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Health Analytics at Georgia Tech: From Information to Knowledge to Decision Making. Nicoleta Serban, PhD and Julie Swann, PhD Industrial and Systems Engineering Georgia Institute of Technology May 2014. Data Science Framework. Data Representation Sampling. Information Infrastructure
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Health Analytics at Georgia Tech: From Information to Knowledge to Decision Making Nicoleta Serban, PhD and Julie Swann, PhD Industrial and Systems Engineering Georgia Institute of Technology May 2014
Data Science Framework • Data • Representation • Sampling • Information • Infrastructure • Management • Knowledge • Computation • Tools • Decisions • System engineering • Data architectures • Data integration, sharing and federation • Data privacy rules • Data wrangling • Deriving hypotheses • Validating hypotheses • Eliciting causal relations • Designing, planning, and optimizing • Testing, ranking, scoring • System dynamics • Data mining • Machine learning • Statistical inference • Network analysis • Simulations • Visualization
Scope of Data in Healthcare Data Types Examples • Disease Registry • Disease Progression • Electronic Health Records • Facility Info • Medical Claims Data • National Survey or Examination Data • State Databases • General • Cystic Fibrosis • “Natural History” models • Queries (CHOA, VHA) on specific projects • VA satellite clinics • Medicaid(children and pregnant women, GA + 13 other states, 2005-2009) • NHANES, HCUP KIDS • GA’s Oasis, HCUP SEDD and SID • Census, National Provider Index, GIS
CMS Data and Background Medicaid claims data will be used as a test bed for the decision-making support tools targeting knowledge representing the care of children with Medicaid.
CMS Medicaid Claims Data • MAX Claims Data • Personal Summary: patients, demographics, birthdate, etc. • Inpatient: claims, diagnoses, procedures, LOS, payment • Other Therapy: claims for physician, lab, clinic, outpatient • Long Term Care:facility type, date of service, etc. • Prescription Drug:paid drug claims • Patient-level Identifiable-Files with locations and a provider-ID • Years 2005 – 2009 for 14 states (+2010-2011 upcoming) • SE: Georgia, Alabama, Arkansas, Louisiana, Mississippi, N. Carolina, S. Carolina, Tennessee, Texas • Other: California, Minnesota, New York, Pennsylvania • Study population: children and pregnant women GT Project Champion: Beth Mynatt (IPaT, GT)GT Lead on Information Technology: Matt Sanders (GTRI) GT Research Leads: NicoletaSerban and Julie Swann
Medicaid Project: Approved Topics • MEASURING AND EXPLAINING INEQUITIES:To assess the impact of healthcare system characteristics vs. inequities in healthcare, including geographical, use, quality, expenditure and outcomes among Medicaid children enrollees, especially in states with historic inequities like in the southeast. • OPTIMIZING INTERVENTIONS AND DELIVERY SYSTEMSTo analyze flows and policies across the system, e.g., the match between supply and demand, and financially, both geographically and across time, along with the corresponding costs or outcomes, to analyze improved methods of delivery including medical homes.
