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Impact of Physician Order Entry System and its Associated Automation of Order Sets on Lab Orders. The Ohio State University Medical Center. OSUMC – Profile Information. 4 Hospitals, 36 primary and specialty care sites 897 Beds 42,930 Inpatient visits 697,843 Outpatient visits

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impact of physician order entry system and its associated automation of order sets on lab orders

Impact of Physician Order Entry System and its Associated Automation of Order Sets on Lab Orders

The Ohio State University Medical Center

osumc profile information
OSUMC – Profile Information
  • 4 Hospitals, 36 primary and specialty care sites
  • 897 Beds
  • 42,930 Inpatient visits
  • 697,843 Outpatient visits
  • Physician Order Entry (POE) System has 2,057 physician users and 5,998 staff users
poe at osumc
POE at OSUMC
  • 100% of Orders Entered Electronically
  • Decision Support tools include:
    • Allergy Checking
    • Drug Interaction
    • Order Duplicate Checking
    • Weight-Based Dosing
    • Drug Route Restriction
  • 450 Evidence-Based Order Sets
  • Robust Interfaces with Most Ancillary Departments like Lab and Pharmacy
slide4

Information Warehouse at the OSUMC Captures:

  • Patient Demographic data
  • Patient Accounting data
  • Patient Management data
  • Lab Results and critical values
  • Blood bank data
  • Physician Order Entry data
  • Discharge Instructions
  • Readmissions
  • 450 Evidence-Based Order Sets
  • Robust Interfaces with Most Ancillary Departments like Lab and Pharmacy
osumc information warehouse conceptual view
OSUMC Information Warehouse: Conceptual view

Data Acquisition

Data Transfer

Information Warehouse

Data Access

Encounter

DM

ICU

DM

Readmt

DM

SMS Patient

Management

HCC Cost

Accounting

Data

Warehouse

E

X

T

R

A

C

T

L

O

A

D

Pharmacy

DM

ED Visits

DM

Mom/Baby

DM

SMS Patient

Accounting

Multidimensional analysis/Data Miming

Rejection

DM

POE

DM

Pt Sat.

DM

Lab

DM

Blood Bank

DM

Adhoc query

SMS

COE

Web reporting & dashboard

Sunquest

Lab System

Census

DM

Summary

Data Marts

De-identify

Revenue

DM

Patient

Satisfaction

OHA

DM

Outside data for benchmarking

Ohio Hospital

Association

5

slide6

Integrating POE DM with Other DMs

Encounter Data Mart

  • Order set utilization, analysis and quality impact by DRG, ICD9 Procedures, Discharge diagnoses
  • Order set utilization and Outcomes analysis

Lab Data Mart

    • Critical results and treatment orders
    • Impact of POE on lab orders and compliance
slide7

Over all impact of order sets on Lab orders: Results

Comparison of average number of lab orders per patient for May 2000 (pre order set*) and May 2002 (post order set*) for all DRGs shows a 69% increase

* Note: Order set implementation for best practice guidelines went into effect in Jan 2001

slide8

Impact of order sets on Lab orders by DRG: Results

Comparison of average number of lab orders per patient for May 2000 (pre order set*) and May 2002 (post order set*) for DRG 302 (among the top 10 DRGs by average lab orders/patient) shows a 48.43% increase

* Note: Order set implementation for best practice guidelines went into effect in Jan 2001

slide9

Impact of order sets on Lab orders for Cardiology related DRGs: Results

Comparison of average number of lab orders per patient for May 2000 (pre order set*) and May 2002 (post order set*) for the top two Cardiology DRGs by patient volume shows an average increase of 175%.

* Note: Order set implementation for best practice guidelines went into effect in Jan 2001

slide10

Impact of order sets on Lab orders: Conclusions

  • Overall Lab Orders have increased by 69%
    • Increase can be significantly higher or lower for specific DRGs
      • This significant difference is due to number of lab orders in ordersets used for those specific DRGs
  • DRGs with more lab orders in ordersets have a greater percent increase
  • Increase in lab orders can be attributed to increased orderset use
    • Orderset Compliance
    • Best Practice Guidelines