Can Advanced Electronic Medical Records Make Patient Care Safer?

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Carnegie Mellon University 70-451 Management Information Systems: Spring 2012

Carnegie Mellon University 70-451 Management Information Systems: Spring 2012 1

Carnegie Mellon University

Saving Patient Ryan

70-451 Management Information Systems: Spring 2012

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Can Advanced Electronic Medical Records Make Patient Care Safer?

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Saving Patient Ryan

Can Advanced Electronic Medical Records Make Patient

Care Safer?

Zia Hydari

SMU Living Analytics Research Centre Seminar

July 2015

(Joint work with Rahul Telang and Bill Marella)

Working Paper: http://ssrn.com/abstract=2503702

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Acronyms

Meaning

EMR

Electronic Medical Records

CPOE

Computerized Provider Order Entry

PD

Physician Documentation

PSA

The Pennsylvania Patient Safety

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The Problem—Patient Safety

“Patient safety” can be defined as freedom, as far as possible, from

harm, or risk of harm, caused by medical management (as opposed

to harm caused by the natural course of the patient’s original

illness or condition).

1

(1) Great Britain House of Commons Committee, Patient Safety, Sixth Report, (2) Senator Bernard Sanders, Subcommittee on Primary Health and AgingMore Than 1,000 Preventable Deaths a Day Is Too Many: The Need to Improve Patient Safety(July 2014), (3) Bos, J. V. D. et al. The 17.1 Billion USD Problem: The Annual Cost Of Measurable Medical Errors. Health Aff 30, 596–

603 (2011).

Extent of Medical Errors in Hospitals

• Third leading cause of death in the US

2

• Hundreds of thousands harmed or at risk of harm

• Tens of billions of dollars in cost to society

3

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Health Information Technology (IT)

1. Federal HITECH Act funding with the purpose of reducing costs and improving outcomes

Sources: S&P Capital IQ Industry Surveys Healthcare : Facilities, p. 21 (December 2013) ; Essentials of the U.S. Hospital IT Market, HIMSS Analytics; “Hospital IT spending jumps high”, Healthcare IT News (August 2013), HITECH abbreviates Health Information Technology Economic and Clinical Health,

• Federal incentive payments $27 billion for health IT adoption

1

• Hospitals investing significantly in health IT

-

IT spending 20% of hospital capital spending

-

Healthcare IT market $22 billion in 2012

• EMR impact on patient safety major societal concern

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Related Literature on Health IT Effects

Reference

Outcome

Study Period

Miller and Tucker (2011)

Neonatal mortality

1995-2006

Agha (2014)

Mortality, complications, readmission,

ADE for Medicare patients

1998-2005

McCullough, Parente,

and Town (WP 2013)

Mortality in fee-for-service Medicare

patients

2002-2007

Parente and McCullough

(2009)

3 Patient Safety Indicators (PSI) for

Medicare patients

1999-2002

Freedman, Lin, and

Prince (WP 2014)

4 postoperative PSIs from Nationwide

Inpatient Sample

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Health Policy Stakeholders’ Assessment of Evidence

“… current literature is inconclusive regarding the overall impact of

health IT on patient safety” (IOM, 2012)

IOM. 2012. Health IT and Patient Safety: Building Safer Systems for Better Care. Accessed July 30, 2013. http://books.nap.edu/openbook.php?record_id=13269. IOM. 2011. Health IT and Patient Safety Appendix B: Literature Tables (online). http://www.nap.edu/html/13269/app_b_tables.pdf.

US Senate Subcommittee on Primary Health and Aging -More Than 1,000 Preventable Deaths a Day Is Too Many: The Need to Improve Patient Safety(July 2014) * Peter Pronovost pioneered checklist protocol for reducing the deadly infections associated with central line catheters (bio).

“… most of the outcomes are measured using billing data ... and they

are truly, near worthless ...” (Peter Pronovost, MD, 2014)*

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Why is existing evidence inconclusive about

effect of EMRs on patient safety?