Medicaid Project: Implementation • Information: • Identifiable patient-level claims • 5 years+14 states = • 266,839,307,070 Observations • 2 Terabytes of information • Data: • Represented as patient care trajectories: utilization, cost and patient characteristics • Sampled by disease Challenge #1: HIPPA and CMS data safeguards compliance - data environment: access, sharing, linking, storage Challenge #2: Database backbone - projected research needs - projected computational needs Challenge #3: Data Processing - unavailability of tools to process-mine claims - additional data and information needs - expert opinion & collaborations
Medicaid Project: Safeguards • Data stored in secured location at Georgia Tech, with access to the identifiable patient files by a limited set of employees approved by CMS & IRB • Sharing of aggregated data is allowed with collaborators, if consistent with research protocol • Cells should have at least 11 entries • Data undergoes review process at GT before release from data workstation • Significant liability involved if breach occurs
Medicaid Project: Health Analytics • Knowledge • Systematic disparities in access, outcomes and cost • Network of providers • Profiles of patient-level care pathways • Data • Baseline Metrics • Care Pathway • Access & Outcomes Process Mining Spatial Statistical Models Functional Data Analysis Unsupervised classification Sequence clustering Markov-decision processes Optimization
Medicaid Project: Health Analytics • Knowledge: • Systematic disparities in access, outcomes and cost • Network of providers • Profiles of patient-level care pathways • Decision Making: • Policy interventions • Network Interventions Markov-decision processes Causal Inference Optimization Modeling Simulations
Medicaid Project: Research Scope • Limitations • Research must fit within the scope proposed to CMS • Analysis of raw data must be conducted at GT • Process for analyzing data is onerous, time-consuming, and “expensive” • The most recent (~2) years of data are not available • Positives • We can benchmark GA against 13 other states • Patients and/or providers can be followed longitudinally • Permission of pursuing research topics and publication of the related findings is not required to be submitted to CMS or GT
Medicaid Project: Opportunities • Developing the proof of concept in building large infrastructures for protected information • Becoming the center for deployment of tools for mining claims data • Advancing rigor in health analytics • Educating students and visiting researchers • Informing policy making in understanding and managing the healthcare system
Health Analytics at GT Health analytics at GT bridges fundamental mathematical and computational modeling with health service research and health economics as a means of translating health and healthcare data into knowledge and decision making.
Health Analytics: Serban& Swann Group • Healthcare Access & Outcomes • Measurement • Linking Access & Outcomes • Interventions • Policy & Network Interventions • Cost-effectiveness: Telemedicine • Pediatric Asthma • Baseline Metrics • Care Pathways in Utilization & Cost • Collaborations between GT ISyE, GT IPaT, Children’s, CDC, VHA, DCH, DPH and other health entities
Healthcare Access & Outcomes We define healthcare accessas the equal opportunity of people to get appropriate care to maintain or improve their health. We focus primarily on making inferences on spatial access, which is particularly important for managing chronic diseases where regular visits and adherence to recommended care practices can reduce severe outcomes.
Health Analytics and Access • Measurement Estimating spatial access of different populations by taking into account supply and demand trade-offs and system constraints. • Inference: Equity Studying systematic disparities in access to services between population groups. • Inference: Linking to Outcomes Understand how access is associated to health outcomes geographically and longitudinally • Evaluating Interventions Informed decision making in healthcare delivery -- policy and network interventions targeting improvement in spatial access with a significant projected impact on outcomes
Access Measures: Pediatric Primary Care Data: National Provider Index (NPI), Medicaid claims, Bureau Census, Geographic Information System among other sources • Study Population: Children in 14 states • Measurement Model: Matching patients to providers using optimization modeling estimated at the census tract level • Spatial Access Measures: Travel distance/time, Congestion & Coverage
Access Measures: Pediatric Primary Care GEORGIA: Disparities across geography Coverage Congestion • Large discrepancies between urban and rural care. • High congestion across state except for some cities. • High coverage in broad regions surrounding the most populated cities. Low coverage in many rural areas. GEORGIA: Disparities between Medicaid & non-Medicaid Congestion Coverage • Yellow and red regions indicate areas where the non-Medicaid patients' availability of services is superior to Medicaid patients, while blue regions indicate areas where the reverse is true
Access ~ Outcomes: Pediatric Asthma Data: Health Cost and Utilization Project (NC) DPH OASIS (GA) • Study Population: Children ages 4-17 in GA & NC • Geographic Access: travel distance between patients and matched providers using optimization estimated at the census tract level • Outcome measure: ED visit & hospitalization rates at the county level
Access ~ Outcomes: Pediatric Asthma • Access is significant alone and in interactions with other factors • Impact of access varies with geography • Improving access is expected to reduce the occurrence of severe outcomes.
Access ~ Outcomes: Cystic Fibrosis Data: clinical information derived from the Cystic Fibrosis Foundation Registry • Study Population: 229,968 observations on 7823 patients from 2002 to 2011. • Geographic Access: realized travel distance from the zip code of each patient to the care centers. • Outcome measure: %FEV1, a common outcome for research in cystic fibrosis
Access ~ Outcomes: Cystic Fibrosis • Not a consistent relationship between geographic access and outcomes • Access is significant for some age groups but not for all • State based analysis shows other factors impact outcome levels The distribution of %FEV1 & CF center locations in Georgia
Policy & Network Interventions A policy intervention is a type of an action that involves design, revision, implementation or translation of a health policy for reducing costs and for improving health outcomes, healthcare access and quality. A network intervention refers to an action that involves altering an existing network of care, including networks consisting of medical facilities.
