Health IT Safety Culture: Working Together to Improve Patient Care

Patient Safety Organizations: the Engine for Collaboration February 29, 2016 Ronni P. Solomon, JD ECRI Institute

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Conflict of Interest Ronni Solomon has no real or apparent conflicts of interest to report.

In medieval medicine…. • ____________ calculations were thought absolutely necessary to diagnose the patient and change treatment. • ____________ movements were regarded as strongly influencing the patient's prognosis.

Agenda

Learning Objectives • Describe ways to establish a culture of safety for health IT safety lessons and solutions • Share multi-stakeholder generated safe practices for copy and paste and patient identification • Discuss safety hazards reported to the Partnership for Health IT Patient Safety • Collaborate on safety solutions through Patient Safety Organizations (PSOs) and the Partnership for Health IT Patient Safety

Benefits for the Value of Health IT Four Domains: Satisfaction, Treatment/Clinical, Patient Engagement, and Savings Building a health IT system that is safe, secure, usable and testable Helping to ensure that patients get the right care at the right time with the right quality Avoiding claims and reputational harm associated with medical errors

http://www.himss.org/ValueSuite

Do you think your EHR or the way it’s used has been associated with adverse events in your organization?

ECRI Institute

Reporting and Analytics Since 1971 Medical Product Reporting

Since 2003 Contractor for state reporting

Since 2008 Patient Safety Organization

Voluntary Incidents, RCAs, Near Misses

Mandatory Incidents Near Misses

Voluntary Incidents, Near Misses, RCAs, more

Over 3.5 million reports

The HIT Safety Landscape

11/1/2011

3/1/2016

Partnership Goals

Making Health IT Safer Together by: ►Establishing a non-punitive environment for sharing and learning ►Testing a collaborative model for collecting and analyzing safety issues ►Achieving robust stakeholder engagement ►Sharing best practices and lessons learned ►Evaluating two reporting taxonomies ►Informing the national safety strategy for health IT

A Multi-Stakeholder Collaboration

Partnership Activities Hold face-to-face meeting; publish proceedings

Convene workgroup on copy and paste

Prioritize; Disseminate

Recruit stakeholders

Obtain funding

Conduct evidence scan; analyze data

Convene workgroup on patient identification

Establish expert advisory panel

Analyze and disseminate data

Develop safe practices; develop toolkit

Disseminate safe practices, lessons learned

Implement web based reporting system

Convene quarterly conference calls

Seek endorsement for safe practices

Hold 2d Face to Face

Design guiding principles

Engage, Exchange, Analyze, Prioritize, Disseminate

Expert Advisory Panel 

David W. Bates, MD, MSc, Brigham and Women’s Hospital



Pascale Carayon, PhD, University of Wisconsin-Madison College of Engineering



Tejal Gandhi, MD, MPH, National Patient Safety Foundation



Terhilda Garrido, MPH, ELP, Kaiser Permanente



Omar Hasan MBBS, MPH, MS, FACP, American Medical Association



Chris Lehmann, MD, Monroe Carell Jr. Children’s Hospital at Vanderbilt University Medical Center



Peter J. Pronovost, MD, PhD, The Johns Hopkins University School of Medicine



Jeanie Scott, VHA Office of Informatics and Analytics/Health Informatics



Patricia Sengstack, DNP, RN-BC, CPHIMS, Bon Secours Health System



Hardeep Singh, MD, MPH, Michael E. DeBakey VA Medical Center



Dean Sittig, PhD, The University of Texas Health Science Center at Houston, School of Biomedical Informatics



Paul Tang, MD, MS, Palo Alto Medical Foundation, Sutter Health

Since HIMSS15! 4/1/2015 3/1/2015

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• Convened Workgroup on Copy and Paste • Issued Health IT Safe Practices: Safe Use of Copy and Paste • Convened Workgroup on Patient Identification • Conducted Ongoing Data Analytics • Disseminated Monthly case studies • Held Annual Meeting: Partnering for Action https://www.ecri.org/resource-center/Pages/HITPartnership.aspx

