a primer · large-scale clinical information s yy,stems, such as hospital information systems...

Medical Informatics:A Primer

Jim Carpenter, RPh, MSRegional Information Services

Pro idence Health S stemProvidence Health SystemPortland, Oregon

email: [email protected]

How Far Have We Come?

1964 Medical Records Video

“The Future is Already HereThe Future is Already Here

It’s just not evenly distributed”j y

- William Gibson

My Story

Pharmacy School Hospital PracticePharmacy School Hospital PracticeCreated a program used by nurses in critical care to calculate drip ratesto calculate drip ratesExpanded to include patient discharge i f iinformationWhere is this all heading?

A Ha!

Pestotnik, Classen, Evans, Burke “ImplementingPestotnik, Classen, Evans, Burke Implementing Antibiotic Practice Guidelines Through Computer-Assisted Decision Support: Clinical and Financial Outcomes” Ann Intern Med. 1996:884-90CONCLUSIONS: Computer-aided decision support integrated with local clinician-derived practice guidelines (and antibiograms) can improve antibiotic

r d t d t biliz r f r i t tuse, reduce cost, and stabilize emergence of resistant pathogens

Medical Informatics?

Attended the 1996 AMIA Fall SymposiumAttended the 1996 AMIA Fall SymposiumGraduate work?P i l i PASCALProgramming class in PASCAL

Where We Were (Are?)

Not Just Doctors

Why Medical Informatics?

The Institute of Medicine (IOM)The Institute of Medicine (IOM) report

To Err is Human: Building a Safer Health SystemHealth System

documented that as many as 7,000 Americans die each year due toAmericans die each year due to medication use errors.

What is Medical Informatics?

“The scientific field that deals withThe scientific field that deals with biomedical information, data, and knowledge – their storage, retrieval, and optimal use for problem-and optimal use for problemsolving and decision-making”- Ted Shortliffe, MD, PhD

Why Medical Informatics?

Doubling of medical Knowledge / literature every 8 g g / yyears!Moore’s Law:

Processing power doubles approximately every 2 yearsAlmost every measure of the capabilities of digital electronic devices is linked to Moore's Law: processing speed,devices is linked to Moore s Law: processing speed, memory/hard disk capacity, even the resolution of digital cameras.

17 d l t t li i l i f ti i t17 year delay to get some clinical information into practice

From publication applicationp pp

History of Medical Informatics1950-1970:1950 1970:

Ledley and Lusted (Science) - computers can be used for medical diagnosis and therapy. Collen and colleagues investigate use of computers to improve clinical practice and outcomes at Kaiser Permanente. Warner and colleagues develop the first successful computerized diagnosis application (in the domain of congenital heart disease). Gorry, Barnett, and others develop expert systems based on Bayes theorem and extend the paradigm to include sequential diagnosis. Li db (CONSIDER) d E l (HEME) d l i iLindberg (CONSIDER) and Engle (HEME) develop criteria-based diagnostic systems. Bleich uses braching logic ("20 questions") to provide evidence-b d f di i f id b di dbased support for diagnosis of acid-base disorders.


The MUMPS programming language and operating system is d ddeveloped as a time-sharing system that supports efficient storage and retrieval of text-based clinical records. The first "Reminder and Alerting" systems appear and their

i i li i l i i d dpositive clinical impact is documented. Dedicated "legacy" systems, such as laboratory information systems (Lindberg) and pharmacy information systems begin to

l ith i l li ti fappear, along with commercial applications for admission/discharge/transfer, billing, and inventory. NLM introduces MEDLINE, a comprehensive computerized research literature databaseresearch literature database. Technicon develops the first Hospital Information System(HIS) at El Camino hospital in California.


Large-scale clinical information systems, such as g y ,hospital information systems (e.g., HELP at LDS Hospital in Utah) and electronic medical record

tem ( PROMIS t th U i r it f V rm t)systems (e.g., PROMIS at the University of Vermont) begin to appear at pioneering academic institutionsCommercial products with limited capabilities appear. Several prototypic demonstration systems employ symbolic artificial intelligence methods to support diagnostic and therapeutic decision supportdiagnostic and therapeutic decision support.


