Slaying the HIT Dragon David Bates, MD, MSc Chief, Division of General Internal Medicine, Brigham and Women’s Hospital Medical Director of Clinical and Quality Analysis, Partners Healthcare Past Board Chair, American Medical Informatics Association Nice, 2010

Overview Background Specific technologies –Computerized physician order entry The right medication-related decision support –Bar-coding –Smart pumps –Computerization of handovers –Results management (outside hospital) Transforming care Conclusions

The Dragon HIT offers enormous promise for improving safety and quality –But many organizations have struggled –Some reports that safety has even gotten worse –Technology is expensive and failure is hard to contemplate When to move? And who will win?

Barriers for Hospitals Capital Uncertainty about vendor systems Typically stuck with one vendor Computerized physician order entry represents a major behavioral change Lack of standards Little interoperability of clinical data with outside world No financial incentives to deliver safer care

Typical Scenario CEO has many competing priorities Hard to pick among specific HIT solutions –Big ones take time –Risk of failure higher with this than with a new MRI for example –Many purchases are infrastructure—ROI tricky Have been uncertainties about whether upgrades will cause problems –Standardization vs. local tailoring Hard to decide when to pull the trigger

Message of Today Stars are now in alignment Federal financial incentives now in place Additional incentives to organizations for delivering safer care Vendor systems are improving rapidly –Still not perfect but good enough Data exchange also coming fast Time to get off the sidelines

Meaningful Use Matrix and Decision Support: Hospitals % all orders through CPOE Drug-drug, drug-allergy, drug-formulary checks Up-to-date problem list Generate lists of patients by condition Implement one clinical decision rule related to a high-priority condition

Inpatient Prevention 55% reduction in serious medication error rate with CPOE Bates, JAMA, % reduction in overall medication error rate Bates, JAMIA, 2000

NEPHROS study Effect of real-time decision support for patients with renal insufficiency Of 17,828 patients, 42% had some degree of renal insufficiency Interv Control Interv Control Dose67%54% Frequency59%35% LOS 0.5 days shorter Chertow et al, JAMA 2001

Medication Safety: Refining the Rules In most systems most alerts get overridden We identified a highly selected set of drug alerts for the outpatient setting Over 6 months, 18,115 alerts –12,933 (71%) non-interruptive –5,182 (29%) interruptive Of interruptive, 67% were accepted Shah, JAMIA 2006

Dispensing Errors and Potential ADEs: Before and After Barcode Technology Implementation Projections for errors prevented per year at study hospital: >13,500 medication dispensing errors >6,000 potential ADEs 31% reduction* 63% reduction* * p< (Chi-squared test) Poon, Ann Intern Med, 2006

Safe IV Systems: Smart Pumps Smart pumps can warn nurse when administering IV drugs Few administration errors get caught –Yet intravenous errors can be especially dangerous Case Heparin bolus dose of 4000 units, followed by an infusion of 890 units/hr –4000 unit bolus dose was given appropriately –But nurse misinterpreted the order and programmed the infusion device to deliver 4000 U/hour, not 890 U/hour Smart pump alerted nurse ISMP Newsletter Feb 6, 2002

Take-Away Messages of Smart Pump Controlled Trial Serious IV med errors were frequent and could be detected using smart pumps However, no impact on the serious med error or preventable ADE rate was found –Likely because of poor compliance Behavioral and technologic factors must be addressed if smart pumps are to achieve their potential Rothschild et al, Crit Care Med 2005

Coverage-Related Events Before data showed patients being cross- covered at 5-fold excess risk of adverse event After computerized sign-out introduction, no excess risk –Included medications Simple from informatics perspective but major benefit Petersen, Jt Comm Jl

Dilbert

Results Manager Home Page

AHRQ/NQF/Leapfrog “Flight Simulator” Assessment Tool for CPOE Hospital logs on (Password access) Complete sample test Obtain patient criteria (Adult or pediatric) Program patient criteria Download and print 30 – 40 test orders (HM if AMB) Enter orders into CPOE application and record results Hospital self- reports results on website Score generated against weighted scheme Report generated Aggregate score to Leapfrog Order category scores viewed by hospital Review patient descriptions Review orders and categories Review scoring The Assessment Tool

Safety Results of CPOE Decision Support Among Hospitals 62 hospitals voluntarily participated Simulation detection only 53% of orders which would have been fatal Detected only 10-82% of orders which would have caused serious ADEs Almost no relationship with vendor Metzger et al, Health Affairs 2010

Copyright ©2010 by Project HOPE, all rights reserved. Jane Metzger, Emily Welebob, David W. Bates, Stuart Lipsitz, and David C. Classen, Mixed Results In The Safety Performance Of Computerized Physician Order Entry, Health Affairs, Vol 29, Issue 4,

Have to Implement Well Changes like CPOE and bar-coding are transformational Can cause major problems if not handled well –Are now guides about what to do, what to avoid Keys to success with CPOE –Strong clinical and administrative leadership

High-Performing Healthcare System Initiatives 6 network-wide initiatives One focuses on IT –Inpatient CPOE –Outpatient EHR Another on safety –Standardizing medication-related decision support –Implementing proactive tools to look for ADEs, implementing standard web-based reported –Making more uniform decisions about administration –Standardizing information exchanged at transfers

What Will It Take to Transform Care? Safety Key issue is making essential processes more reliable –New approaches like CPOE, bar-coding, etc –Checklists And central line infection rates (Pronovost) And rates of ventilator-associated pneumonia Surgical checklists in the operating room (Gawande) Will likely need dozens of checklists Also essential to measure performance in on-going way

Conclusions Information technology is becoming ubiquitous in healthcare—near a tipping point –All organizations should get on the bandwagon—time is now –CAN slay the dragon—but need to play cards right –Tools like simulator can help EHRs and HIT more broadly can provide major benefits with respect to safety –Checklists –Reliable processes –Right decision support –HIT is simply a tool—part of a program –But nearly every other effort to improve safety/quality/efficiency will rely on HIT

Conclusions--Leadership Leadership must be involved, supportive –Clinical –Administrative –HIT is NOT like plumbing Will be more things than any organization can afford –Prioritization process key What vendor you pick is not the only decision –Need effective processes for incremental improvement –All organizations will need some in-house expertise –Processes around decision support especially important

“Insanity is doing the same things the same way and expecting different results” Albert Einstein