Health IT Safe Practices: Toolkit for the Safe Use of Copy and Paste

■ Evidence review ■ Toolkit ■ Implementation tools ■ Checklists ■ Resources

The Workgroup Process ► Held monthly meetings (March-August) ► Defined copy and paste ► Looked at uses of copy and paste ► Prepared evidence-based literature review ► Reviewed de-identified events ► Identified vendor functionalities that are alternatives to copy and paste ► Heard about solutions ► Discussed and designed best practices

Partnership Toolkit: What, Why, and How

Safe Practices

Evidence Review

Toolkit

Tools

Resources

Safe Practice Recommendations for Copy and Paste • Recommendation A: Provide a mechanism to make copy and paste material easily identifiable. • Recommendation B: Ensure that the provenance of copy and paste material is readily available. • Recommendation C: Ensure adequate staff training and education regarding the appropriate and safe use of copy and paste. • Recommendation D: Ensure that copy and paste practices are regularly monitored, measured, and assessed.

Workgroup Members 

Tejal Gandhi, MD, MPH, CPPS, Workgroup Chair, President and CEO, National Patient Safety Foundation



Matthew P. Fricker, Jr MS RPh FASHP, Program Director, Institute for Safe Medication Practices, ISMP



Beth Acker-Moodhard, RHIA, Health Information Management Specialist, U. S. Department of Veterans Affairs



Terhilda Garrido, MPH, VP Health Information Technology Transformation & Analytics, Kaiser Permanente





Graham Atkinson, D.Phil., Vice President for Research and Policy, Jayne Koskinas Ted Giovanis Foundation for Health and Policy

Ted Giovanis, FHFMA, MBA, President, Jayne Koskinas Ted Giovanis Foundation



Kristina M. Hengehold, BSN, MHA, RN, CPN, Manager, Patient Safety/Infection Prevention, St. Louis Children's Hospital



John D. McGreevey III, MD, FACP, Assistant Professor of Clinical Medicine Section of Hospital Medicine, Division of General Internal Medicine, Associate CMIO, University of Pennsylvania Health System



Allen Chen, MD, PhD, MHS, Associate Professor, Oncology and Pediatrics, and Health IT Patient Safety Officer, Armstrong Institute for Patient Safety and Quality, Johns Hopkins University



R. Lacey Colligan, MD, MSc



Landon Combs, MD, Medical Director for EPIC, Wellmont Health System



Anna Orlova, PhD, Senior Director, Standards, American Health Information Management Association (AHIMA)



Sarah T. Corley, MD FACP, FHIMSS, Chief Medical Officer, QSI NextGen Healthcare Information Systems, Inc.



Ann Presley, RPh, Executive Director, Product Management, McKesson Technology Solutions





Patrick Cross, Senior Knowledge and Technology Auditor, Wellmont Health

Sue Prill, MD, MBA, Medical Director and Dyad Partner, Oncology Services, Wellmont Health





Tina Eldridge, RN, IT Clinical Program Director, OhioHealth MedCentral

Harry Rhodes, MBA, RHIA, FAHIMA, CHPS, CDIP, CPHIMS, American Health Information Management Association (AHIMA), Director, National Standards



Daniel Ellison, System Director HIM Operations and Data Integrity, Wellmont Health



Jeannie Scott, CPHIMS, Director, Informatics Patient Safety, Veterans Health Administration



Trisha Flanagan, RN, MSN, Senior Manager, Patient Safety, athenahealth



Mark Segal, PhD, Vice President, Government and Industry Affairs, GE Healthcare IT

Workgroup Members 

Gregorio Sicard, MD, MBI, Physician and Content Analyst, McKesson



Ronni P. Solomon, JD Executive Vice President and General Counsel



Dean Sittig, PhD, Professor of Biomedical Informatics, University of Texas Health Science Center at Houston



Maura Crossen-Luba, MPH, CPH Business Development Analyst/Patient Safety Analyst



Paul Tang, MD, Vice President, Chief Innovation and Technology Officer, Palo Alto Medical Foundation





Michael Victoroff, MD, Chief Medical Officer, Lynxcare, Inc.

