeMeN/Ahttps://www.patientslikeme.com/Patient Privacy, Privacy, Online Patient CommunitiesY
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imHealthy: A Comprehensive Health Assessment and Intervention System for People in Medically Underserved CommunitiesLeming Zhou, Valerie Watzlaf, Paul AbernathyEHRUsed to collect subject's health information. Based on Open EMR (www.open-emr.org). Includes doctor appointment management, lab appointment management, patient tracking, medical assistant status track, and itemized data collectionAuthorN/AN/AUnderserved Communties, Personalized Intervention, Health Status AssessmentNThese two datasets will be constructed and used by the author for their idea. The paper is qualitative and describes the construction of these datasets as solutions to a problem.
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Well-being indexMeasures constructs such as positive emotions, resilience, the quality of relationships and realization of an individual’s potential. AuthorN/AN/AN
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Asthma Surveillance Using Social Media DataWenli Zhang, Sudha Ram, Mark Burkart, Max Williams and Yolande PengetenzeCDC Asthma Surveillance Data (Source: 2013 National Health Interview Survey (NHIS))Asthma surveillance data includes collection of asthma data at both the national and the state level. National data is available on asthma prevalence, activity limitation, days of work or school lost, rescue and control medication use, asthma self-management education, physician visits, emergency department visits, hospitalizations due to asthma, and deaths due to asthma from National Center for Health Statistics (NCHS) surveys and the Vital Statistics System. Asthma surveillance data at the state level include adult and child asthma prevalence from the Behavioral Risk Factor Surveillance System (BRFSS) and in-depth state and local asthma data through implementation of the BRFSS Asthma Call-back Survey (ACBS).CDCN/Ahttp://www.cdc.gov/asthma/asthmadata.htm, http://www.cdc.gov/asthma/nhis/2013/table3-1.htmAsthma, Prevalence rates, Twitter, Geo-locationY
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Asthma-related Twitter datasetAsthma-related Twitter stream containing one or more of 18 related keywords that were suggested by the clinical collaborators from Parkland Center for Clinical Innovation (PCCI). A large dataset of more than 5 million asthma-related tweets was collected over a period of approximately 6 monthsAuthor11/1/2013 – 6/30/2014N/AN
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Managing Paradoxical Tensions to Improve Patient Satisfaction: A View from the Patients through User-Generated Online Physician ReviewsFeng Mai, Zhe Shan,Dong-Gil KoVitals.compreliminary analyses include 1,286,648 physicians and1,560,639 reviews. study limited to 272,192 physicians who received at least one textual review and have no missing value.Vitals.comN/AVitals.comOnline Physician Reviews, Patient Satisfaction, Individual Health Care (IHC), Individual Health Services (IHS), Patient-Centered CareY
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Do online communities help patients to achieve health goals? The role of sub-group cultures and progression spiral effects Nadee Goonawardene, Sharon Swee-Lin TanA health 2.0 websiteThe ‘Obesity’ Support Group was chosen for analysis for information such as: profile information, friend network, discussion threads, subscribed support groups, goal descriptions, updates, self-reported progress levels, comments, and goal end dates of patients.N/AN/AN/AOnline Healthcare Communities, Healthcare Goal Achievement, Social SupportN
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Is Technology Eating Nurses? Staffing Decisions in Nursing HomesSusan F. Lu, Huaxia Rui, Abraham SeidmannOnline Survey Certificate and Reporting Database (OSCAR)2,119 nursing homes and construct a seven-year,unbalanced panel with 12,313 observationsCenters for Medicare and Medicaid Services2006-2012https://www.cms.gov/Research-Statistics-Data-and-Systems/Downloadable-Public-Use-Files/Provider-of-Services/index.htmlProcess Quality, Staffing, Labor, Automation Technology, Vertical DifferentiationY*
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Health Information Systems Society (HIMSS)HIMSS Analytics is a global healthcare advisor, providing guidance and market intelligence solutions that move the industry forward with insight to enable better health through the use of ITHIMSS Analytics2005-2011http://www.himssanalytics.org/Y*
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