# Presentation: 194322

## NHII Operating in a Community 

**NHII Operating in a Community**

- J. Marc Overhage, MD, PhD
- Regenstrief Institute
- June 30, 2003

- NATIONAL HEALTH INFORMATION INFRASTRUCTURE 2003DEVELOPING A NATIONALACTION AGENDA FOR NHII

## The Indiana Network for Patient Care (INPC)

**The Indiana Network for Patient Care (INPC)**

- An _operational_ community wide electronic medical record

## Slide 4

## Indiana Network for Patient Care

**Indiana Network for Patient Care**

## Slide 6

## Indiana 	IAIMS		Initiative

- 3

**Indiana ****	****IAIMS****	****	****Initiative**

## Regenstrief Institute

**Regenstrief Institute**

## Initial RMRS Aims

- Eliminate the logistic problems associated with the paper record
- Standardize the care process.  Deliver information in a more organized and useful way. Actively process this record and provide decision support to clinicians.
- Analyze and understand the data to improve the health of populations

## INPC Project Goal

- 	Demonstrate the feasibility and benefit of a community wide electronic medical record system in acute care situations.

## Resistance is futile!

*You will be assimilated*

- INPC Project Motto

## INPC Hospital Geographic Coverage

## INPC Users

- Most central Indiana med/surg hospital EDs
- Hospital based providers (expanding)
- Primary Care (85 providers/20 locations)
- Homeless care network
- Public school clinics
- Marion County Health Department
- Indiana State Department of Health

## INPC functions

- Results retrieval
- Clinical messaging/document delivery
- Data entry
- Reporting
- Clinical decision support
- Public health surveillance
- Medical reference access

## INPC database organization

- St.
- Vincent’s

- Community
- Hospitals

- Wishard

- Clarian
- Health

- Global
- Patient
- Index

- Dictionary

- MCHD

- Global
- Doctor
- File

- St.
- Francis

- VA

- LabCorp

- IU
- Medical
- Group

**16**
**Practices**

**Statewide**

- Data entry

- Data entry

**Notes:**
Reportable conditions is in a separate database

## Slide 16

| Datum | Coverage | Penetration |
| --- | --- | --- |
| Chief Complaint | Indianapolis MSA | 98% |
| Lab orders | Indianapolis MSA | 30% |
| IP Diagnoses | Indianapolis MSA | 98% (179K/yr) |
| OP Diagnoses | Indianapolis MSA | 10% |
| Laboratory Results | Indianapolis MSA | 90% (46.5M/yr) |
| IP Medications | Indianapolis MSA | 95% (48.2M/yr) |
| Radiology reports | Indianapolis MSA | 90% (1.3M/yr) |
| Outpatient Prescriptions | Indianapolis | 70-80% |
| OTC Medications | Indiana | 10% |
| Nurse hotline | Indianapolis MSA | 10% |

## Current
HL7 messages for most as the envelope
DICOM messages for images as the envelope
LOINC for laboratory results content
CPT-4 for procedures content
ICD-9 for diagnoses content
NDC and RxNorm for medications content
Anticipated
Organisms for microbiology content

- Current
- HL7 messages for most as the envelope
- DICOM messages for images as the envelope
- LOINC for laboratory results content
- CPT-4 for procedures content
- ICD-9 for diagnoses content
- NDC and RxNorm for medications content
- Anticipated
- Organisms for microbiology content

- Clinical Data Standards

**Notes:**
Could not be successful without standards. Tommy Thompson’s support and activities including connecting for health and eHI are important

## Slide 18

## Patient Linkage

- When data is returned from multiple sites, it will need to be combined and linked.
- For example, if “John Doe” is seen for various aspects of his colon cancer at different institutions, the data must be aggregated:

- ED

## Patient linkage approach

- Create global registry
- One record per assigned patient number per institution
- Create logical links between each of these records
- Match on social security number, patient name, birth date and gender
- Use Sideli/Friedman algorithm1 for near name matches

- Sideli RV  1992

## Global Patient Registry

- Assigning
- _Authority _	_Global #_	_Local Pat #_	_Patient Name_	_Birthdate_	_Sex_
- Hospital A	99-1	231456	Sinkwell, Ralph J	12-2-59	M
- Hospital B	123-0	A47239	Sinkwell, RJ	2-12-59	M
- Hospital A	99-1	1032115	Sinkwell, Ralph	12-2-59
- Hospital C	101-0	A3276	Fredrick, Alice	4-14-78	F
- Hospital A	101-0	2314590	Fredrick, Alyce	4-14-78	F

## Slide 22

## INPC - Confidentiality

- While numerous measures in in place to protect confidentiality of patient’s data, the provider has to know who the patient is.
- Secure physical network
- Encryption
- Authentication
- Agreements
- Device controls

**Notes:**
HIPAA 19-24 identifiers which must be eliminated in order to share data. A one-way hash functions in much the same way as our fingerprint: Our fingerprint uniquely identifies us, but we cannot be reproduced from our fingerprint. Hash functions are sometimes called “digital signatures”

## INPC Data Access

**Notes:**
The data are delivered (pushed) in paper form and is available for on-line retrieval.

## Slide 25

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## Details

**Patient Name**

**Details**

**Report**

**Image**

## Slide 30

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## Slide 37

## Data reuse

- Clinical care
- Emergency room
- Primary care
- Inpatient
- Public health (state and local HD)
- Immunization registry
- Reportable conditions
- Surveillance
- Health services research
- Clinical research
- Accreditation reports

## Reportable condition processor

- To Infection
- Control

- Inbound
- HL7

- Potentially
- Reportable

- Reportable
- Condition

- To Public
- Health

- Reportable
- Conditions
- Databases

- Abnormal flag,
- Organism name
- in Dwyer II, Value above threshold

- Compare to Dwyer I

- Record Count
- as denominator

- E-mail
- Summary

- Realtime

- Daily Batch

- Print
- Reports

**Notes:**
Generalize what is on the right
What is on the left is difficult to generalize because it has to be integrated with and optimized for performance

## Shigella Outbreak: Timeline

## Integrating Non-traditional Data

- The sales of OTC Electrolytes are a result of two outbreaks:  RSV (in gold) and Rotavirus (in pink)

**Notes:**
In this slide, we show the middle, anomalous year (1999-2000) for one region as an example (all the regions show the same phenomena).  Note that an outbreak of RSV (in gold) precedes the Rotavirus outbreak (in pink).  The two outbreaks together explain the sales of electrolytes (in blue).

The general principles to be learned are:
(1) That we have to look carefully at what diseases in the community might be causing spikes in unorthodox data, especially before measuring timeliness!  
(2) That the gold standard must contain all diseases that can cause a spike in the unorthodox data being measured in order to avoid a potentially spurious and erroneous conclusion. 

By including all relevant diseases in the gold standard,, we avoid the risk of a spurious correlation because the disease actually causing the spike is not visible.  To be very concrete, we could potentially say that OTC electrolyte sales seem to precede Rotavirus by 3 months if we were to look at this graph without knowing about the gold RSV curve.

## Links to knowledge sources

**PraxisMD**

## Slide 43