# Presentation: 707006

## The Case for Clinical Repositories as a Data Source for Research

- Clem McDonald, MD
- Director
- Lister Hill National Center
- for Biomedical Communications
- National Library of Medicine
- National Advisory Research Resources Council (NARRC) Meeting
- January 18, 2007

## Clinical Repositories 

- What do they do?
- What could they do?

## Why Clinical Repositories

- Many sources of electronic data  in an institution
- Labs, radiology, Pharmacy, MD orders, EKGs,   Dictated reports, Radiology images, etc, etc, etc
- Most of these sources can deliver this data via HL7  messages to another computer
- Repository is a database that provides a unified and simple access to all of this data in a unified view.

## What Data is Available Within the Institution

- Lab data (almost always electronic)
- Medication orders in patients
- Radiology reports (text)
- Pathology reports (text)
- Dictation (discharge summary)
- EKGs
- Cardiac echoes
- Endoscopy
- Obstetrical Ultrasound
- Nursing Observation

## More Data – “Outside” the Institution

- Event Data – Coded diagnoses and procedures
- Tumor registries – whole country
- Cardiology data bases (ACC, ATS, etc.) – whole country
- Federal ESRD -base
- Out patient medications – From pharmacy benefit managers.  Rx.Hub
- More

## Potential Availability Still More

- Medicaid – procedures, diagnoses and drug use
- Medicare – Diagnoses, procedures and (now) medication use
- Lots of special federal collection instruments – Nursing home, disability, Medicare introduction, etc.

## Why codes for observations so important  

- The observation is not defined by the field as in typical in research and policy data bases

## Flat Data Set(Analytic Conceptualization)

| Pat ID | Name | surgery date | Hgb | DBP
 | # of 
BPU | Bypass 
Minute | Cholest |
| --- | --- | --- | --- | --- | --- | --- | --- |
| 1234-5 | Doe Jane | 12May95 | 13 | 95 | 3 | 80 | 180 |
| 9999-3 | Jones T | 1Aug95 | 12.5 | 88 | 2 | 90 | 230 |
| 8888-3 | Doe Sam | 4June95 | 16 | 78 | 0 | 80 | 205 |

## Why observation codes important

- The observation ID defined by a “pointer” to a master table – as follows

## Stacked Data Set Application Conceptualization

| Pt ID | Relevant  Date | Observation ID | Value | Units | Normal Rang | Place | Observer  |
| --- | --- | --- | --- | --- | --- | --- | --- |
| Doe J | 12-May-95 | Hemoglobin | 13 | mg/dl | 12.5-15 | St Francis | Dr Smith |
| Doe J | 12-May-95 | Hemoglobin | 11.5 | mg/dl | 12.5-15 | St Francis | Dr Smith |
| Doe J | 12-May-95 | Dias BP | 95 | mm/Hg | 80-140 | St Francis | Dr Smith |
| Doe J | 12-May-95 | Dias BP | 110 | mm/Hg | 80-140 | St Francis | Dr Smith |
| Doe J | 13-May-95 | Bypass minutes | 80 | min |  | St Francis | Dr Sleepwell |
| Doe J | 12-May-95 | Cholesterol | 180 |  |  | St Francis | Dr Bloodbank |

**Operational Data Base: One  Record Per Observation**

## For Repositories – Need to Think in a Different Data Structure.

-  Instead of dedicating one data field (in a visit record)  –  per result as is the common model in clinical research
- Dedicate a record per result.
- That structure is found in every lab, repository, pharmacy system
- You will never find a field for hemoglobin or cholesterol – or for penicillin V
- The record carries extra pieces of information about each value as follows

## Limits and Issues Depending Upon How the Data is Represented

- Things to be aware of

## Clinical Information Comes in Multiple Forms, Each with its own Issues

- Quantitative – e.g. Maximum calf circumference, Serum calcium
- Attend to units – and possible need to convert
- Special forms ( 1:128, > 10,  1-5 , codes and mixed with numeric's)
- 2.	Ordinal “measures” – e.g. Glasgow eye opening score
- Answers  likely to be “fixed” text or localized codes
- 3.	Nominal (football jerseys) – e.g. Blood culture results
- Same issues as 2.
- But require small amounts of labor compared to direct manual capture

## Narrative Text Can be Good

**The Good:**
**Easy to record/capture and use**
_**Can**_** be searched for text patterns**
**Some success in finding specially-**_**targeted**_** with simple NLP **
**The Bad:**
**Usually requires some  human review of retrieved records**
**Still light years faster than chart review**

## Document Images, Clinical Images and Tracings 

**Fast access for human review**
**Access to original data – esp. with tracings**
**Human assisted measurements of biologic images**

## By-Patient and Cross-Patient Access

- Clinical repositories usually justified for clinical care – so data is organized by patient for clinician review
- May lack efficient cross-patient access as needed for research
- Three kinds of problems:
- They may not have the right index structures or computer power for searching
- The may not have tools for non-programmer access per query
- The data may be a mess inside – good enough for display to a human but not for automatic searching

