# Presentation: 841801

## Using CPOE to Prevent Adverse Drug Events

- David W. Bates,
- Medical Director of Clinical and Quality Analysis, Partners Healthcare
- Chief, Division of General Medicine,
- Brigham and Women’s Hospital

## Goals

- Public’s view
- Theory of error and care improvement
- CPOE and associated applications
- Epidemiology of medication errors and ADEs
- ADE prevention
- Future of medication systems

## Leadership and IT

- Leadership is the capacity to hold a shared vision of that we wish to create.
- 					– Peter Senge

## Reengineering Medicine:The Role of IS

- Could be changed by providing external aids
- Linking medical knowledge and patient-specific data
- Identifying options
- Without such tools, experts
- Make errors
- Overlook available knowledge
- Don’t sufficiently account for uniqueness
- Patients could participate in decision-making

**Weed LL, Weed L, *****Federation Bulletin*****, 1994**

## The (first) IOM Report

- Report targets hospital errors: Mistakes killing thousands every year  *11/30/99*
**Medical errors kill 44,000-98,000 people per year**
**“****More people die from medical errors each year than from suicides, highway accidents, breast cancer, or AIDS”**
- “These stunningly high rates of medical errors - resulting in deaths, permanent disability, and unnecessary suffering - are simply unacceptable in a system that promises to first ‘do no harm.’”                                                       					*William Richardson*

## What Does the Public Think?

- 42% of Americans report either experiencing or having a family member or friend experience a medical mistake within 5 years
- 100 million Americans
- Half of surveyed felt major problem was bad providers
- Three quarters feel getting rid of bad providers would be the most important change for improving the problem

***Source: Lou Harris, 1997***

## Reporting and Punishmentof “Bad” Health Care Providers

- Advocated by some such as Sidney Wolfe
- Plays some role--there are a few “bad apples”
- Problem--will have little/no impact on system performance
- Almost all errors made by good people

## Slide 8

## Theory of Error: Why Do Errors Occur?

- Errors are very common
- Complex cognitive mechanisms involved
- Slips--errors of implementation
- Low-level, semi-automatic behavior used for many routine daily activities
- Mistakes--errors of higher functioning
- Occur in new, or non-stereotypic situations
- Common causes include
- Distractions
- Overly high workload

## Handwriting example

## Systems Approach to Error

- Most important cause of error is faulty systems or design
- Assumes that individuals are doing their best
- Accidents result from defects in systems design that allow operator error to result in an adverse outcome

## Computerized Physician Order Entry

- Application that allows physicians to write all orders
- Most things that happen in hospitals occur as a result of orders
- Computerizing process structures, allows contact at key times

## Types of Decision Support

- Information display
- Generic across patients
- Patient-specific
- Reminders
- Alerts
- Guidelines/Algorithms

**Notes:**
Next, there are a variety of types of decision support.  These include information display which can be either generic across patients (for example, the charges for tests) or patient-specific--the patient's last potassium.  Reminders are things which help prevent errors of omission, for example to order gentamicin levels when beginning gentamicin.  Alerts point out events that the physician wasn’t aware of, for example that a potassium result has just been recovered that is dangerously high.  And guidelines and algorithms can help structure decisions in specific situations, for example in designing whether to order ankle films in a patient with ankle pain.  Most of what we’ve done to date has to do with the first three, but I think a lot of the next few years will be spent on on computerization of guidelines, and I’d like next to discuss the rationale for this.  

## Advantages of Computerized Guidelines

- Facilitate memory, always findable
- Immediately generalizable to all providers
- Possible to point providers to them
- Facilitates central control
- Allows measurement of outcomes
- Whether people use
- Patient outcomes
- Get feedback to developers
- Iterative refinement

**Notes:**
So, why computerize guidelines? Although lots of guidelines have been developed, it's been a struggle to get people to actually use them.  Computerization can help in a number of was; perhaps most important, they facilitate memory and are always findable.  How many of you remember the outstanding guideline for when to do an ankle radiograph?  I can’t.  They're immediately generalizable to all patients and provides, an enormous advantage.  It’s possible to point providers to them--no one can keep track of all the guidelines we have at a place like this.  Further, some central control is necessary, so that inappropriate guidelines aren’t being used, and this is much easier with computerization.  And with computerization, you can measure outcomes--how often do people use them, and if not, why.  And finally, it's easy to get feedback to developers and the guidelines can then be iteratively refined.   

