perioperative information management systems: driving discovery & reliability in the operating...

Perioperative Information Management Systems: Driving Discovery & Reliability

In The Operating Room

Jesse M. Ehrenfeld, M.D., M.P.H. Assistant Professor of Anesthesiology

Assistant Professor of Biomedical InformaticsDirector, Perioperative Data Systems Research

Director, Center for Evidence-Based Anesthesia Medical Director, Perioperative Quality

Co-Director, Vanderbilt Program for LGBTI Health

Vanderbilt University School of MedicineDepartment of Anesthesiology

jesse.ehrenfeld@vanderbilt.edu

Overview

Part I – Perioperative Information Management SystemsOverview & FunctionalityReliable Processes

Part II – Clinical Decision SupportThe Problem, The Need, Opportunities

Part III – Our Research & The FutureUsing PIMS to Measure & Increase ReliabilityPredictive Modeling / Real Time Feedback LoopsCase Studies: Blood Pressure Gaps & Glucose Control

Vanderbilt Department of Anesthesiology

60,000 adult and pediatric patient encounters

90 anesthetizing locations

20,000 patients are seen in the Vanderbilt Preoperative Evaluation Clinic (VPEC)

3,000 patients are seen annually in our Vanderbilt Interventional Pain Center

20,000 Vanderbilt adult and pediatric patients receive an anesthetic during a radiologic, gastrointestinal, or other diagnostic or therapeutic procedure

Provide care in eight intensive care units, including six adult, the pediatric and neonatal intensive care units

4,000 anesthetics per year in the labor and delivery suite

Perioperative Data Systems Research Group

DirectorJesse Ehrenfeld, MD

Data Intelligence Analyst

Jason Denton

Health Systems Database Analyst

Chris Eldridge

Data Warehouse Architect

Michealene Johnson

Health Systems Database Analyst

Dylan Snyder

Research AssistantRasheeda Lawson

Research Analyst

Khensani Marolen

Data Management

SpecialistTBD

Project Manager

Angelo del Puerto

Last updated 7.2012

Graduate Students• Amlan Bhattacharjee• Sean Chester• Kristen Eckstrand• Aneesh Goel• Paul Hannam• Mary Marschner• Monika Jering• Ilana Stohl

Undergraduate Students• Molly Cowan• Lindsay Lee• Shane Selig• Jacob Shiftan• Emily Wang

Overview

Part I – Perioperative Information Management SystemsOverview & FunctionalityReliable Processes

Part II – Clinical Decision SupportThe Problem, The Need, Opportunities

Part III – Our Research & The FutureUsing PIMS to Measure & Increase ReliabilityPredictive Modeling / Real Time Feedback LoopsCase Studies: Blood Pressure Gaps & Glucose Control

Biomedical Informatics

Medical Informatics• Intersection of information

science, computer science and health care

• Resources, devices, methods optimize information acquisition, storage, retrieval and use

• Involves computers, clinical guidelines, information, medical terminologies, communications systems

Perioperative Information Management Systems

Accurate / reliable data recording

Interface with hospital-wide EHR

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Size

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Population/Development

RuralUrban

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Academic Status

Teaching

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GeographicalDistribution

NortheastSoutheastSouthwestMidwestWest

PIMS Adoption in the U.S. – 2011

Stohl, Sandberg, Ehrenfeld. Assoc. of SCIP Compliance with Use of a PIMS. (submitted)

Small

Large

Areas Impacted by PIMS

Major Areas of Impact

PatientsDepartment

al managemen

t

Clinical Practice

Ehrenfeld, J.M., Rehman, M.A. “Anesthesia Information Management Systems: Current Functionality and Limitations” (2010) Journal of Clinical Monitoring and Computing Aug 24

PIMS: Impact on Patients

• Provision of real-time intraoperative decision support

• Allows the anesthesia care team to focus on the patient, rather than recording vital signs

• Better legibility and availability of historical records

• More precise recording of intraoperative data & patient responses to anesthesia

Impact on patients

Chau, A., Ehrenfeld, J.M. “Using Real Time Clinical Decision Support to Improve Performance on Perioperative Quality and Process Measures” (2011) Anesthesiology Clinics

