March 14, 2006: I. Sim Dollars, People, Health IT Epi – 206 Medical Informatics PAMF EHR...

March 14, 2006: I. Sim Dollars, People, Health ITEpi – 206 Medical Informatics

PAMF EHR

• Discussion on Paul Tang’s lecture


Outline

• “Critical Crossroads”

• Biomedical informatics base for clinical research– health research via the web

– management and analysis

– application to clinical care

• Summary


eHealth Research• As more patients do more care on the web,

greater opportunities to use web to– recruit patients– survey patients– deliver interventions

• What are theoretical and methodological problems?


Digital DivideInternet Access Broadband Access

<$30,000 41% 8%

$30-49,000 71% 16%

>$50,000 89% 39%

No children 59% 16%

Children in home 76% 29%

White 69% 23%

African-American 56% 15%

Hispanic 48% 14%

"Digital Divide" Still Shapes Media Landscape (10/19/04, Knowledge Networks/SRI); http://www.knowledgenetworks.com/info/press/releases/2004/101904_htmtrends.htm

http://www.knowledgenetworks.com/info/press/releases/2004/101904_htmtrends.htm


Digital Health Divide• Spanish-language sites have lower quality

– 45% of English-language sites vs. 22% with minimal coverage & complete accuracy (JAMA 2001;

285:2612-2621)

• Broadband more available to higher-income white households with children– uneven potential access to tele-consultation


Pitfalls• Scenario A

– You are planning a study of participants in an email list. You plan to send individual invitations to the list of current subscribers -- you know the company that hosts the group keeps such a list.

– But, the list hasn't been "cleaned" for years. You do your mailing and receive 20,000 "bounced" emails that jam your server, set off alarms that you are a spammer, get you temporarily ousted from several service providers till you can prove otherwise, and leave you having to sort positive and negative responses, as well as questions from bounces for a variety of reasons.

– You had no way of knowing this could happen, so you didn't budget for it. Not for the delay, not for the effort to sort through the mail, not for the senior-level time to assure the service providers that you aren't a spammer, etc. etc.

(From http://virgo.cit.nih.gov:4080/roller/page/bglassmanblog)


Pitfalls

• Scenario B– You send a questionnaire as an email attachment in MS Word. To

keep track of the attachments, you build in a little field that increments a number by 1 for each attachment. Your properly consented participants get the attachment, open it in Word, and get a message warning them that it may contain a dangerous macro that can act like a virus. Not only do they not fill it out, they go online to warn others.

– No one told you that you can inadvertently leave macros in attachments that will set off alarms on the recipients' computers.


A New Arena for Research • Issues include

– appropriate experimental and non-experimental designs– the reliability and validity of scales and instruments – how to calculate an online sample size– how to deal with confidentiality– what constitutes informed consent online, and standards for soliciting

informed consent in online research– how to verify authenticity– how to improve IRB understanding of ehealth research– standards for conducting and reporting online research

• First ever NIH Critical Issues in eHealth Research conference, June 2005 http://www.scgcorp.com/ehealthconf2005/index.asp

• UCSF Internet Health Center– Ricardo Munoz, [email protected]

http://www.scgcorp.com/ehealthconf2005/index.asp


Outline


• Biomedical informatics base for clinical research– design and execution



• Summary


Case Study: RCTs• How many Phase III RCTs does NIH fund?• What percentage of depression RCTs are placebo-

controlled?• What percentage of patients in CHF trials have

renal failure?• Which acute MI trials report 5 year mortality?• Is industry funding correlated with positive

outcomes?• What percentage of trials report outcome results

selectively (ie positive outcomes only)?


NIH Data Reporting Requirement

• As of May, 2003– all grants over $500,000 direct required to

“report all data publicly”– variation among institutes on how to implement

this• no standard format

• What use is “the data” if the study design is poorly reported, not computable, or not available?


Management/Analysis of Completed Studies

• Systematic under-investment in informatics for clinical research results management and analysis– need common data structures for various study types

(e.g., RCTs, systematic reviews, guidelines)– need standardized research variables and coding of

variables (e.g., NCI Common Data Elements/Thesaurus)

• Contrast to rise of computational biology/ bioinformatics– DNA sequences: GenBank; metabolic pathways:

BioCyc– pharmacogenetics, pharmacogenomics: PharmGKB– protein sequence: UniProt; protein structure: PDB, etc.


Heads-Up

• In response to mis-representations of trial results (e.g., CLASS, Vioxx studies)

• Journals seriously considering requiring submission of– study protocol– study data, for re-analysis by journal and

journal’s outside statisticians• use Global Trial Bank?


