influencing patient safety in rural primary care clinics through computerized order entry
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Influencing Patient Safety in Rural Primary Care Clinics through Computerized Order Entry. Matthew Samore, MD VA Salt Lake City Health Care System Professor of Internal Medicine Adjunct Professor of Biomedical Informatics University of Utah. Acknowledgments. Kim Bateman, MD - PowerPoint PPT PresentationTRANSCRIPT
Influencing Patient Safety in Rural Primary Care Clinics through Computerized Order EntryMatthew Samore, MD
VA Salt Lake City Health Care SystemProfessor of Internal MedicineAdjunct Professor of Biomedical InformaticsUniversity of Utah
Acknowledgments
Partners: University of Utah, Healthinsight, CaduRx Funding:
INFORM study: AHRQ R01 HS15413
Kim Bateman, MD Frank Drews, PhD Wu Xu, PhD Brian Sauer, PhD Shobha Phansalkar,
PhD Charlene Weir, PhD Jonathan Nebeker, MD
Amyanne Wuthrich Jose Benuzillo Warren Pettey Marjorie Carter Marci Fjelstad Rui Saito Shuying Shen
Outline Background
Conceptual framework Related work
Our active studies in patient safety Patient safety and health information
technology in rural settings: the INFORM study Methods
Participating clinics Health information technology Measurements
Results to date Summary and future directions
Conceptual framework Natural vs. engineered systems
Natural systems Living organisms Weather systems
Engineered systems Aircraft Computers
Conceptual framework Commonalities
Defining properties and functional capabilities are causal Internal component relationships are
deterministic Potentially predictable relationship between
internal components of system
Conceptual framework Differences
Adaptability How does system deal with change
Natural systems evolved No spontaneous change of defining parameters Adaptation as a result of unstable previous
states, to meet the changing demands placed upon by environment
Technical systems Intentional design and manufacturing to provide
solution to a specific, practical problem Suitable to perform a number of specific tasks
Conceptual framework Differences
Transparency Observers ability to observe and understand the algorithms
that govern a systems operation Natural systems
Non-transparent Algorithms describing component interactions and state
variables have to be deduced and are not fully understood Models – describe and mimic behavior
Engineered systems Designed for transparency
Relationships are planned and calculated during design process Despite transparency have complex systems sometimes
unpredicted and unanticipated emergent behavior Transparency affects the ability to cope with changes of system
Conceptual framework Differences
Linearity Mathematical description of the relationship
between output of system to its input Natural systems
Often display non-linear behavior Engineered systems designed to be linear
Makes it possible to solve linear problems analytically
Linear systems output can be predicted from input Allows application of reductionistic approach
Conceptual framework Differences
Predictability Allows for anticipation of future state of system
Natural systems More challenging to understand and modify Partly due to adaptive and non-linear nature Even simple natural systems difficult to predict
Engineered systems Easier to understand and maintain Higher complexity increases operator demand Maintenance and prediction are possible
Conceptual framework Clinical care delivery
Complex natural system that varies across myriad settings and care
Health information technology implementation Engineered system meets natural system
Our active studies in patient safety Medication management
Real-time detection of prescribing problems Medicaid population (Nebeker, Xu, and Sauer) VA health care system (Nebeker and Weir)
Clinical decision support systems in rural settings Primary care (Samore and Bateman) Nursing homes (Rubin)
Optimal laboratory monitoring intervals (Sauer) Error-producing conditions in intensive care
units, including interruptions and task ambiguity Medical device problems
Use of observation (Drews and Samore)
Methods INFORM study
Clinic-randomized trial to evaluate impact of computerized clinic order entry tool on clinical practice and office efficiency
Features of computerized clinic order entry tool Web-based writing of outpatient orders, including
prescriptions, immunizations, laboratory tests, X-ray studies, and work notes.
Accessible via any type of computer. Electronic transmission of orders to pharmacies and
other vendors or to local printers Common access to records across providers who
share patients while maintaining strict levels of confidentiality.
Methods: participating clinics Rural primary care clinics with
minimum of two providers were recruited None had pre-existing electronic health
records Eighteen clinics randomly assigned to
two groups Early implementation (launch in year 1) Delayed implementation (launch in year 2)
Post-launch, 2 clinics closed
Methods: participating clinics
Methods: health information technology user interface
Fumbling for his recline button, Ted unwittingly instigates a disaster.
Methods: provider home pageList of all refill requests your staff
has submitted for your approval Pt: Search for a patient record
Methods: refill requestsSelect patient name to review medication history
Select drug to review request and to edit, approve or deny
Methods: patient home pageA brief tour:
Status barNavigation commands Special functions
Active drug listFiltersStatus icons
Methods: drug favorites
Methods: drug interactions
Methods: mild drug interactions
Methods: measurements Observation of office processes
Efficiency of refill process Survey:
Theoretical framework of Information Technology Adoption Model (ITAM)
Focus groups “Time trials” using scenarios
Comparison handwritten and electronic prescription writing
Time-to-complete task Assessment of completeness, legibility, errors
Electronic prescriptions Chart review (Fall, 2007)
Methods: experimental scenarios Instructions for time trials
“For these scenarios, imagine that you’re with a real patient and work at your normal pace.”
Example: “Next you see a regular patient, Abigail B.
Cook. She is 74-year old with a history of rheumatoid arthritis. She also complains of pain when she urinates. The urine culture reveals that the she has a UTI due to E. coli that is resistant to fluoroquinolones. Prescribe an antibiotic to treat her UTI”
Results: impact on workflow Medication refill
system in one participating clinic before implementation of computerized clinic order entry
Post-implementation of computerized clinic order entry
Results
Results: survey Response to one of the questions
pertinent to patient safety: “Integrating drug reference and drug
interaction look-up with prescription writing is useful” 57% (26 of 45) strongly agree
(answered 6 or 7 on 7 point Likert scale) 9% (4 of 45) strongly disagree
(answered 1 or 2 on 7 point Likert scale)
Results: focus groups Favorable:
“We see the interactions. That helps quite a bit. So, that has made a difference a couple of times. Essentially changed my mind…”
“One thing I liked, that there was a medicine that I don’t prescribe very often and didn’t know about the dose…”
Results: focus groups Unfavorable
“it’s like getting those stupid things from the pharmacy…. It commented on too many menial, insignificant things that just waste your time…. Way too sensitive.”
“… the drug to drug reaction says “major” and you Look at other… look at ePocrates later and no reaction at all.”
Results: time trials 65 66
31
20.5
38
56.5
47.5
34.5
0
10
20
30
40
50
60
70
New familiar drug New unfamiliardrug
New dose Refills
Med
ian
Dur
atio
n (S
econ
ds)
CCOE PAPER
Results: drug interaction scenario Frequency with which trimethoprim/sulfa
was prescribed to elderly woman on methotrexate for rheumatoid arthritis Handwritten
11 of 13 instances Computerized clinic order entry tool
3 of 12 instances Recommendation of the Multum database
“Generally avoid”
Results: electronic prescriptions Analysis of instances of significant drug-
interactions is in progress Altogether:
430,000 electronic prescriptions 52,000 unique patients
Coumadin interactions 7 patients received coumadin and
amiodarone concomitantly 29 patients received coumadin and NSAID
(excluding celecoxib)
Summary and future directions Implementation is clinical care system
redesign Measures of impact
“Perceived usefulness” versus “average effect” “Experiment” versus “experience in the wild” “Errors” versus “adverse events”
Transition to health information technology that enhances patient-centered care