PIMS: Impact on Dept Management

• Supply cost analysis by provider/type of surgery/patient

• Improved billing accuracy and timeliness

• Fulfills the Joint Commission requirements for legible and comprehensive patient records

• Facilitates verification of Accreditation Council for Graduate Medical Education case requirements for trainees

• Simplifies compliance with concurrency and other regulatory issues

Impact on Department

al Manageme

nt

Chau, A., Ehrenfeld, J.M. “Using Real Time Clinical Decision Support to Improve Performance on Perioperative Quality and Process Measures” (2011) Anesthesiology Clinics

PIMS: Impact on Clinical Practice

• Provides precise, high-resolution records which can be used for educational purposes

• Enables researchers to rapidly find rare events or specific occurrences across a large number of cases

• Facilitates individual provider performance tracking

• Allows better quality assurance functionality through the creation of more complete and precise records

• Integration with other hospital databases can allow assessment of short and long term patient outcomes

• Provision of additional legal protection via the availability of unbiased, precise information

Impact on Clinical Practice

Chau, A., Ehrenfeld, J.M. “Using Real Time Clinical Decision Support to Improve Performance on Perioperative Quality and Process Measures” (2011) Anesthesiology Clinics

13Mobile PIMS: VigiVUTM

Transformative technology

•Enhance situational awareness•Enable development of new anesthesia care models•Significant impact on operational efficiency

14Mobile PIMS: VigiVUTM

Push Notifications

Push Notifications• Abnormal vital signs• Lab results• Operational

notifications• Patient in holding• Patient in OR• Surgeon closing

• Notable drug events• Vasoactives

Process Reliability

Processes are collections of systems and actions following prescribed procedures for bringing about a result.

Reliability of any processes can be determined using data when process failure criteria are established.

Results of the analysis can be graphically displayed, problems identified, categorized and identified for corrective action.

The hardest part of any reliability analysis is getting the data.

Process Reliability in Health Care

Given our intentions, as talented providers, why are clinical processes carried out at such low levels of reliability?

Don’t show up for work wanting to provide bad care!

‘‘It’s the system, not the people’’ – true, but not helpful as we aim to improve our processes

Resar, RK. Making Noncatastrophic Health Care Processes Reliable. Health Serv Res. 2006.

Process Reliability in Health Care

Reasons for reliability gap:

Health care improvement methods excessively dependent on vigilance and hard work

We benchmarking to mediocre outcomes in health care – leads to false sense of process reliability

Allow clinical autonomy creates wide, unjustifiable, performance variation

Processes not designed to meet specific, articulated reliability goals.

Resar, RK. Making Noncatastrophic Health Care Processes Reliable. Health Serv Res. 2006.

Overview

Part I – Perioperative Information Management SystemsOverview & FunctionalityReliable Processes

Part II – Clinical Decision SupportThe Problem, The Need, Opportunities

Part III – Our Research & The FutureUsing PIMS to Measure & Increase ReliabilityPredictive Modeling / Real Time Feedback LoopsCase Studies: Blood Pressure Gaps & Glucose Control

Clinical Decision Support

Perioperative Info. Management Systems• Not just record

keeping systems• Facilitate application of

• collective wisdom of previous

• cases to your current patient

• “Big brain” in the sky• Advice and support

Problem/Need

Why do we need clinical decision support?

Mistakes happen You own a calculator don’t you?

Knowledge evolves Pubmed / Medline

Problem/Need

To err is human•Time constraints•Frequent interruptions•Limits of memory•Multi-tasking•Fatigue

Not just looking for errors

•Define optimal care improve our performance

General Solution: Decision Support

“Clinical consultation systems that use population statistics and expert knowledge to offer real-time advice to clinicians…they provide for patient specific information management and consultation.”

