Leveraging Existing Health Data to Augment Information Collected by Cancer Registries

Transcription

1 Leveraging Existing Health Data to Augment Information Collected by Cancer Registries Joan L. Warren Ph.D. Healthcare Delivery Research Program National Cancer Institute CARCR Conference December 8, 2015

2 NU1 Historical Context Population-based cancer registries began in the 1970s. At that time: Cancer was usually considered a fatal diagnosis Most patients were treated in hospital Registrars collected details about cancer including site and stage- local, regional, and distant Information about the cancer was collected on paper by inperson abstraction of the hospital record 2

3 Slide 2 NU1 Why do this. These people know this in spades. NCI User, 12/3/2015

4 Current Context Information about the cancer includes stage (TNM, collaborative) and tumor markers (rapidly evolving) Many cancers are now considered chronic diseases Many cancer patients are treated outside the hospital Most information about the cancer is electronically recorded (80%+ of pathology reports are electronic) Watson is being used to aid treatment decisions 3

5 Current Context: Ideal of information needed about cancer- what is collected by registries Cancer control continuum Screening/ Detection Use of screening tests Type of screening tests Diagnosis Mode of detection Stage Biomarkers Specialty consultation Treatment Surgery Chemotherapy (IV and oral) RT Comorbidities Adverse events Survivorship Survival Surveillance tests Late effects of treatment Recurrence/ Progression Patient QOL End of Life Palliative care Hospice use Across the spectrum: cancer quality and costs RED=currently collected by registries; italics= not completely collected

6 Assessment of Current Status Registries currently do not include much of the important information needed to assess the burden and outcomes of cancer The information that is needed does not reside with a single, accessible source How do we fill the gaps? 5

7 There Are a Growing Number of Data Resources

8 Focus of Presentation Health Claims Government Proprietary (Optum, Truven, Fairhealth, HCCI) State-Level Data - All Payers Claims Data CRN Data Electronic Health Records (EHRs) 7

9 Disclaimer Augmenting data can be populating fields on the registry data or can involve facilitating linkages to data that will not be incorporated into the registry data I am not with the SEER program My comments reflect my perspective about health data, not that of NCI 8

10 HEALTH CLAIMS 9

11 Growth of Health Claims: from None to Billions in 30 years Over the past 30 years, there has been dramatic change in use of health data: Availability Accessibility Technology Acceptance Late 1980s The internet is introduced into mainstream 1991 NCI funds first grant using Medicare data 1993 SEER- Medicare data first linked 1999 CRN first funded by NCI 2000s Explosion in # of analyses using secondary data 2012 Obama gives $200 million for Big Data Initiative % of physician offices have EHRs and 60% of hospitals have basic EHR

12 Government Sources of Health Claims Medicare data SEER-Medicare Medicaid data 11

13 SEER-Medicare Data PROS 1.8 million SEER cases linked to Medicare data Information about all covered Medicare services for FFS pts; longitudinal Defined population. Great enrollment information Deterministic linkage, 94% match. Updated biennially First linked 24 years ago. Used extensively by researchers. Over 1,350 publications. CONS HMO enrollment now 30% of pts (encounter data will be available in 2017) Under 65 population includes only people who are disabled and have ESRD Does not include services not covered by Medicare Data are not timely 12

14 Medicaid Data PROS 19% of US population. Important group to study- evaluate ACA CMS has a standardized format, Medicaid Statistical Information System (MSIS), that removes variability between state formats Good information about enrollment States have their own Medicaid data, much messier CONS Coverage policies vary by state. Recipients are on/off Medicaid Most states place Medicaid recipients in managed care (no encounter data) Linkage via CMS possible, state linkages may be problematic Data are not timely, questions about quality CMS changing Medicaid data system. New system available in

15 Proprietary Health Claims- PROs Large number of patients Cancer treatment patterns among patients < 65 year olds Medicare Advantage patients included Some Medicaid data Many of the analyses that have been conducted with SEER-Medicare could now be undertaken with linked registry-claims data covering all ages. 14

16 Proprietary Health Claims- CONs The data are not population-based, patchwork of patients. Do these patients reflect the underlying population? Denominator quality can vary Enrollment stability- lack of long-term follow-up Completeness of the data Cost of acquiring the data Linkage process not transparent Restriction on use of these data. Would others have access? Working out details of these types of relationships is not easy. 15

17 All-Payer Claims Data (APCD) State-level data Databases created by state mandate, that typically include data derived from medical claims, pharmacy claims, eligibility files, provider files, and dental claims from private and public payers. population-based In states without a legislative mandate, there may be voluntary reporting of these data 16