## Event monitor architecture

**Event monitor architecture**

- Rule editor

- Knowledge base

- Inference engine
- (decisions)

- Applications (new data)

- Applications (new data)

- Applications (new data)

- Patient database

- Annun-ciators

- page, email, write to file, [real time message]

- Coverage List

**Notes:**
The architecture for our event monitor is shown here.  As data is written to the patient database (for example new orders or new lab results), a copy of the data is sent to our inference engine for evaluation.  The inference engine examines a knowledge base to determine if any rules pertain to this piece of new data and then performs the evaluations.  The rules have been created by a rule editor.  The inference engine may have to examine other patient data to determine if the rules are true.  

If a rule is found to be true, an annunciator is called to communicate the message.  The knowledge base contains information about how the event should be communicated.  If a clinician needs to be contacted, the coverage list database tells who is covering and the physician can be paged or sent email. Other events, such as possible adverse drug events, are simply written to a file for later review.  Also, if this process occurs in the middle of an interactive session, a message can be presented to the user in real time.  

## Physician Coverage List

- Functions
- Identifies first and second-call physicians
- Manages physician rotation
- Handles evening coverage and signing out
- Facilitates delivery of computer-generated messages
- Computer-page interface allows automated paging

## Development and Implementation of POE

- Developed at BWH beginning in 1993
- Physician involvement and leadership
- Decision to automate existing systems as is
- Constant focus on speed
- Strong support from hospital administration
- Willingness to be flexible, modify system

## Relationships Between Med Errors, Potential ADEs and ADEs

**Pot**
**ADEs**

**Medication**
**Errors**

**Non-Preventable ADEs (ADRs)**

**Preventable**
**ADEs**

## Detection of ADEs

- Most hospitals depend upon spontaneous reporting
- Spontaneous reporting reveals few events
- Only about 1 in 20

## Medication Error Frequency and Potential for Harm

-  In 10,070 Orders
- 530 Medication Errors	 1.4 per admission
-   35 Potential ADEs
-     5 Preventable ADEs
- 1 in 100 medication errors results in an ADE
- 7 in 100 represent potential ADEs

## Pediatric Results

- 1120 patients admitted to 2 pediatric hospitals
- Orders reviewed		10,778
- Medication errors	   	     616 (5.7%)
- Potential ADEs		             115 (1.1%)
- ADEs				        26 (0.24%)
- 	Preventable ADEs	                     5 (19%)

*JAMA 2001; Kaushal et al*

## Pediatric Conclusions

- Rate of medication errors similar to adults
- Rate of potential ADEs higher in pediatrics
- Highest in the NICU
- Most occurred at the ordering stage (79%)
- Overly high dosing biggest issue (34%)
- Intravenous drugs a major problem (57%)
- Potential prevention strategies
- CPOE 93%
- Unit-based clinical pharmacists 94%

## ADE Prevention Study: Key Results

- 6.5 ADEs/100 admissions
- 28% preventable
- 3 potential ADEs for every preventable ADE
- 62% of errors at ordering and transcription stages