- EH Shortliffe, JAMA 1987;258:61-6

Clinical Decision SupportObjective: assist clinicians in (1) making the best clinical decision and (2) following recommended practices

Wide range of tools: very simple data field checks complex calculations performed in the background

Potential to changes approaches to patient safety Reactive Proactive

General Solution: Decision Support

Goals in the Operating Room: Optimize outcomes by enabling physicians Reduce errors by providing reminders Increase skill by sharing information

Data

Data

Data

Data

OR Decision Support Hierarchy

Type Consequence Level

Level of Difficulty

Managerial Low Low Example: Bayesian analysis to predict amount of surgical time remainingProcess of Care Medium Medium

Example: SCIP measures (antibiotics before incision, normothermia, etc.)Outcome Based High High

Example: Provide risk-adjusted 30 day post-op pain scores after arthoplasty

OR Decision Support Hierarchy

Type Consequence Level

Level of Difficulty

Managerial Low Low Example: Bayesian analysis to predict amount of surgical time remainingProcess of Care Medium Medium

Example: SCIP measures (antibiotics before incision, normothermia, etc.)Outcome Based High High

Example: Provide risk-adjusted 30 day post-op pain scores after arthoplasty

OR Decision Support Hierarchy

Type Consequence Level

Level of Difficulty

Managerial Low Low Example: Bayesian analysis to predict amount of surgical time remainingProcess of Care Medium Medium

Example: SCIP measures (antibiotics before incision, normothermia, etc.)Outcome Based High High

Example: Provide risk-adjusted 30 day post-op pain scores after arthoplasty

OR Decision Support Hierarchy

Type Consequence Level

Level of Difficulty

Managerial Low Low Example: Bayesian analysis to predict amount of surgical time remainingProcess of Care Medium Medium

Example: SCIP measures (antibiotics before incision, normothermia, etc.)Outcome Based High High

Example: Provide risk-adjusted 30 day post-op pain scores after arthoplasty

Clinical Decision Support

I’m not convinced. Does it really make a difference?

Perioperative Information Management Systems (PIMS) Mediate Improved SCIP Compliance Compared to Hospitals Without PIMS

Stohl, Sandberg, Ehrenfeld. Assoc. of SCIP Compliance with Use of an PIMS. (submitted)

Decision Support Version 1.0

Outside the Operating Room Web-based tools Computerized Physician Order

Entry PDA, iPhone applications

Inside the Operating Room Anesthesia Information

Management Systems

Clinical Decision Support 2.0

Machine Learning

Techniques

Artificial Intelligence

Advanced Algorithms

Contextual InformationProcessing

PreviousCases

ClinicalGuidlines

Real-TimeData

Clinical Decision Support 2.0

SURGICAL EVENT(blood loss, allergy, etc)

orEXTERNAL EVENT

(lab values, new info, etc)

SUGGESTIONS / GUIDELINES /

STATISTICS

IDEAL RESPONSE

DATA FROM ALL PREVIOUS CASES

Clinical Decision Support 2.0

Envelop of Care

Case Progression Over Time

Clinical Decision Support 2.0

Envelop of Care

Case Progression Over Time

Clinical Decision Support 2.0

Envelop of Care

Case Progression Over Time

Clinical Decision Support 2.0

Envelop of Care

Case Progression Over Time

Alert

Clinical Decision Support 2.0

Envelop of Care

Case Progression Over Time

Alert

Clinical Decision Support 2.0

Envelop of Care

Case Progression Over Time

Alert

Alerting

Once you generate knowledge/ information, how do you disseminate it?

Alerting modalities: Who and How? Identify appropriate provider Get their attention:

On-screen pop-ups Pager messages Emails

Limitations/Factors

Usability: Ability to provide a useful function.

Does it do anything of value?

Limitations/Factors

Ergonomics: The study of how people interact with their

environment. Can physicians use it?

Limitations/Factors

Latency: Delays in usage and availability.

Will it work in a time-sensitive scenario?

Limitations/Factors

Interconnectivity / Interoperability: Ability to connect to other sources of information and

share information effectively. Does it network well with existing infrastructure?

Limitations/Factors

Ability to Adapt: If we don’t have the knowledge, can the system be

used to generate missing info? Can it develop a hypothesis?