Partly lack of reliable patient safety data and

partly unconvincing analysis

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Patient Safety Data For this Study

• Comprehensive

-

Broad categories of patient safety events

-

All patient ages, diseases, and conditions

-

Inpatient and outpatient events

• State law mandates accurate reporting

-

Hospitals promised data confidentiality

-

Hospitals penalized for failure to report

-

Whistleblower protection

• PSA collects all data

• Record identifies category, date, hospital, and includes text report

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Advanced Electronic Medical Records

• Computerized Provider Order Entry (CPOE)

-

Process and transmit medication, laboratory, and radiology orders digitally

-

Integrate patient data, decision support, best practices

1 _{into clinical workflow}

• Physician Documentation (PD)

-

Record diagnoses, treatment plans, and clinically relevant events digitally for

patient over time

-

Consolidate progress notes across departments

-

Enable coordination across care providers such as physicians and pharmacists

Advanced EMR = CPOE or PD

2

1_{ZynxHealth integration with Epic; PatientOrderSets.com integration with Cerner}

2_{Defined by Dranove et al. Used by McCullogh et al; Freedman et al; Li etc. [Dranove, David, Christopher Forman, Avi Goldfarb, and Shane Greenstein. “The Trillion Dollar Conundrum:}

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Mechanisms of EMR Impact on Patient Safety

1. Spear, Steven J., and Mark Schmidhofer. "Ambiguity and workarounds as contributors to medical error." Annals of Internal Medicine 142, no. 8 (2005): 627-630. 2. Bates, David W., and Atul A. Gawande. 2003. “Improving Safety with Information Technology.” New England Journal of Medicine 348 (25): 2526–34.

Why errors in modern medicine?

1 Work complexity

Knowledge intensiveness

Variety and volatility of

circumstance

Patient safety improves because EMRs:

2 Improve communications

Make knowledge accessible

Provide decision support

Require key pieces of information for correct treatment

Assist with calculations

Perform real-time checks

Assist with monitoring

14,000 diagnoses

6,000 drugs

4,000 medical, surgical procedures

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Summary of Main Results

Advanced EMR adoption leads to:

• 27% decline in all (aggregated) events

• 30% decline in medication events

• 25% decline in complication of procedure,

test, or treatment

Medication events include incorrect medication lists, unauthorized drugs, omitted/extra/wrong dosage, prescription delays, monitoring errors, or inadequate pain management (but not adverse drug reactions). Complications of procedure, test, or treatment include complication following surgery or invasive procedure, anesthesia event, emergency department, maternal complication, neonatal complication, nosocomial infection, cardiopulmonary arrest outside ICU, IV site complication, extravasation of drug or radiologic contrast, catheter or tube problem, onset of hypoglycemia, and complication spinal therapy

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Data Sources For This Study

Source

Description

Patient Safety

Events

PSA

All patient safety events for 2005-2012 in

Pennsylvania Hospitals

EMR Adoption

HIMSS

Adoption of Basic EMR (CDR, CDSS) and

Advanced EMR (CPOE, Physician

Documentation) and non-Clinical IT for

2005-2012

Hospital Controls

PHC4,

AHA, CMS

In-patient days, teaching status, residency

status, JCAHO, medical school, transfer-adjusted

case mix index

County Controls

AHRF

Population; percent white; percent over 65;

unemployment, household income

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EMR Adoption in Pennsylvania Hospitals

Advanced EMR

• CPOE

• PD

[Dranove 2014]

CDR: Clinical Data Repository; CDSS: Clinical Decision Support System; CPOE: Computerized Physician Order Entry;

Dranove, David, Christopher Forman, AviGoldfarb, and Shane Greenstein. “The Trillion Dollar Conundrum: Complementarities and Health Information Technology.” American Economic Journal: Economic

Policy, 2014. https://www.aeaweb.org/forthcoming/output/accepted_POL.php.