## Repositories have Different Scopes 

- Local clinical data only
- Local clinical data plus administrative data (Very Useful)
- Local data supplemented  with “external data” some of which may be “internal”
- Tumor registry (local – state)
- ACC data – Local – more
- Social Security death tape – NO INSTITUTION SHOULD BE WITHOUT ONE
- Medicaid?
- Other
- Community wide repository (RHIOSs)

## Research Uses

- Find potential cases for studies (local)
- Review candidates for study eligibility before trying to enroll (even with no search capabilities)
- Obtain numbers and statistical characteristics of potential candidates for grants (local)

## Research Uses – More 

- Estimate variance for sample size analysis
- Track outcomes – (labs – death) – longitudinal studies/Cost-benefit studies (local)
- Epidemiologic studies (esp. with community scope)
- Obtain tissue (through pathology reports)
- Link phenotype with genotype (if also collecting genetics)

## Problems with Today’s Research Strategies

## Not Enough Research Data

- Clinicians are faced with zillions of decisions
- Research helps only some  of them
- Preventive decisions – but  even for  some of these (pneumonia vaccine)  data are soft
- Many cardiovascular interventions
- Some anticoagulation interventions
- Little help with special circumstances – age, co-morbidity
- Almost no data for decisions about diagnostic testing, surgery, use of devices
- Almost no help regarding cost benefits

## Deeper Problems 

- Sample size requirements for trials become difficult/impossible when
- Event rates are small
- When difference between treatment and “control” are small:
- often the case is comparison of new with best existing treatment
- We want to  quantify the *amount* of benefit  accurately for cost benefit analysis A

## Deeper Problems - More 

- A study with 10% event rate and 25% difference (big difference) can require enrollment of 10,000 patients.
- To be 95% sure of finding one case of finding with event rate of 1/25,000 need to observe 63,000 cases (e.g. rhabdomyolysis)
- Trials can’t cover the entire water front

## How to Get More for Less

- Collect less on greater number of patients
- _Use Repositories_
- To find patients for trials
- For retrospective analysis of rare events
- For post-marketing drug toxicities
- To supplement data collection in traditional clinical trials
- For gathering outcomes and follow up in longitudinal studies and large simple trials (Community repositories)
- To find tissue (paraffin) for study

## Repository Examples 

- Partners analytical database (Murphy SN)
- Considering labs alone – more than 125 different labs interfaced
- Uses LOINC as lingua franca for gluing different results together
- At LEAST (old data):
- 2.5 million patients with clinical data
- 700 million clinical facts
**750 active researchers  7000 queries/year**

## More Examples

- The VA – mapping all of their lab tests to LOINC – so data can be pooled across hospitals.
- CRN – collaboration of 10 large “HMOs” for cancer research (Puget Sound, Kaiser, etc.) lab, radiology, drugs available from the collaborators  (Wagner, et. al)

## Community-Based Repositories 

- Memphis
- North West Indiana
- British Columbia
- Pediatric hospitals in Ontario
- North Jutland, Denmark
- Utrecht, Netherlands
- Central Indiana (Indianapolis) (INPC)

## INPC – What Is It?

- Centralized (federated) clinical repository  for central Indiana
- Data delivered from all major Indianapolis hospital systems as  HL7 ver. 2.x
- Treat patients from each institute as separate institution
- Funded by NLM (INPC) and NCI (SPIN)
- Open Source software

## What is it For?

- Clinical care
- Eligible providers can access clinical data from all sources in one view when patient is seeking their care
- Public Health
- Bio-surveillance
- Reportable disease reporting
- Quality
- Research (Today’s subject)

## Who Contributes Data?

- Hospitals
- Five Indianapolis Hospital Systems (total of 15 separate hospitals)
- Stand alone labs
- Payers
- Medicaid  (whole state)
- Encounter ICD & CPT + meds
- 150 M encounters  75 M prescriptions
- WellPoint (largest healthcare company in US – more patients than Medicare)

## Who Contributes Data? – More  

- Tumor registry (De-identified research only – whole state) – 550 K cases  (another “institution”)
- Death tapes (_Important_)
- Indiana State Public Health Department
- Social Security ( 80 million )

## INPC Storage Strategy

- Central Community database resource and management of mapping, etc.
- Standardized data structure – all use same software and observation codes.
- Data for each organization in its own physical files
- Combine on-the-fly when needed
- Patient linking needed – because no national ID

## Slide 33

## All Hospitals Contribute – At Least 

- Lab results
- Cardiology reports
- Tumor registry data
- In-patient medication orders (committed)
- TEXT IS GOOD
- Discharge summaries/admission summaries Operative notes
- Radiology reports
- Pathology reports – gets you to existing tissue
- Some Contribute All

## 2006 INPC Data Flows and Content

- Flows
- More than 150 HL7 message streams
- More than 100 million separate HL7 messages per year (380 million OBX’s)
- Add about 80 million results per year
**HL7 ver. 2  works!!!!**
- Content
- 6 million distinct patient _registration records ( 3 M)_
- 850 million discrete  results
- 50 million radiology images
- 17 million narrative reports

## How does the Data Flow from Source to RHIO Repository?

- HL7 messages delivers most of the clinical data.
- DICOM for radiology images.
- NCPDP for outpatient pharmacy.
- LOINC – provides standard codes that define the content of each delivered result.