The INTERNIST-I system at the University of y yPittsburgh covers the whole spectrum of internal medicine and is subsequently shown to perform diagnoses in very difficult cases with an accuracy g y yequal to or at times better than that of experienced physicians. The MYCIN system for diagnosis and therapy ofThe MYCIN system for diagnosis and therapy of bacteremia and meningitis is developed and evaluated at Stanford. Kahneman, Slovic and Tversky explore and demonstrate the pitfalls of human judgment in diagnosis and other decision problems in their seminal g pbook Judgement Under Uncertainty: Heuristics and Biases.


Foundation and growth of the American Medical Informatics Association to a professional membership organization of 3000-4000 members, subsuming the American College of Medical Informatics. F h d l l di d i i fFurther development at leading academic sites of exemplary clinical information and electronic medical record systems (many sponsored through the National Library of Medicine's IAIMS initiative) with appearance of hundreds ofof Medicine s IAIMS initiative), with appearance of hundreds of new health-related informatics commercial endeavors. Emergence of medical informatics as an autonomous, rigorous field with well-defined focus and development ofrigorous field with well defined focus and development of high-quality formal medical informatics research and training programs with long-term funding by NLM (U.S.).


Adoption of medical informatics training, research, and p g, ,development as high-priority strategic goals in both the U.S. and the European Union. Exploration of sophisticated indexing and retrievalExploration of sophisticated indexing and retrieval terminologies and systems. Wide-spread use of machine learning, data mining,

d d di h d i bi di iand automated discovery methods in biomedicine. Development of messaging standards through Healthlevel 7 (HL7). v ( )

History of Medical Informatics1995-Today:1995 Today:

Development of extensive network-based papplications, such as client-server clinical information systems. Widespread adoption of microcomputer-basedWidespread adoption of microcomputer-based clinical applications by medium-to-large sized hospitals and clinics (e.g., laboratory data display; display of textual documents)display of textual documents).

History of Medical Informatics1995-Today:1995 Today:

Explosive growth of Internet and World-Wide-p gWeb-based applications (including telemedicine, telematics, and distributed databases). Near-completion of the Human Genome ProjectNear-completion of the Human Genome Project, with related explosive growth of bioinformatics research (including studies of the structure and function of micro and macro biomolecules linkage betweenof micro and macro-biomolecules, linkage between genes and diseases, and comparative genomics), and commercialization of bioinformatics (by Celera and

l th i )several other companies). Rise of consumer health informatics.

Incidence of Medication Errors

Roughly 400 000 medication errors occur inRoughly 400,000 medication errors occur in hospitals each yearA 2006 Institute of Medicine report suggests theA 2006 Institute of Medicine report suggests the figure could be cut substantially with the help of information technologyinformation technology.

Where do Med Errors Happen?

Prescribing – 39%Prescribing 39%Wrong dose – 38%Wrong choice 19%Wrong choice – 19%Allergy – 12%W f 6%Wrong frequency – 6%Drug interaction – 4%W D 2%Wrong Drug – 2%


Prescribing – 39%Prescribing 39%Wrong dose – 38% CPOEWrong choice 19% CPOEWrong choice – 19% CPOEAllergy – 12% CPOEW f 6% CPOEWrong frequency – 6% CPOEDrug interaction – 4% CPOEW D 2% ( d d) CPOEWrong Drug – 2% (advanced) CPOE


Dispensing – 11% of ErrorsDispensing 11% of ErrorsDecimal Point Error – 37%Calculation Error 23%Calculation Error – 23%Dosage Misdivided – 19%D N t Di id d 12%Dosage Not Divided – 12%


Dispensing – 11% of ErrorsDispensing 11% of ErrorsDecimal Point Error – 37% CPOECalculation Error 23% Advanced Rx SystemsCalculation Error – 23% Advanced Rx SystemsDosage Misdivided – 19% BCMAD N t Di id d 12% BCMADosage Not Divided – 12% BCMA


Transcription – 12% of ErrorsTranscription 12% of ErrorsIllegible Signature – 78% Time Missing 58%Time Missing – 58%Incomplete Order – 24%Ill ibl O d 20%Illegible Order – 20%


Transcription – 12% of ErrorsTranscription 12% of ErrorsIllegible Signature – 78% CPOETime Missing 58% CPOETime Missing – 58% CPOEIncomplete Order – 24% CPOEIll ibl O d 20% CPOE!!!!Illegible Order – 20% CPOE!!!!