40

50

60

70

80 Ad

o

p

ti

o

n

o

f

Ad

va

n

ce

d

EMR

(%

)

2004

2006

2008

2010

2012

Year

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Methods

1._{Case Mix Index used in robustness check}

Unit of analysis

Hospital-year

Outcome

Log of patient safety events

Research Design

Differences-in-differences

Time-variant Controls

Inpatient days (hospital size), case mix

index

1 Time-invariant Controls

Interacted with linear time trend

County:

population, household income,

age over 65 years

Hospital:

teaching, residency, medical

school, JCAHO

Identification

Conditional on controls, EMR adoption

uncorrelated with error

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Model Specification

𝐿𝑜𝑔 (# 𝑃𝐴𝑇𝐼𝐸𝑁𝑇 𝑆𝐴𝐹𝐸𝑇𝑌 𝐸𝑉𝐸𝑁𝑇𝑆)

_it

= 𝛽

₀

+ 𝛽

₁

𝐴𝐷𝑉𝐴𝑁𝐶𝐸𝐷 𝐸𝑀𝑅

_𝑖𝑡

+𝛽

₂

𝐿𝑜𝑔 𝐻𝑂𝑆𝑃𝐼𝑇𝐴𝐿 𝑆𝐼𝑍𝐸

_𝑖𝑡

+ 𝛽

₃

𝐵𝐴𝑆𝐼𝐶 𝐸𝑀𝑅

_𝑖𝑡

+Β

₄

𝐻𝑂𝑆𝑃𝐼𝑇𝐴𝐿 𝐶𝑂𝑁𝑇𝑅𝑂𝐿𝑆

_𝑖

× 𝑌𝐸𝐴𝑅

+ Β

₅

𝐶𝑂𝑈𝑁𝑇𝑌 𝐶𝑂𝑁𝑇𝑅𝑂𝐿𝑆

_𝑖

× 𝑌𝐸𝐴𝑅

+ 𝐻𝑂𝑆𝑃𝐼𝑇𝐴𝐿 𝐹𝐼𝑋𝐸𝐷 𝐸𝐹𝐹𝐸𝐶𝑇𝑆

_𝑖

+ 𝑌𝐸𝐴𝑅 𝐹𝐼𝑋𝐸𝐷 𝐸𝐹𝐹𝐸𝐶𝑇𝑆

_𝑡

+ 𝐸𝑅𝑅𝑂𝑅

_𝑖𝑡

(16)

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EMR Adoption and Patient Safety Events

(1)

b/p

(2)

b/p

(3)

b/p

(4)

Advanced EMR

-0.24**

-0.28**

-0.27**

0.048

0.021

0.019

0.017 Hospital Size

Yes

Basic EMR Control

_No

No

Yes

Hospital Controls

1 _No

_Yes

County Controls

1 _No

_No

_Yes

Year and Hospital FE

Yes

Variance-Covariance Est.

cluster

Observations

952

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Medication Errors

• Incorrect medication lists

• Unauthorized drugs

• Dosage

– Omitted

– Extra

– Wrong

• Prescription delays

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EMR Adoption and Medication Errors

Log of Medication Errors

(b/p)

Advanced EMR

-0.30**

0.01 Hospital Size

Yes

Basic EMR Control

Yes

Hospital-level Controls

Yes

County-level Controls

Yes

Year and Hospital FE

Yes

Variance-Covariance Estimator

cluster

(19)

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Alternate Explanations

• Differential trends at adopting hospitals:

-

Patient safety initiatives

-

Training programs

• EMR causing changes:

-

Reporting

-

Case mix

• Patient safety events causing adoption at peak

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Ruling Out Alternate Explanations

Alternate Explanation

Falsification or Robustness Check

Selection:

• Patient safety initiatives

at adopting hospitals

• Unobserved hospital

ability correlated with IT

adoption and patient

safety

• No effect on skin integrity events (falsification test with

placebo outcome)

• No effect of lead value of EMR on patient safety events

• No effect of non-clinical IT

• Effect of Advanced EMR persists with non-clinical IT as

covariates

Reverse causality

• Lagged events (and changes) do not predict Advanced

EMR adoption

Sample issues (outliers etc.)