**http://www.regenstrief.org/loinc**

## Radiology Images - Thumbnail

**Radiology Images - Thumbnail**

## BIG

**BIG**

## BIGGER

**BIGGER**

## BIGGEST 2800 x 2000

**BIGGEST 2800 x 2000**

## EKG Discrete Variables

**EKG Discrete Variables**

## EKG Tracings

**EKG Tracings**

## Flow Sheet for Blood Count

**Flow Sheet for Blood Count**

## Slide 45

## Orders

## Report Delivery to Office Practices

- 1300+ practices (3800 MDs) at present
- 90% of the active care providers in 9 county region
- Many opportunities to practice access for

## Repository Research Uses 

## INPC Use for Research

- 100’s of queries for grants/year, e.g. to estimate # of cases available for study. To find cases.
- Pull supplemental data for many clinical trials
- Used in 80% of human subjects studies at some point in study
- Remind MDs of studies underway (recruitment)
- Database studies – the greatest:
- Erythromycin and pyloric stenosis 1

- 1

- 1 Mahon BE, Rosenman MB, Kleiman MB.  Maternal and infant use of erythromycin and other macrolide antibiotics as risk factors for infantile hypertrophic pyloric stenosis. J Pediatr. 2001 Sep;139(3):380-4.

## Tissue Access

- SPIN project
- NCI-funded Collaboration among IU/Regenstrief Harvard, University of Pittsburgh, UCLA
- Use query to find clinical cases of interest.  Pathology reports provide the link to tissue – paraffin block – 4 M in Indianapolis

## Special Query Capabilities 

- Access to more than 10,000 distinct variables
- Built in de-identification processes
- Dates truncated to year
- Forbidden fields removed
- Text reports are scrubbed  **(Examples)**
- Build cohort twin databases then statistical analyses.

## Special Query Capabilities – More 

- Each kind of text report is just another variable – Google-like searches on text, more traditional criteria for numeric and coded variables search
- Tie in to R-(RECCOMENDED) and pre-packaged statistical routine
- User can do statistical analyses without ever touching any data

## SPIN Build Data Set Query

## SPIN Look at Data Set

## SPIN Look at Individual Scrubbed Report

## SPIN Survival Analysis

## How do we Glue Data Together?

- Use linking algorithms to tie patients – based on registration data
- Use LOINC codes and mapping tools to tie equivalent variables together

## How do we Get There?

- Glue data from many sources together
- First from your institution
- Then other related data bases (hospital is full of them from tumor registry to heart attack database)
- Rx.Hub – 60% of the nations prescriptions
- Don’t forget Death tapes
- Push for community data melds – they could revolutionize clinical ...

## How do we Get There? – 2

- Force connections between clinical trials systems and institutional systems
- The current state makes no sense
- Demand HL7 bidirectional registration and resulting transmission
- Push for use of HL7 clinical trial segments in orders and reporting

## How do we Get There? – 3 

- If combining independent sources
- Need linking routines (NIH should make good tools publicly available)
- Combine for clinical use – de-identify for research use (limited data sets)
- Make well-tested de-identification tools publicly available

## How do we Get There? – 4

- Develop national catalogues for variables and questionnaires. Contribute new variables to the catalogue when existing ones really won’t do.
- Use LOINC – as the catalogue – try it, you’ll like it

## LOINC and RELMA Web Site – No Cost Downloads

- Type in LOINC at Google
- Pig

## Challenges Exist

- Each Study  (and phase) needs ID => Institutional study database
- Ordering systems need option for adding trial ID and phase to the order
- HL7 has segments defined for these – not hard, just need to be articulated

## Challenges Exist – 2 

- Catch 22's – e.g., recruitment
- Defeats the efficiencies intrinsic to repositories
- Need more rational rules

## Challenges Exist – 3 

- Managing (and retrieving consents)
- Solvable with scanning and proper workflow

## Medicare & Medicaid – Miracles 

- Could follow-up of deaths via SS death tapes (here now)
- Find outcome events and (Medicare patients) in Medicare database
- Track medication and intervention use (Medicare patients) Medicare database
- Similar opportunities with Medicaid databases

## Research Will Still be Hard

- Clinical systems will not carry all data of interest
- Repositories are not magic.
- But we could collect less if we used the available clinical data where it sufficed and focused the question on strong outcomes

## ASIMO at CES 2007

- _[htthp://www.youtube.com/watch?v](http://www.youtube.com/watch?v=UOWYIjbKDcc)__[=UOWYIjbKDcc](http://www.youtube.com/watch?v=UOWYIjbKDcc)_

## The END