Ellen Deutsch, MD, MS, FAAP, FACS, CPPS, Medical Director





Elizabeth Wade, Pharm D, BCPS, Medication Safety Officer, Concord Hospital

Amy Goldberg-Alberts, MBA, FASHRM, CPHRM Executive Director, Partnership Solutions Patient Safety, Risk, and Quality



Jonathan S. Wald, MD, MPH, Director, PatientCentered Technologies | Center for the Advancement of Health IT, RTI International



Robert Giannini, NHA, CHTS-IM/CP Patient Safety Analyst and Consultant





Diana Warner, MS, RHIA, CHPS, FAHIMA, Director, Health Information Management, Practice Excellence, American Health Information Management Association (AHIMA)

Lorraine Possanza, DPM, JD, MBE, FACFOAM, FAPWCA, Senior Patient Safety, Risk, and Quality Analyst, Workgroup director



Erin Sparnon, MEng, Engineering Manager



Amy Tsou, MD, MSc, Senior Research Analyst, Health Technology Assessment, ECRI-Penn AHRQ Evidence Based Practice Center (EPC)



Peter Zang, MD, Product Manager, Enterprise Information Solutions, McKesson Corporation

The workgroup acknowledges and thanks Neal Patel, MD, MPH, Chief Medical Informatics Officer, Professor of Clinical Pediatrics, Vanderbilt University Medical Center, Nashville, TN for his presentation to this workgroup.

Next Set of Health IT Safe Practices:

Patient Identification

Workgroup: November 2015- April 2016

PSO Deep Dive PSO Deep Dive Evidence Review Multi-stakeholder deliberation

• Key word search* of PSO database identified events – (n = 10,915) • Manual review of events for verification of patient identification issues – (n = 7,613) • Event dates: January 2013 through August 2015 • Tagged utilizing patient identification taxonomy by patient safety analysts

Vendor Functionalities Innovations Recommended Practices Toolkit

*Keywords: same name, last name, first name, patient name, pt name, pt. name, patient’s name, pts’ name, no name, name corrected, else's name, elses name, exact name, name band, wrong patient, wrong pt, to another patient, to another pt, incorrect patient, incorrect pt, one patient, one pt, patients identification, patient’s id, patient sticker, pt sticker, patient label, pt label, wrong person, identification band, Identification bracelet, patients id, identity, identifier, identifying patient, identifying pt, ID band, ID bracelet, ID number, date of birth, DOB, social security, SSN, medical record number, mr#, incorrect mr, wrong mr, wrong paperwork, wrong paper work, wrong medical, arm band, armband

Patient Identification Process Map

Percentage of issues during the phases of care

12.6%

87.2% 0.2%

Systematic Evidence Review • From 1/2009 through 12/31/2015 • 243 potentially relevant articles identified • Plan to include U.S. and non-U.S. literature

Patient Identification Workgroup Members 

Hardeep Singh, MD, MPH, Workgroup Chair, Michael E. DeBakey Veterans Affairs Medical Center



Terhilda Garrido, MPH, VP Health Information Technology Transformation & Analytics, Kaiser Permanente



Jason Adelman, MD, MS, Chief Patient Safety Officer & Associate Chief Quality Officer, New York-Presbyterian Hospital/Columbia University Medical Center



Andrew Gettinger, MD, Chief Medical Information Officer, Office of the National Coordinator for Health IT, Office of Programs & Engagement, Office of Clinical Quality & Safety