Adminstration – 38% of ErrorsAdminstration 38% of Errors


Adminstration – 38% of Errors BCMAAdminstration 38% of Errors BCMA

CPOEAlerts

Reminders

CPOE=Computerized ProviderOrder Entry

ElectronicHealth Record Reminders

Med ImagesCT MR US NM

XR Card Endo Path

WaveformsECG EEG ICU

Multidisc Charting/Bar-coded Med Admin

MedSurg Crit Care OB Periop ED

General

MedSurg p

Machinef tt d MDLab reports X-ray reports General

dictationformatted MDprogress notes

Importance of Med Errors

7000 deaths per year7000 deaths per yearNumber of serious ADE’s is underestimatedMedication errors are the biggest fear ofMedication errors are the biggest fear of adults entering the healthcare systemPotential ADE rate 3 times greater in a pediatricPotential ADE rate 3 times greater in a pediatric setting

Lancet: Vol 351, Feb 1998; AJHP Vol 46, May 1998; JAMA Vol 285, April, 2001Lancet: Vol 351, Feb 1998; AJHP Vol 46, May 1998; JAMA Vol 285, April, 2001

Patient safety

Right now a major imperative for health systemsg j p yMarch 5, 2008 Wall Street Journal Article: Medication Errors / ADRs

Confusing packagingConfusing packagingStorage and free access to drug cabinets"In perhaps the most challenging step, hospitals are tackling h ' b d ' l i b h i l h idthe 'grab and go' culture in busy hospitals that evidence

increasingly shows causes errors," Hospitals also have begun to invest in health care i f rm ti t h l h b r d t m d " m rtinformation technology, such as bar code systems and "smart pumps" to prevent medication errors.

But Most Errors Are Intercepted

How? “Magic Nursing Glue”How? Magic Nursing Glue50% of physician errors intercepted33% f di i / i i33% of dispensing / transcription

ONLY 2% of administration errors are intercepted!!p

Putting patients at risk

Larger patient loads for nursesLarger patient loads for nursesHigher acuity patientsSh f N / Ph iShortage of Nurses / PharmacistsExplosion of new pharmaceuticals

500% increase in ten yearsOver 17,000 trade and generic namesg

Who is involved in medical W o s vo ved ed cinformatics?

Physicians, Nurses, Pharmacists, Lab, RT (all li i l di i li )clinical disciplines)

Programmers / DBA’sCognitive Scientists / EthnographersProcess / Workflow EngineersProcess / Workflow EngineersHuman Factors Experts

What is Pharmacy Informatics?

Using IT systems and tools to benefitUsing IT systems and tools to benefit Pharmacists, patients, and the practice of PharmacyPharmacyA vital part of a larger Healthcare Informatics continuumcontinuumGoals: advance the quality and effectiveness of h i f Ph b fi ithe practice of Pharmacy to benefit patients

Why Pharmacy Informatics?

Profession priority: less associate with Rx product p y pand more recognized as dispensers of information

I tit t f M di i (IOM) t dInstitute of Medicine (IOM) report recommends all programs that educate and train health professionals should adopt 5 core competencies

deliver patient-centered carework as a member of an interdisciplinary teamengage in evidence-based practiceg g papply quality improvement approachesuse information technology

Why Pharmacy Informatics?

OpportunitiesOpportunitiesVendor

Large Health Systems

Informatics Research

Consulting

Domain expertise advising all of theDomain expertise advising all of the above

What Inspires Informatics Work?

Feedback from clinician usersFeedback from clinician users

Current Informatics Research

Breakthrough (a.k.a. simple) ideas…

“The Checklist”12/10/07 New Yorker: Atul Gawande, MD

“What makes her recovery astounding isn’t just the idea that someoneWhat makes her recovery astounding isn t just the idea that someone could come back from two hours in a state that would once have been considered death. It’s also the idea that a group of people in an ordinary hospital could do something so enormously complex. To save y p g y pthis one child, scores of people had to carry out thousands of steps correctly”