18 Availability of APCD 17

19 Availability of APCD 18

20 Availability of APCD in SEER Areas State Status as of Summer 2015 Connecticut Utah Washington No data are currently being released outside of the agency but they are working on a policy to do so. The state plans to release aggregate tabulations in the future. Not clear if the data will be used for research purposes. 19

21 Potential for APCD to augment registry data APCD have limited potential to augment registry data because: Limited number of states with functioning APCD programs Administrative challenges for obtaining the data Data likely messy with significant reconciliation required Ongoing litigation- Vermont Liberty Mutual 20

22 CANCER RESEARCH NETWORK 21

23 Cancer Research Network (CRN) The CRN is an NCI-funded consortium of non-profit HMOs that have research units; first funded in 1999 These HMOs provide care to ~11 million people, all ages included Encounter data have been linked to cancer registry data; can link to medical record The CRN is designed to conduct research on cancer prevention, early detection, treatment, and surveillance CRN also develops and uses standardized approaches to data collection, data management, and analysis across health systems.

24 CRN s Data and Virtual Data Warehouse (VDW) Automated medical record Administrative Data Membership Outside claims Patient scheduling Deaths Automated clinical data Outpatient visits Hospitalizations Emergency room Pharmacy Laboratory Home health care Radiology Pathology Cancer registry To protect patient information, each CRN site creates standardized datasets and stores these behind separate security firewalls at each participating own CRN site. The VDW consists of databases with a common set of standardized variables in each CRN site. For specific projects, each CRN site submits the needed variables to a centralized site

25 CRN data- PROs CRN data include persons of all ages, defined population Data are longitudinal and broad Standardized set of variables Data can be extracted from the EHR + claims HMO data have been already been linked to registry data

26 CRN data- CONs Does not include persons with fee-for-service coverage or in HMOs outside of the CRN Requires collaboration with a CRN investigator VDW data need to be cleaned for specific projects Data are expensive

27 ELECTRONIC HEALTH RECORDS (EHRs) 26

28 EHRs- the potential EHRs are designed for clinical care but can potentially provide data for research Dissemination of EHRs has increased rapidly Data can be reported in structured fields or in free-text Processing free text requires natural language processing 27

29 Key data about cancer that may be available from the EHR Cancer site Histology Stage at diagnosis Results from biomarker testing over time Other discrete data elements (helps with observational data) Functional status Performance score Recurrence Disease free interval Opportunity to capture text documents Clinical notes can be accessed with NLP

30 EHRs- the challenges Physicians must enter data for it to be available Stage is often entered in free text, challenge for registrars Software packages vary and are not all compatible The quality of NLP programs that read free text in the EHR varies Patients not in integrated health systems will have incomplete data 29

31 CONCLUDING THOUGHTS ABOUT AUGMENTING REGISTRY DATA 30

32 INFORMATION ABOUT CANCER BY SOURCE- INITIAL CARE PERIOD Registries Claims CRN Screening/Detection -Use of Screening YES YES -Type of Screening YES YES -Screening Results Med rec Diagnosis -Mode of detection some -Stage YES - Biomarkers some Med rec -Specialty consultation YES YES Treatment -Surgery YES YES YES -Chemotherapy YES YES -Radiation Therapy YES YES YES -Comorbidities YES YES - Adverse events YES YES

33 INFORMATION ABOUT CANCER BY SOURCE- LONG TERM Registries Claims CRN Survivorship -Survival YES YES YES -Surveillance tests YES YES -Late effects of treatment some some - Recurrence/Progression Med rec - Patient QOL End of Life -Palliative Care Med rec -Hospice YES YES

34 No one data sources provides complete information needed for population-based assessment of cancer treatment and outcomes Target the data resource that will provide the most information for the least amount of cost and administrative effort Currently, APCD and EHR data are the least likely to provide useful information but may offer great potential down the road

35 Criteria to Evaluate a Data Resource (Pt 1) PATIENT POPULATION (is there bias??) Number of cases, what percent of underlying population Representativeness of the patients (age, race, income, geography) Why are the patients included in the data Are there statistical methods to overcome incomplete information LINKAGE Deterministic vs. probabilistic Quality of the linkage variables Transparency of linkage process Repeated or one-time 34

36 Criteria to Evaluate a Data Resource (Part II) DATA Completeness, especially for important variables Validity Timeliness Accessibility/Limitations on Use Ease of Use Cost 35

37 Final Thoughts We are in the midst of an explosion of health data. It is relatively easy to produce numbers from existing health data. It is much harder to produce correct answers It is really important to understand the data- strengths and limitations. Validation studies are often needed. Look under the hood, kick the tires 36

38 THANK YOU! 37