Summary: Process Monitoring & Control

Goal: right inforight time

right person

Keys to electronic process monitoring Process models Process exceptions Alert Generation

Overview

Part I – Perioperative Information Management SystemsOverview & FunctionalityReliable Processes

Part II – Clinical Decision SupportThe Problem, The Need, Opportunities

Part III – Our Research & The FutureUsing PIMS to Measure & Increase ReliabilityPredictive Modeling / Real Time Feedback LoopsCase Studies: Blood Pressure Gaps & Glucose Control

Required Components

Define Norms of Practice / Baseline

Real-Time Data Capture

Alerting Mechanism

Measure Outcomes

Required Components

Define Norms of Practice / Baseline

Real-Time Data Capture

Alerting Mechanism

Measure Outcomes Increasing D

ifficulty

Required Components

Define Norms of Practice / Baseline

Real-Time Data Capture

Alerting Mechanism

Measure Outcomes Increasing D

ifficulty

Decision Support Engine

Define Norms of Practice

Single center retrospective analysis of PIMS data Equipment performance characteristics

Ehrenfeld, J.M., Walsh, J.L. & Sandberg, W.S. “Right and Left Sided Mallinckrodt Double Lumen Tubes Have Identical Clinical Performance” Anesthesia & Analgesia. (2008) 106 (6) 1847-1852.

Physiologic Monitoring Ehrenfeld, J.M., Epstein, R.H., Bader, S., Kheterpal, S., Sandberg, W.S. “Automatic Notifications

Mediated by Anesthesia Information Management Systems Reduce the Frequency of Prolonged Gaps in Blood Pressure Documentation” Anesthesia & Analgesia. (2011) Aug;113(2):356-63. Epub 2011 Mar 17.

Ehrenfeld, J.M., Funk, L.M, Van Schalkwyk, J., Merry, A., Sandberg, W.S., Gawande, A. “Incidence of Hypoxemia During Surgery: Evidence from Two Institutions” Canadian Journal of Anesthesia. 2010: 57 (10) 888-97.

Predictors of Blood Transfusion Henneman, J.P., Ehrenfeld, J.M. “A Predictive Model For Intraoperative Blood Product Requirements”

IARS, 5/11 Multi-center data aggregation (MPOG)

Epidural abscess / hematoma Bateman, B.T., Mhyre, J.M., Ehrenfeld, J.M., Kheterpal, Abbey, K.R., Argalious, M., Berman, M.F., St. Jacques, P., Levy, W., Loeb, R.G., Paganelli, W., Smith, K.W., Wethington, K.L., Wax, D., Pace, N.L., Tremper, K., Sandberg, W.S. “The Risk and Outcomes of Epidural Hematomas and Abscesses Following Perioperative and Obstetric Epidural Catheterization: A Report from the MPOG Research Consortium.” Anesth Analg. 2012 Apr 13.

Alerting Mechanisms

Notification modalities Pagers / iPhones On-screen pop-ups Vibration belts Heads-up displays

Frequency One time vs. Multiple

Level of Acknowledgment Hard-Stop vs. Soft Alerts

Alerts to Drive Performance

Assessments of Cognitive Deficits in Mutant MiceRamona Marie Rodriguiz and William C. Wetsel Duke University Medical Center

Active Avoidance Learning

Outcomes Measurement

What are the Outcomes Process of Care

“Wake-Up” time / Time to extubation Room turnover time Time to discharge from PACU

Patient Centered Post-operative pain scores (immediate, 30 days) Rates of PONV and PDNV 30 day re-admission rates Mortality, wound infection rates

1. GAPS IN BLOOD PRESSURE MONITORING

2. INTRAOPERATIVE GLUCOSE MONITORING

3. REAL TIME PATIENT PREDICTIVE MODELS

4. ENHANCING VALUE IN ANESTHESIA

A Few Quick Examples … …To Bring It All Together

GAPS IN BLOOD PRESSURE MONITORING

Example #1

Ehrenfeld J, Epstein RH, Bader S, Kheterpal S, Sandberg WS. Automatic notifications mediated by anesthesia information management systems reduce the frequency of prolonged gaps in blood pressure documentation. Anesth Analg 2011;113:356–63