***JAMA *****1995;274:29-43**

**Notes:**
Although I noted earlier that I would talk about improving quality, a key part of quality improvement is demonstrating that a problem or opportunity exists to begin with.  Even if something is an issue elsewhere, there is no guarantee that it is a local issue, so in general we have taken the approach: collect datea, determine whether there is an opportunity for improvement, and then implement a quality improvement strategy.
	The most ambitious project I’ve been involved with here is the Adverse Drug Event Prevention study, which we began 5 years ago.  This study was designed to evaluate how common adverse drug events, or injuries due to drugs, were in hospitalized patients and what could be done to prevent them.  It was done here at BWH and at MGH.  Key findings were that there were 6.5 ADEs for every 100 admissions, of which 28% were preventable, that there were almost 3 potential ADEs or errors with potential for harm that didn’t result in an injury for every preventable ADE, and that 62% of errors occurred at the ordering and transcription stages of the medication process.  Much of the previous work in this area had focussed on the dispensing and administration stages, which accounted for relatively few of the serious errors.  These data suggested that a physician order entry system had great potential to have an impact on this problem.
	Another this we did was to have a multidisciplinary group perform a detailed system analysis of every serious error.  Some of the key findings were that no individual exhibited a pattern of repeated errors, and that systems should be designed to make errors less likely, and to catch those that did occur, both of which could be addressed by order entry.

## Error Stage for Preventable ADEs and PADEs (n=264)

## Annual Projections per Hospital

- 6,000,000 medication orders
- 300,000 medication errors
- 1900 ADEs
- 530 preventable ADEs
- 1370 non-preventable ADEs
- 1600 potential ADEs

## Administration’s Awareness of Medication Errors

***Source: Brusking Goldring Res, 1999***
***sponsored by BD.id***

***(n=80)***

***(n=81)***

***(n=60)***

## Costs of ADEs

- ADEs are expensive
- $2461 per ADE, $4555 per preventable ADE
- ADE average similar to Utah figure
- Annual BWH costs $5.6 million for all ADEs
- $2.8 million for preventable ADEs
- These figures exclude costs of:
- Injuries to patients
- Malpractice costs
- Costs of admissions due to ADEs
- Figures justify investment in prevention efforts

***JAMA *****1997;277:307-311**

## Efficiency of Incident Report System for Identifying ADEs

- Standard IR system was in place
- Included a pharmacy hotline
- Only 3 of 54 ADEs identified by study (6%) had an incident report
- 2 of 26 serious ADEs had an incident report
- Implications--
- Rates of IRs not useful since denominator unknown
- May still get useful information on individual events
- Supplementation by computerized detection systems may help

***Cullen et al, J on Qual  Improvement, 1995:10***

## ADEs in Nursing Homes

- ADEs: n=546
- Rate: 1.9 per 100 resident-months
- Preventable ADEs: n=276
- Rate: .96 per 100 resident-months
- 72% of serious events preventable
- 47% of errors at ordering stage, 53% at monitoring
- Psycho-active drugs and anticoagulants were most commonly implicated in preventable ADEs

***Gurwitz, Am J Med 2000***

**Notes:**
In other words,     if you have 100 residents, 2 events per monthApproximately half of the events we found were judged preventable - high rate

## Computerized Surveillance for ADEs in Hospitalized Patients

- Method: computerized detection with pharmacist followup
- 18 months; 36,653 patients
- 731 ADEs in 648 patients
- Only 9 ADEs detected using traditional methods
- Pharmacists, nurses and MDs voluntarily reported 92 of 731
*                                         **Classen, JAMA 1991;266:2847-2851*

## BWH Computer ADE Monitor

- 150 new pos screens/day
- ~15% represent ADEs
- Doesn’t identify potential ADEs
- 54% of ADEs found through this route serious, life-threatening or fatal
- Analysis of monitor vs. chart review
- Many do not overlap, but substantial overlap for severe ADEs

## Routine Operation

- Monitor can write a file including a variety of items which pharmacist can then use in daily activities along with other screens
- IV-PO switches
- Formulary exceptions
- Guideline overrides
- Pharmacists can use central database
- Spontaneous reporting also useful, especially for potential ADEs

## Streamline, structure process
Doses from menus
Decreased transcription
Complete orders required
Give information at the time needed
Show relevant laboratories
Guidelines
Guided dose algorithms
Perform checks in background
Drug-allergy		Dose ceiling		Drug-lab
Drug-drug		Drug-patient 

- Doses from menus
- Decreased transcription
- Complete orders required
- Give information at the time needed
- Show relevant laboratories
- Guidelines
- Guided dose algorithms
- Perform checks in background
- Drug-allergy		Dose ceiling		Drug-lab
- Drug-drug		Drug-patient

