1 How Informatics Can Drive Your Research Barry Smith

Four Lectures TodayIntroduction 2/15How Electronic Health Record (EHR) Data Can Drive Your Research 2/22 Case Study: Pain and Mental Health 2/29 Case Study: Alzheimer’s Disease 2

Agenda How to 1. use data gathered in the clinic 2. use (and find) publicly available clinical data 3. use (and find) publicly available biological data relevant to clinical work 4. use data to address NIH funding requirements Albert Goldfain: Bad and good practices 3

Agenda How to 1. use data gathered in the clinic 2. use (and find) publicly available clinical data 3. use (and find) publicly available biological data relevant to clinical work 4. use data to address NIH funding requirements Albert Goldfain: Bad and good practices 4

New York State Center of Excellence in Bioinformatics & Life Sciences R T U New York State Center of Excellence in Bioinformatics & Life Sciences R T U How informatics can help the clinician Δ = outcome observation & measurement data organization diagnosis use add Generic beliefs verify treatment

New York State Center of Excellence in Bioinformatics & Life Sciences R T U New York State Center of Excellence in Bioinformatics & Life Sciences R T U How informatics can help the researcher observation & measurement data organization hypothesis use add Generic beliefs verify further R&D (instrument and study optimization) Δ = outcome

The meaning of life Choose your disease and devote your life to finding a cure Form a cohort of patients Assemble maximally accurate data for all the patients in this cohort Use this data to forge links with other researchers who find your data valuable Important: In the era of genomic medicine, only structured data is valuable 7

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The 3 big problems ICD Free text EHR coding systems 9

The 3 big problems ICD –used for billing –used sloppily, and inconsistently, and … –is a poorly structured coding scheme Free text EHR coding systems 10

The 3 big problems ICD Free text –what your colleagues have been using to create all those paper records –is not structured –you will need to use high quality codes EHR coding systems 11

The 3 big problems ICD Free text EHR coding systems –EHR’s are used inconsistently, and sloppily –with lots of free text –but most of all: there are many different systems 12

The problem of data silos

You need to create a patient database of your own 14 And populate this database through manual chart review Drawing in data from all relevant sources (both patient data and biology data) But how to do this? You will be creating an Excel spreadsheet But how to do this? (How will you avoid silos?)

Problems with databases How to find and integrate other people’s data? How to reason with data when you find it? How to understand the significance of the data you collected 3 years earlier? Part of the solution must involve use of standardized coding schemes 15

An example: SNOMED-CT 16 Systematized Nomenclature of Medicine Very large, internationally maintained Covers the whole of medicine Still poorly and sloppily used (mainly for diagnosis and treatment; not for etiology, signs, symptoms) Built by pathologists Will help to structure your data

Examples of Buffalo sources Roswell Biosample repository Pharmacology data (Medicare/Medicaid) Buffalo Ontology Group 17

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You will need to embrace this strategy in any case if you want to get funding NIH Mandates for Sharing of Research Data Investigators submitting an NIH application seeking $500,000 or more in any single year are expected to include a plan for data sharing ( 22

You don’t need to become a computer scientist for this strategy to work Werner Ceusters (CoE, Psychiatry) Jason J. Corso (Computer Science) Alexander D. Diehl (Neurology) Albert Goldfain (Blue Highway Inc.) Alan Ruttenberg (Director, UB Clinical and Translational Data Exchange, Dental School) Alexander C. Yu (Infectious Disease) + Barry Smith 23

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Next week 2/15How Electronic Health Record (EHR) Data Can Drive Your Research On the pitfalls and the promise of EHRs with Peter Winkelstein 25