“The average ICU patient required a hundred and seventy-eight individual actions per day, ranging from administering a drug to suctioning the lungs, and every one of them posed risks. Remarkably, g g , y p y,the nurses and doctors were observed to make an error in just one per cent of these actions—but that still amounted to an average of two errors a day with every patient”

http://www.newyorker.com/reporting/2007/12/10/071210fa_fact_gawande?printable=true

“The Checklist”

Aerospace Engineering – Human FactorsOctober 30, 1935, at Wright Air Field in Dayton, Ohio Initial test flight of the B-17 – spectacular crashInitial test flight of the B 17 spectacular crashMore training? No – a checklist

Using a checklist for takeoff would no more have occurred to a pilot than to a driver backing a car out of the garage. But this new plane g g g pwas too complicated to be left to the memory of any pilot, however expert.

With the checklist in hand, the pilots went on to fly a total of 1 8 million miles without one accident The Army ultimately1.8 million miles without one accident. The Army ultimately ordered almost thirteen thousand of the aircraft

“The Checklist”

“Medicine today has entered its B-17 phase. Substantial parts of what hospitals do—most notably, intensive care—are now too complex for clinicians to carry them out reliably from memory alone. I.C.U. life support has become too much medicine for one person to fly.”

Peter Provonost MD – Johns Hopkins University – line placement checklist

(1) wash hands with soap( ) p(2) clean the patient’s skin with chlorhexidine antiseptic(3) put sterile drapes over the entire patient(4) wear a sterile mask hat gown and gloves(4) wear a sterile mask, hat, gown, and gloves(5) put a sterile dressing over the catheter site once the line is in.

“The Checklist”These steps are no-brainers; they have been known and taught for years.

Seemed silly to make a checklist just for them. Pronovost asked the nurses in his I.C.U. to observe the doctors for a month as they put lines into patients, and record how often they completed each step.

In more than a third of patients, they skipped at least one.

Pronovost and his colleagues monitored what happened for a year afterward. The results were so dramatic that they weren’t sure whether to believe them: w y w w

The ten-day line-infection rate went from eleven per cent to zero. So they followed patients for fifteen more months.

Only two line infections occurred during the entire period.

They calculated that, in this one hospital, the checklist had prevented forty-three infections and eight deaths, and saved two million dollars in costs.

Some physicians are offended by the suggestion that they needed checklists

“The Checklist”

The Medical Informatics Spectrum

EHR’s / EMR’s

Telemedicine / Home Care /Telemedicine / Home Care / Hospice / E-Health

Home monitoring devices

Mobile ComputingMobile Computing

Computer-Aided Learning

The Medical Informatics Spectrum

Personal Health Records – Google getsPersonal Health Records Google gets involved:

http://www.technewsworld.com/story/Can-Googles-Midas-p // / y/ gTouch-Turn-Health-Record-Keeping-Golden-61826.html

single, unified electronic record when interacting with physicians and pharmacies.p

pilot with Cleveland Clinic Microsoft HealthVault a competing conceptChallengesChallenges

Data StandardsSensitivity of DataS itSecurity

Health Information on the ‘Net

"The Internet is like one of those garbage dumps outsideThe Internet is like one of those garbage dumps outside of Bombay. There are people, most unfortunately, crawling all over it and maybe they find a bit ofcrawling all over it, and maybe they find a bit of aluminum, or perhaps something they can sell. But mainly it's garbage "mainly it s garbage.

J h W i b P f E i- Joseph Weizenbaum, Professor Emeritus, Computer Science, MIT

Challenges – Security / C fid i liConfidentiality

Laptop theftsLaptop theftsPoor Data Stewardship“S i ”“Snooping”Clinicians “walking away”Wireless “sniffing”Compare with a physical chartCompare with a physical chart

Can go missing (but only one at a time)Not password protectedNot password protected

Challenges – Security / ConfidentialityConfidentiality

Paradox – increasing security g ymeasures for EMRs

But only with a computerized system are largeBut only with a computerized system are large-scale breaches of security possible

Challenges – Vocabulary

Different clinical terms meaning the same thingg gExample:

Heart AttackMyocardial InfarctionMIAcute Coronary SyndromeAcute Coronary Syndrome

Computers a quite literalTranslation tablesTranslation tables

Within enterpriseBetween enterprises

Challenges – Vocabulary

SNOMEDSNOMEDCPTNIC/NOC/NANDANIC/NOC/NANDAICD-9 CMMeSHImpacts on CDS – conditions / problems / labImpacts on CDS conditions / problems / lab results (ml vs. milliliter)

Challenges – Human Factors

Data entry issues / aging populationData entry issues / aging populationTyping dexterity / speedVisual issues / font size/

Screen layout – intuitiveness Compare your system with the iPod!Compare your system with the iPod!