Graham Atkinson, DPhil, Vice President for Research and Policy, Jayne Koskinas Ted Giovanis Foundation for Health and Policy



Ted Giovanis, FHFMA, MBA, President, Jayne Koskinas Ted Giovanis Foundation



Linda G. Brady, CAE, Chief Executive Officer, Association for Healthcare Documentation Integrity (AHDI)



Lynn Thomas Gordon, MBA, RHIA, CAE, FACHE, FAHIMA , Chief Executive Officer, American Health Information Management Association (AHIMA)



Gerry Castro, PhD, MPH, Project Director, Patient Safety Initiatives, Joint Commission on Accreditation of Healthcare



Helen Haskell, Mothers Against Medical Errors



William Isenberg, MD, PhD, Vice President Patient Safety, Sutter Health



Caroline Jonker, Executive Director, McKesson



Leslie Kringstein, Interim Vice President of Public Policy of College of Healthcare Information Management Executives (CHIME)



Nana Kunlertkit, The Johns Hopkins Hospital



Christoph U. Lehman, MD, FAAP, FACMI, Professor, Pediatrics and Biomedical Informatics, Monroe Carell Jr. Children’s Hospital at Vanderbilt University Medical Center



Allen Chen, MD, PhD, MHS, Associate Professor Oncology, Associate Professor Pediatrics, Armstrong Institute for Patient Safety and Quality, The Johns Hopkins Hospital



Brian Crawford, Epic



Justin Cross, MD, Medical Informatics Fellow, Office of the National Coordinator for Health IT



Sharon Fiveash, Baptist Memorial Health Care PSO



Trisha Flanagan, RN, MSN, Senior Manager, Patient Safety, athenahealth





Angela Franklin, JD, Senior Officer, Drugs and Medical Devices, The Pew Charitable Trusts

Susan Lucci, RHIA, CHPS, CHDS, Consultant/Chief Privacy Officer, Just Associates, Inc.



John D. McGreevey III, MD, FACP, Assistant Professor of Clinical Medicine, Associate CMIO, University of Pennsylvania Health System

Patient Identification Workgroup Members 

Feliciano “Pele” Yu, Jr., MD, MSHI, MSPH, FHIMSS, Chief Medical Information Officer, St. Louis Children’s Hospital; Medical Director, Washington University Pediatric Computing Facility; Associate Professor, Department of Pediatrics, Washington University School of Medicine



Mary Beth Navarra-Sirio, RN, MBA, Vice President Regulatory Strategy, McKesson



Lori A. Paine, RN, MS, DrPH(c), Director, Patient Safety, The Johns Hopkins Hospital and Armstrong Institute for Patient Safety and Quality, Johns Hopkins Medicine



Susan Paparella, RN, MSN, Vice President, Institute for Safe Medication Practices (ISMP)



Ronni Solomon, JD, Executive Vice President and General Counsel, ECRI Institute



Kalyan Pasupathy, PhD, Mayo Clinic, Center for the Science of Health Care





Josh Rising, MD, MPH, Director, The Pew Charitable Trusts

William Marella, MBA, Executive Director, PSO Operations and Analytics, ECRI Institute, Workgroup Moderator



Ellen Deutsch, MD, MS, FAAP, FACS, CPPS, Medical Director, ECRI Institute

ECRI Institute



Jim Russell, RPh, Epic



Jeanie Scott, CPHIMS, Director, Informatics Patient Safety, Veterans Health Administration



Robert Giannini, NHA, CHTS-IM/CP, Patient Safety Analyst and Consultant, ECRI Institute



Mark Segal, PhD, Vice President, Government and Industry Affairs, GE Healthcare IT





Debora Simmons, PhD, RN, CCNS, FAAN, The University of Texas Health Science Center at Houston, School of Biomedical Informatics

Amy Goldberg-Alberts, MBA, FASHRM, CPHRM Executive Director, Partnership Solutions Patient Safety, Risk, and Quality, ECRI Institute