- Improving the Quality of Drug Ordering with Order Entry

## Allergy to Medication

## Chemotherapy Order:Patient Characteristics

## High Chemotherapy Dose Warning

## High Chemotherapy Dose:Requires Attending Physician’s Approval

## Results of Two Studies on Medication Error Prevention

- First study uses non-missed dose medication errors as main outcome
- Design--interrupted time series over 5 years
- Intervention--POE at several stages of development
- Units studied--three medical units
- Second study uses serious medication errors as main outcome
- Design--controlled trial, over 15-month period
- Intervention--POE at one stage, and a team intervention
- Units studied--6-8 units

***JAMA *****1998;280:1311-6**

## Systems Characteristics By Period

- Baseline
- Orders written on paper
- No automated decision support

## System Characteristics By Period

- Period 1
- Complete orders required
- Medication name, dose and frequency from lists
- Relevant laboratories displayed
- Decreased transcription
- Rudimentary drug-allergy checking
- Rudimentary drug-drug interaction checking
- Notification for several drug-laboratory problems
- Many orders entered using preapproved order sets

## Systems Characteristics By Period

- Period 2
- Improved drug allergy checking
- Period 3
- Improved potassium ordering
- Improved drug-drug interaction checking

## Non-Missing Dose Medication Error Rate

## Impact of CPOE and Team on Serious Medication Errors

- POE Intervention
- All orders complete
- Transcription minimized
- Early checking including drug-allergy, drug-drug
- Team Intervention
- Changing role of pharmacist
- Computerized drip rate calculation program
- Standardized labeling of bags, tubes and pumps
- Implementing pharmacy communication log to facilitate communication between nursing and pharmacy

## Comparison of Serious Medication Error and ADE Rates 

## Paired Comparison of Preventable ADEs and Non-int Potential ADEs

## Effect of  Changing Default Dosing Frequency for Ceftriaxone

## Guideline for Expensive Agent

## Rough Cost-Benefit for POE

- Costs:
- Development 	$1,000,000
- Hardware		$400,000
- Maintenance	$500,000/year
- Benefits:
- Overall 		$5-10 million/year charges
- Main savings relate to efficiencies re drugs, ADE prevention, and tests
- Many other interventions coming on line all the time

## Pharmacist Participation on Physician Rounds in the ICU

- Before/after comparison in two ICUs, one with intervention in Phase 2, one control
- 66% decrease in preventable ordering ADEs from Phase 1 to 2
- 10.4/1000 pt-days to 3.5
- Pharmacist made 366 recommendations, 362 (99%) accepted

***Leape et al, JAMA 1999:282:267-70***

## Adopting A Systems Approach

- In the ADE Prevention Study, no individual was found who had a pattern of repeated errors
*Barriers*
- Traditional culture of medicine is to blame the individual who erred
- Pressures for administrators to scapegoat workers
- Rational commitment to consider errors “systems problems” hard to maintain
- Rewards for revealing errors are lacking

## Regulatory Developments

- HCFA is developing new rules
- May mandate on-going computerized monitor
- Initial regs set upper limit of 0 serious medication errors, are being redone
- MEDPAC report suggested financial incentives for POE implementation
- Leapfrog effort
- Financial incentives
- Proposed amendment to the BBA

## California Senate Bill 1875

- Condition of licensure that facilities have a plan by 1/05 to reduce medication errors
- Plan submitted by 1/1/02
- Except for small/rural hospitals must include technology such as but not limited to CPOE
- No reimbursement
- Similar legislation planned in New York, NJ, several other states

## Medication System of the Future

- Providers write orders on computerized systems, get feedback
- Face-to-face counter-detailing for complex problems
- Orders sent directly to pharmacy (review)
- Simple orders filled using automation
- Point-of-care delivery devices linked with order-entry systems dispense medications
- All drugs, patients, personnel bar-coded
- Computerized MAR records what given, when

## Reducing Error in Medicine

- “I don’t want to make the wrong mistake.”
- 					Yogi Berra