How much training?!!!!8 hours8 hours3 hoursZero hours?Zero hours?

Informatics Applications

Clinical Decision SupportClinical Decision SupportHow do we define Clinical Decision Support?

D R b H d f h C f H l hDr. Robert Haywood of the Centre for Health Evidence:

"Clinical Decision Support systems link healthClinical Decision Support systems link health observations with health knowledge to influence health choices by clinicians for improved health care".

In a nutshell, it’s utilizing dynamic patient data and a set of rules in order to alert clinicians to situations h i h i i i fl d i ithat might require action or influence decisions

concerning patient care.

Informatics Applications

PACsPACs

Picture Archiving and Communication Systems

Digitalization of medical imagesDigitalization of medical images

Replaces hard-copy management

Informatics Applications: PACs

Computed TomographyComputed TomographyWith contrast

Lab Results Review

How a Physician Portal Stitches this ll t th rall together

The integrated EMR:The integrated EMR:OrdersResultsResultsMedical NarrativeNursing DocumentationgFlowsheetPACSDASMedical Records

DAS: Data Acquisition Systems

ECG Ventilator DataECG, Ventilator DataReal-time review via Portal from anywhere

RHIOs

Regional Health InformationRegional Health Information Organizations

Enable Integration and availability of data across organizationsof data across organizations

Seen as key to the US National yHealth Information Network

RHIOs

ProsProsPre-requisite: Uniform data format – great for interoperability care/disease managementinteroperability, care/disease management Cohesive, centralized, “whole” system, easier to access, maintain and control (e.g. role-based access) cc ss, d c ( g b s d cc ss)Research, population studies, public health research

RHIOs

ConsConsPolitical: medical data ownership and control in a central locationcentral location More complex implementations Cost sharing implications who’s going to pay?Cost sharing implications – who s going to pay?Scalability Privacy & Security issuesPrivacy & Security issues

Telehealth / Telemedicine

ExamplesExamplesDecentralized verification of

di i dmedication ordersHome CareHospice CarePatient EmpowermentPatient EmpowermentE-ICU

Informatics training sites

Pharmacy Informatics Training

MassachusetsMichiganNebraskaVirginiaMarylandUtahUtahAlabamaVendor Opportunities – McKessonppSee http://www.ashp.org/s_ashp/docs/files/R-InformaticsTrainingProgs.II.pdf

Groups to participate in –

ASHP Informatics Working GroupASHP Informatics Working Group

AMIA – American Medical Informatics Asssociation

HIMSS – Health InformationHIMSS Health Information Management Systems Society

“Paperless” Myth

Persistent Paper: The Myth of “Going Paperless”Persistent Paper: The Myth of Going PaperlessRichard H. Dykstra, M.D., M.S., Emily Campbell, R.N., M.S., Dean F. Sittig, Ph.D., Kenneth Guappone, M.D., James Carpenter, R Ph M S Joan S Ash Ph DR.Ph., M.S, Joan S. Ash, Ph.D.

“Paper has helped to shape work practices, and work practices have been designed around the use of paper.”

Harper R, Sellen A. Paper-Supported Collaborative Work. Xerox Technial Report EPC-1995-109 1996.

Heavy paper use even at the most computerized of settingsGoing paperless is a process – not an event


Paper is:pPortablePersistent during outtagesNot dependent on computer availabilityAnnotatableA i ti diA communications mediumCustomizablePermits improvisationPermits improvisationEasier to represent flowsheetsEasier to read (sometimes)( )


Risks of a “hybrid environment”yDuplicate simultaneous order creationPaper is immediately out of dateEncourages inertia

Some “wet signature” requirements ConsentsConsentsEmergency trip recordsCode Blue records.

Becoming a transitory element in healthcare workflowShould the goal be design of “better paper”??

a primer · large-scale clinical information s yy,stems, such as hospital information systems...

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