• Similar effects with balanced panel and balanced panel

with basic EMR throughout study

Skin integrity events include pressure ulcers, burns, rashes / hives, abrasions, lacerations, blisters, and skin tears. These events are problems with patient positioning, movement, or manipulation; or physical environment; or use of devices near or on patients—so no expected effect from IT

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Skin Integrity Events (Placebo Outcome)

• Pressure ulcers

• Burns

• Rashes / hives

• Abrasions

• Lacerations

• Blisters

• Skin tears

Problems with patient positioning, movement, or manipulation; or physical

environment; or use of devices near or on patients—so no expected effect from IT

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No Effect of Advanced EMR on Skin Integrity

Log of Skin Integrity Events

(b/p)

Advanced EMR

0.01

0.87 Hospital Size

Yes

Basic EMR Control

Yes

County Level Controls

Yes

Hospital Level Controls

Yes

Year and Hospital FE

Yes

Variance-Covariance Estimator

cluster

(23)

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Non-Clinical IT Adoption and Patient Safety

Non-clinical IT systems

-

Revenue Cycle

Management

-

General Financials

-

Financial Decision

Support

-

Human Resources

-

Supply Chain

Management

No direct impact on

patient safety

Control for unobserved

hospital ability

1

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Log Events

b/p

Log Events

b/p

Advanced EMR

-0.26**

0.03 General Financials

-0.08

0.33

0.37 Financial Decision Support

-0.02

-0.03

0.57

0.53 Human Resources

0.02

0.01

0.61

0.86 Supply Chain Management

-0.10

-0.09

0.30

0.37 Revenue Cycle Management

-0.01

-0.00

0.67

0.89 Hospital Size

Yes

Year and Hospital FE

Yes

Hospital Controls

Yes

County Controls

Yes

Variance-Covariance Estimator

cluster

Observations

951

(25)

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Patient Safety Events and Lead EMR Adoption

Log of PS Events

(b/p)

Lead(1) Advanced EMR

0.055

0.411 Hospital Size

Yes

Hospital and Year Fixed Effects

Yes

Time-Interacted Hospital Controls

Yes

Time-Interacted County Controls

Yes

Basic EMR Control

Yes

Variance-Covariance Estimator

cluster

(26)

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EMR Adoption and Patient Safety Events—Case Mix Control

Log of PS Events

(b/p)

Advanced EMR

-0.18**

0.04 Transfer Adjusted Case Mix Index

Yes

PSA eReporting

Yes

Hospital Size

Yes

Hospital and Year FE

Yes

Basic EMR, Hospital, County Controls

Yes

Variance-Covariance Estimator

cluster

(27)

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EMR Adoption and Skin Integrity Events—Case Mix Control

Log of Skin Events

(b/p)

Advanced EMR

0.01

0.88 Transfer Adjusted Case Mix Index

Yes

PSA eReporting

Yes

Hospital Size

Yes

Hospital and Year FE

Yes

Basic EMR, Hospital, County Controls

Yes

Variance-Covariance Estimator

cluster

(28)

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Reverse Causality

Alternate Explanation:

• Hospitals adopting EMRs when patient safety events are at peak

• Events regress to mean rather than decline due to EMRs

Check for:

(29)

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Lagged Safety Events and EMR Adoption

BLPM1

b/p

BLPM3

b/p

BLPM5

b/p

Lag(2) All Events

0.0034

0.0056

0.9968

0.9927

Lag(2) ∆ All Events

-0.0009

0.9994

Lag(2) Patient Days

-0.0003

-0.0013

0.9995

0.9501

Variance-Covariance Estimator

bootstrap

Replications

1000

Seed

11

11 Observations

144

89

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Limitations

• Instruments

• Patient safety initiatives not directly observed (measured)

• Measurement Issues

-

Coarse EMR adoption measure

-

Reports correlated with actual events but not perfectly

-

HIMSS survey inaccuracies

• SUTVA

1 violations

-

Hospitals differ in meaningful EMR usage

-

Hospitals use different EMR products

-

Spillover effects over units (especially private practice physicians credentialed at