Jeremy J. Michael, MD, MHS, Health Technology Assessment, ECRI-Penn AHRQ Evidence Based Practice Center (EPC), ECRI Institute



Lorraine Possanza, DPM, JD, MBE, FACFOAM, FAPWCA, Senior Patient Safety, Risk, and Quality Analyst, ECRI Institute



Amy Tsou, MD, MSc, Senior Research Analyst, Health Technology Assessment, ECRI-Penn AHRQ Evidence Based Practice Center (EPC), ECRI Institute



Jeff Smith, AMIA



Maria Stolz-Epple, The Johns Hopkins Hospital



Allen J. Vaida, PharmD, FASHP, Executive Vice President, Institute for Safe Medication Practices



Diana Warner, MS, RHIA, CHPS, FAHIMA, Director, HIM Practice Excellence, American Health Information Management Association

Reports to the Partnership using the HIT Hazard Manager • • • •

Subset of Partnership events 152 Reports 6 Hospitals/Health Systems Events submitted July 2014 – May 2015 • HIT Hazard Manager terminology

Observations • Health IT-related events not always apparent • Majority communicated internally, not to vendor • Usability identified as the leading contributing factor – Confusing information display Over 2/3 usability – Mismatch between real workflows and HIT issues – Mismatch with user expectations. • Other leading contributing factors – Decision Support – Missing safeguard – Local Implementation – Faulty local configuration or programing – Other Factors – Inadequate training

HIT Hazard – Contributory Causes

[CATEGORY NAME], [CATEGORY NAME], [VALUE], [VALUE], [PERCENTAGE] [PERCENTAGE] Local Implementation, 62, 22%

Usability

[CATEGORY NAME], [VALUE], [PERCENTAGE]

Data Quality

Decision Support Vendor Factors Local Implementation

[CATEGORY NAME], Decision Support, 36, [VALUE], 13% [PERCENTAGE]

Other factors

Usability CONFUSING INFORMATION DISPLAY

29

MISMATCH BETWEEN REAL WORKFLOWS AND HIT

27

MISMATCH WITH USER EXPECTATIONS

21

DIFFICULT DATA ENTRY

9

INDEQUATE USER FEEDBACK

8

INFORMATION HARD TO FIND

8

SUB-OPTIMAL SUPPORT OF TEAMWORK

4

EXCESSIVE DEMAND ON HUMAN MEMORY

2

OTHER

5 0

5

10

15

20

25

31

30

35

Data Quality FAULTY REFERENCE INFORMATION

12

DISCREPANCY IN DISPLAYED, PRINTED OR EXPORTED DATA

9

DATA ENTRY IN WRONG PATIENT RECORD - IT DESIGN

7

LOST DATA

3

PATIENT INFORMATION/RESULTS ROUTED TO WRONG RECIPIENT UNPREDICTABLE ELEMENTS ON PAPER OR SCANNED DOCS

3

2

VIRUS OR MALWARE

0

INACCURATE NATURAL LANGUAGE PROCESSING

0

DATA ENTRY IN WRONG PATIENT RECORD ORGANIZATION POLICY

0

OTHER

13 0

2

4

6

8

32

10

12

14

Decision Support MISSING SAFEGUARD

22

FAULTY RECOMMENDATION

6

INAPPROPRIATE LEVEL OF AUTOMATION

5

INADEQUATE CLINICAL CONTENT

0

EXCESSIVE NON SPECFIC ALERTS

0

OTHER

5 0

5

10

15

20

25

Vendor Factors FAULTY SOFTWARE DESIGN

17

SUB-OPTIMAL INTERFACES BETWEEN APPLICATIONS (AND DEVICES)