Policy Interventions • Improving Access to Pediatricians for Medicaid Population • Considered policies that would: • Improve patients’ mobility. • Increase percentage of physicians accepting any Medicaid patients. • Increase percentage of caseload physicians devote to Medicaid patients. • Simulate policy change by altering inputs in the access measurement models. • Evaluate impact at the state-wide and local level with respect to access and health outcomes.
Network Interventions Research Question: How can the existing network be modified to meet specified goals? Evaluation Criteria: Equity, Effectiveness, Efficiency Interventions • Open new facilities • Expand hours • Mobile clinics, telemedicine
Cost-Effectiveness: Telemedicine Telemedicine: • Originated in the Netherlands in the early 1900s • More than 100 definitions of telemedicine (WHO, 2010) • Time Magazine has called telemedicine “healing by wire” • Countries of implementation: • Almost everywhere on the globe! • Provider-driven implementations in the US (e.g., VHA) • Example of an implementation: • Tele-ophthalmology at VHA • Cost-effectiveness: Diabetic Retinopathy Screening
Cost-Effectiveness: Telemedicine • Step 1: Model Individual Disease Progression • Data: Sample of veterans with diabetes (Atlanta) • Model: Markov Decision Model • Step 2: Simulate individual disease profiles • Data: VHA & General parameter input • Model: Estimated Disease Progression Model • Step 3: Compare traditional to telemedicine • Simulate screening with and without telemedicine • Utility measures: Quality Adjusted Life Years (QALY) vs. costs of the program
Cost-Effectiveness: Cost vs. QALY Ratio • Cost-effectiveness for pool sizes of 3500 or larger (~9000 VHA patients in Atlanta area) • Cost-effectiveness for patients between the ages of 50-80
Pediatric Asthma Our research spans multiple directions, including deriving a set of baseline measures for asthma care, linking access to outcomes, and identifying care pathways in utilization and cost. The end point is to design policy and network interventions to improve health outcomes and access with limited resources.
Baseline: Utilization, Cost & Treatment Objective: develop a set of baseline metrics for pediatric asthma to be used in designing and evaluating interventions to have the greatest impact with limited resources. Pilot Study: Children population with Medicaid insurance ages 4-17 in Georgia, 2009
Baseline: Utilization Metrics by Race & Age The Other population (e.g., mostly Hispanic) has the most visits per patient and the African American population has the most patients per 1000 children on Medicaid. There are no differences in visits per patient, but the number of patients per 1000 children decreases with age.
Baseline: Cost Metrics by Race The Other population has the highest charge per visit, followed by the African American population. Payment amount and prepaid value show no significant differences. The Other population has the highest charge per enrollee per month, followed by the African American population.
Baseline: Treatment Control Metrics Fulton county and the surrounding areas have the lowest medication ratio in the state. The African American population has a lower medication ratio than the other two populations, indicating a lower use of long term controller medication.
Care Pathways: Utilization & Cost Objective: To identify underlying care pathways and to visualize the utilization relational system for pediatric asthma care in the Medicaid system using large patient-level claims data. Pilot Study: Children population with Medicaid insurance ages 4-17 in Georgia, 2009
Care Pathways: Cost to the Medicaid System
Acknowledgements Supporting Institutes and Organizations • National Science Foundation (CAREER Award) • Institute of People and Technology • Children’s Healthcare of Atlanta Research Team IT Staff: Matthew Sanders and Paul Diederich Postdoctoral fellow: Dr. Monica Gentili Undergraduate students: Sarah Drath, Pravara Harati, Qiming Zhang, Sean Monahan PhD Graduate students: Erin Garcia, Ross Hilton, Ben Johnson, Kevin Johnson , Zihao Li, RodriqueNgueyep, Richard Zheng
Contact Us • Nicoleta Serban email@example.com 404-385-7255 • Julie Swannjswann@isye.gatech.edu or 404-385-3054