Gaps in Physiologic Monitoring

BP reading: 9:52 amInduction: 9:53 amBP reading: 10:08 am (16 minutes later)

Blood Pressure Gaps: Results

Blood Pressure Gaps: Results

INTRAOPERATIVE GLUCOSE MONITORING

Example #2

Closing Example

Diabetes Management

12.22%

24.33%

38.21%

57.84%63.90%

77.52%80.70%

87.88%

100.00% 100.00%

0.00%

10.00%

20.00%

30.00%

40.00%

50.00%

60.00%

70.00%

80.00%

90.00%

100.00%

0-1 1-2 2-3 3-4 4-5 5-6 6-7 7-8 8-9 >9 hrsSurgical Duration

(excludes anesthesia induction & emergence time)

Diabetes Patients Receiving Intraoperative Insulin Who Had Intraoperative Glucose Measured

Peterfreund, R.P., McCartney, K., Ehrenfeld, J.M. “Impact of Intraoperative Glucose Notifications” ASA 2012 (accepted)

Diabetes Management

12.22%

24.33%

38.21%

57.84%63.90%

77.52%80.70%

87.88%

100.00% 100.00%

0.00%

10.00%

20.00%

30.00%

40.00%

50.00%

60.00%

70.00%

80.00%

90.00%

100.00%

0-1 1-2 2-3 3-4 4-5 5-6 6-7 7-8 8-9 >9 hrsSurgical Duration

(excludes anesthesia induction & emergence time)

Diabetes Patients Receiving Intraoperative Insulin Who Had Intraoperative Glucose Measured

Peterfreund, R.P., McCartney, K., Ehrenfeld, J.M. “Impact of Intraoperative Glucose Notifications” ASA 2012 (accepted)

Better Care for Diabetic Patients

Peterfreund, R.P., McCartney, K., Ehrenfeld, J.M. “Impact of Intraoperative Glucose Notifications” ASA 2012 (accepted)

Better Care for Diabetic Patients

Reduced Readmission

Rates

Peterfreund, R.P., McCartney, K., Ehrenfeld, J.M. “Impact of Intraoperative Glucose Notifications” ASA 2012 (accepted)

REAL TIME PREDICTIVE PATIENT MODELS

Example #3

“Enhancing Perioperative

Safety Through the Determination of Intraoperative

Predictors of Post-Operative

Deterioration”

Funded by Anesthesia Patient Safety Foundation

PI – J. Ehrenfeld

ENHANCING VALUE IN ANESTHESIA

Example #4

Enhancing Value in Anesthesia

Wanderer, J.P., Hester, D., Ehrenfeld, J.M. “Cost Variability in Anesthesia Services” ASA 2012 (accepted)

Enhancing Value in Anesthesia

Wanderer, J.P., Hester, D., Ehrenfeld, J.M. “Cost Variability in Anesthesia Services” ASA 2012 (accepted)

Enhancing Value in Anesthesia

Value Cost

Enhancing Value in Anesthesia

Value

Cost

Quality

Enhancing Value in Anesthesia

Wanderer, J.P., Hester, D., Ehrenfeld, J.M. “Cost Variability in Anesthesia Services” ASA 2012 (accepted)

Vanderbilt Anesthesia Optimal Care Score

Real-Time Perioperative Dashboard

Blood Product Utilization Dashboard

Overview

Part I – Perioperative Information Management SystemsOverview & FunctionalityReliable Processes

Part II – Clinical Decision SupportThe Problem, The Need, Opportunities

Part III – Our Research & The FutureUsing PIMS to Measure & Increase ReliabilityPredictive Modeling / Real Time Feedback LoopsCase Studies: Blood Pressure Gaps & Glucose Control

What Does the Future Hold

More “Decision Support 2.0” Live comparison of current clinical data Indexed (pre-sorted) set of cases

Matching closest cases on surgery, age, ASA, etc

More Outcomes Beyond PONV & the SSN death index

More Notification Modalities & Mobile Apps

More Patient Specific Real-Time Prediction Models

Perioperative Genomics

Conclusions

Medical Informatics will empoweranesthesiologists in the 21st century

Vanderbilt Perioperative Data Systems Research Group