13

FAULTY VENDOR CONFIGURATION RECOMMENDATION

3

INADEQUATE VENDOR TESTING

2

UNUSABLE SOFTWARE IMPLEMENTATION TOOLS

2

INADEQUATE CONTROL OF USER ACCESS

1

INADEQUATE VENDOR SOFTWARE CHANGE CONTROL

1

NON-CONFIGURABLE SOFTWARE

1

OTHER

7 0

2

4

6

8

10

12

14

16

18

Local Implementation FAULTY LOCAL CONFIGURATION OR PROGRAMMING

40

SUB-OPTIMAL INTERFACE MANAGEMENT

12

INDEQUATE LOCAL TESTING

6

INADEQUATE CONTROL OF USER ACCESS

3

INADEQUATE SOFTWARE CHANGE CONTROL

0

INADEQUATE PROJECT MANAGEMENT

0

OTHER

7 0

5

10

15

20

25

30

35

40

45

Other Factors INADEQUATE TRAINING

26

COMPROMISED COMMUNICATION AMONG…

7

USE ERROR

5

INTERACTIONS WITH OTHER (NON-HIT) CARE…

4

PHYSICAL ENVIRONMENT

2

UNCLEAR POLICIES

2

EXCESSIVE WORKLOAD

1

INADEQUATELY SECURED DATA

0

HARDWARE FAILURE

0

INADEQUATE SYSTEM DOWNTIME MANAGEMENT

0

INADEQAUTE ORGANIZATION CHANGE… 0

OTHER

4 0

5

10

15

20

25

30

A Multi-Stakeholder Collaboration

Thank you. Please get involved!

Session 2: Patient Safety Organizations: The Engine For Collaboration

Real-Time Patient Safety Monday, February 29th 2016 William J. Andrews, MD. VP Client Development, Pascal Metrics

Conflict of Interest William J. Andrews, MD Salary: Yes Royalty: No Receipt of Intellectual Property Rights/Patent Holder: No. Consulting Fees (e.g., advisory boards): No. Fees for Non-CME Services Received Directly from a Commercial Interest or their Agents (e.g., speakers’ bureau): No. Contracted Research: No. Ownership Interest (stocks, stock options or other ownership interest excluding diversified mutual funds): Yes. Other: No.

Agenda • A Problem Worth Focusing On

• How We’re Thinking • Still Foundational: Culture Of Safety • The Control Tower: Surveillance Of All-Cause Harm In Real- Time • Real-Time Impact In Pascal’s National Collaborative

Learning Objectives Participants will learn how PSOs are fostering the ability of providers and HIT developers to work together in a confidential safety culture to facilitate the development of solutions. PSOs will share their safety programs and some of the solutions and best practices developed focusing on HIT Safety. • Learn how PSOs give providers tools to implement a Safety Culture and a learning system for HIT • Learn how PSOs have been working with HIT developers and providers to develop solutions • Learn of the recent and continuing best practice results of the Partnership for HIT Patient Safety

http://www.himss.org/ValueSuite

A Problem Worth Focusing On

Patient Safety Case In The HIT Era • Day-0. 54-year-old male with osteoarthritis undergoes left knee replacement • Day-2 post op, develops anemia. Given a transfusion. Anticoag stopped • Day-4 post op, patient develops leg pain and swelling. Treated with analgesia • Day-5 post op, DVT found. Placed on anticoagulation and discharged • Day-14 post op, readmitted for treatment of large infected knee hematoma • Multiple follow ups visits over 1 year and did not return to work for 6 months.

Patient Safety Case In The HIT Era • Day-0. 54-year-old male with osteoarthritis undergoes left knee replacement • Day-2 post op, develops anemia. Given a transfusion. Anticoag stopped • Day-4 post op, patient develops leg pain and swelling. Treated with analgesia • Day-5 post op, DVT found. Placed on anticoagulation and discharged • Day-14 post op, readmitted for treatment of large infected knee hematoma • Multiple follow ups visits over 1 year and did not return to work for 6 months.

Not detected in any of: • fully implemented HIMSS Level 7 EHR • fully functioning electronic incident reporting system • fully operational quality improvement/peer review program.

Downstream Impact Of Inpatient Harm Early investigation shows: • 30% risk of another complication on this admission • Double the risk of death • Prolong the length of stay by several days • Increase the cost of hospitalization by thousands of dollars • Increase risk of readmission at 30 days, 90 days, 6 months, and 1 year • Increase ambulatory services for 12 months after hospitalization • Increase risk of death for 12 months after hospitalization

Why Is That Case Interesting?

Why Is That Case Interesting? We know it happens a lot:

33% patients suffer adverse events. Half extend care.

Why Is That Case Interesting? We know it happens a lot:

33%

$100B

patients suffer adverse events. Half extend care.

in additional cost due to harm in the U.S.?

Why Is That Case Interesting? We know it happens a lot:

33%

$100B

440,000

patients suffer adverse events. Half extend care.

in additional cost due to harm in the U.S.?

American lives lost each year as a result?

Takeaways: • Harm is frequent (2030%) • Harm is costly ($500M) • Hospitals lose money (27% of harm cost)

Harm Reduction Improved Adventist Health System Financial Performance Using 2009 as the baseline year compared to years 2010-2012 for 24 hospitals in AHS: • Saved $108M in Total Cost

• Saved $48M in Variable Cost • Improved bottom line by $18M • Reduced ~60,000 inpatient days

How We’re Thinking

1. A Model Shift Library

1. A Model Shift Library

The Enabler: Real-time clinical data delivered by health IT

Control Tower

2. Metrics Of Safe Care

Safety Inputs Safety Outcomes

2. Metrics Of Safe Care • What are the human factors that drive safe care? • How can we measure them reliably? • How can we make this information useful?

Safety Inputs Safety Outcomes

2. Metrics Of Safe Care • What are the human factors that drive safe care? • How can we measure them reliably? • How can we make this information useful?

Safety Inputs Safety Outcomes • What is your rate of all-cause harm? • What is your pattern of all-cause harm? • How can we see and act on that that in real time?

3. Government ‘Moat’ Around Patient Safety Analytics (PSA) PSA: PSO Protected Safety Inputs Safety Outcomes

EHR Analytics

“Above the EHR” Analytics

Still Foundational: Culture Of Safety

Culture Is Related To Clinical Outcomes Higher HCAHPS scores with good culture

Lower adverse events with good culture 100

350

100

100 90

300 80

80

80

250 60

200

70 60

60 50

150

40

40

40 30

100 20

20

20

50 10 0

0 Unit A

Unit B

Unit C

Adverse Events

Unit D

0

0 Unit A

Unit B

Unit C

HCAHPS

Unit D

Culture Lives At The Clinical Unit Level Hospital Level Data

Unit Level Data

Up to 5-6 fold variance between clinical units across a given hospital

Culture Matters, And Can Change GENERATIVE Organizational Culture “Genetically-wired” to produce safety

PROACTIVE “We methodically anticipate”— prevent problems before they occur

SYSTEMATIC Systems being put into place to manage most hazards

REACTIVE “Safety is important. We do a lot every time we have an accident”

Where are you? UNMINDFUL “We show up, don’t we?” Chronically Complacent

The Control-Tower: Surveillance Of All Cause Harm In Real-Time

Please use this blank slide if more space is required for charts, graphs, etc.

To remove background graphics, right click on selected slide, choose “Format Background” and check “Hide background graphics”.

It’s Clear, We Seek “Surveillance” In Healthcare

90% discuss patient harm routinely within their teams

It’s Clear, We Seek “Surveillance” In Healthcare

90% discuss patient harm routinely within their teams

98% lack the data to discuss patient harm in real-time

PSO Control Tower: Providing Actionable Info In Complex Environment

System Level Leadership

PSO

Hospital Level Leadership

Quality/Safety/Risk Team

Unit Level Team UNIT

OR

ED

MS

OB

Real-time HL7 feeds from health IT systems to cloud

Sample Of 120+ Automated Triggers 3rd or 4th degree vaginal laceration after delivery. Administration of Digoxin Immune Fab (Digibind). Administration of Naloxone (Narcan). Administration of Protamine Sulfate after Heparin. Administration of Romazicon (Flumazenil). Administration of Sodium Polystyrene (Kayexalate) and serum Potassium > 6.0. Administration of Vitamin K after Warfarin. Antifactor Xa > 0.71. Any death. Any hospital visit (ER and IP) within 30 days post surgery. Any hospital visit (ER and IP) within 90 days post implant type surgery. Clostridium Positive Stool Decrease in Hemoglobin (Hgb) > =25 % within 5 days postoperatively. Decrease in Hemoglobin (Hgb) >=25% within 48 hours. Glucose = 250 back to back. Heparin Induced Thrombocytopenia antibody. ICU Readmission within 24 hours. INR > 6 Intubation. Possible acute kidney injury (Creatinine > 2 * baseline and/or patient receiving nephrotoxic medication). Patient fall. Post-operative increase in Troponin level > 0.09. Pressure Ulcer. Pressure Ulcer - Hospital Acquired. PTT > 100. PTT > 100 and Heparin within previous 24 hrs. Radiology Study for Emboli or DVT. Radiology Study for Emboli or DVT within 30 days after surgery. Return to Surgery… etc.

Trigger Monitoring



Trigger Monitoring

Authenticating A Trigger



Harm Analytics Dashboard

Harm Analytics Dashboard

Harm Analytics Dashboard

Harm Analytics Dashboard

Real-Time Impact In Pascal’s National Collaborative

Clinical Impact 1.

Population-Level Diffuse Pattern Recognition: Increased central line thrombosis rate. Frontline aware of individual cases, but blind to wider pattern. RTM uncovered pattern resulting in improvement process.

2.

Clinical Skills Deficit Recognition: Incorrect diagnosis of pressure ulcers. Pattern causing significant overtreatment and cost. Nurse education program implemented to recognize and treat the condition appropriately.

3.

Inappropriate Practice Recognition: Nurse practitioners routinely administering anesthetic reversal agents post-procedure without reason and in contravention of protocol. Issue addressed to prevent further harm.

4.

Dangerous Protocol Recognition: Pattern of anticoagulation-induced bleeding - from slight harm through to death. Hospital revisited protocols, found flaw and implemented a revised procedure.

Financial Impact $1MM per 100 beds at a minimum… Leave alone: • CMS penalties • Readmission costs • Ambulatory care costs’ • Liability costs

Operational Impact “To be honest, we thought this would be a fringe activity: something we do because we are especially focused on patient safety and finding every opportunity to improve. In fact, this has become central to our activity. It tells us more about how we practice medicine than any other technology we have. We have only scratched the surface of what this view of the world will show us.”

Operational Impact “We can get hung up on RATES of harm. Actually, harm is one of those things we should measure in absolute terms; event by event, child by child.”

Predictive Safety Analytics In Beta

Predictive Safety Analytics In Beta

Right Now, System Is: • PSO protected • Finding patients injured or dying from care, not disease • Revealing high number of preventable harm events • Indicating financial impact of clinically confirmed harm

• Measuring 10x more harm than event reports • Getting harm into workflow so it makes a difference

1. A Model Shift Library

The Enabler: Real-time clinical data delivered by health IT

Control Tower

2. Metrics Of Safe Care

Safety Inputs Safety Outcomes

3. Government ‘Moat’ Around Patient Safety Analytics (PSA) PSA: PSO Protected Safety Inputs Safety Outcomes

EHR Analytics

“Above the EHR” Analytics

http://www.himss.org/ValueSuite

Questions

DR. WILL ANDREWS

Vice President, Client Development | Pascal Metrics +1 202 470 4342 | [email protected]