Outline


• Biomedical informatics base for clinical research– design and execution



• Summary


2nd Translation, to Clinical Care

• 2 approaches using information technology– “firehose” approach

• if 1 article/guideline is good, 1000 is better…

– decision support to present evidence-based action options


IT for Translation• “Non-thinking” information technology approaches

– evidence-based medicine resources• ACP Journal Club, Clinical Evidence, PubMed, Up-to-Date• Cochrane systematic reviews

– guidelines (1807 guideline summaries)• www.guidelines.gov

– quality measures (301 measures)• www.qualitymeasures.ahrq.gov

• Clinical decision support systems– … in which the characteristics of an individual patient

are matched to a computerized clinical knowledge base for patient-specific recommendations

– need both coded EHR and coded biomedical knowledge

http://www.guidelines.gov/

http://www.qualitymeasures.ahrq.gov/


Computable Biomedical Knowledge Bases

• Pharmacogenetics/genomics: PharmGKB• SNPs: SNP Consortium• Randomized trials

– Trial Bank Project (http://rctbank.ucsf.edu/Presenter/)• computable database of RCT design, execution, results

• Physiological models– Archimedes: diabetes

• No computable repositories of – clinical guidelines– systematic reviews, decision/cost-effectiveness models– “textbook” information

http://rctbank.ucsf.edu/Presenter/

http://rctbank.ucsf.edu/Presenter/


Shared Knowledge Bases (KBs)• Should share the primary knowledge (e.g., RCT results)!

– open access knowledge repository– each system can build its own rule set based on shared

evidence– KB updating and maintenance also shared

• E.g., Global Trial Bank partnership w/ Public Library of Science– computable RCT protocols and results tied in to trial registration

(e.g., clinicaltrials.gov)– peer reviewed by PLoS Clinical Trials– open access knowledge base and journal, with an open peer

review/discussion forum– decision support systems/EHRs can directly query GTB


Summary: Application to Care• Currently at first generation

– static web/e-text information resources• humans have to retrieve and read articles

– individual decision support systems have expensive, hard-to-maintain, hand-coded knowledge bases

• To move to next generation, need– widely deployed EHRs, reasonably coded in SNOMED– shared computable biomedical knowledge bases– a better theory of when and how to translate evidence

to care


Dollars, People, and Health IT

Ida Sim, MD, PhD

March 14, 2006

Division of General Internal Medicine, and Graduate Group in Biological and Medical Informatics

UCSF

Copyright Ida Sim, 2006. All federal and state rights reserved for all original material presented in this course through any medium, including lecture or print.


Outline

• Socio-economic-technical health IT– connectivity– the path to EHRs– falling off the path

• Class summary


Administrative Clinical Care Research

ClinicalBilling

Physical Networking

Standard Communications Protocols (e.g., HL-7)

Standard Vocabulary

PracticeManagement

Systems

EHRExecutionAnalysis

Medical BusinessData Model

Clinical CareData Model

Clinical StudyData Models

Application to Care

From Here to There

• Health care is fragmented, research infrastructure even more so


Case of Market Failure?• The more interoperable/integrated health IT is, the more useful

it is– connectivity (physical networks, governance)

– standardization (vocabulary, transmission protocols)

• A case of “network externalities” (or network effects) – a change in the benefit or loss that an agent derives from a good

when the number of other agents consuming the same kind of good changes. e.g., fax

– an “externality” because the agent (e.g., a practice) does not fully capture the benefits to others (e.g., to others for having EHR)

• Both market and government solutions imperfect– some argue that EHRs are a case of “market failure”


Towards Interoperability• Interoperability is big goal of ONCHIT (the fed’s health IT czar)

– 2006 Fed. budget was $125 million for demo projects

• Will require ~$300 billion– who should pay? physician practices?– who will make decisions on the technology?

• Massachusetts e-Health Collaborative– Mass. Blue Cross/Blue Shield pledging $50 m to connect Mass.

• to determine costs and benefits of sharing healthcare data electronically

• to create a business model to expand the effort across the state– connect hospitals, labs, physicians; then deploy decision support– saving 3% of $50 b annual Ma. health care costs will yield $1.5

billion for connecting the state


Outline


• Class summary


Costs and Benefits of EHRs• “...Without a national pledge to create and fund [a health

informatics] framework, progress to enhance quality of care will be painfully slow.” (IOM Report, 3/01)

• Low penetration of EHRs – outpatient (MGMA, Oct. 2001)

• 1% (ACGroup) to 7% have one• 14% in implementation process• 68% have considered getting an EHR

– inpatient (HIMMS Leadership Survey, 2002)

• 13% fully operational• 32% in implementation process• 23% planning to implement

– By mid-2005, only 20-25% of groups of >50 MDs


Costs• Initial costs $20-27,000 per MD for full-function

EHRs– can be as low as $99.95/month with Application

Service Provider (ASP) versions• subscribe to web service that stores your EHR (e.g., GE

Centricity)

– lower end EHRs with little functionality ~$300/MD

• Ongoing costs of $7-9,000 annually per MD• > 1/2 of costs are for hardware and software• Other half

– for “complementary innovations”(R Miller, I Sim, Health Aff 2004; 23(12):116-126)


Complementary Innovations• Everything you need to do to make the purchased “out

of the box” EHR work in your organization• Customization of

– installation: interfaces to existing (legacy) systems– user interfaces– user templates (e.g., for URI, DM)

• Workflow redesign• New quality improvement programs

– e.g., clinical pathways

• Organizational change– appoint, train, and pay physician EHR leaders/champions


Benefits• Tangible (range $0 to $14,000 per MD)

– reduction in dictation costs– reduction in medical records staff (for chart pulls, etc)– reduction in duplicate lab tests

• Intangible (in current reimbursement climate)– quality of care– improvement in care coordination– service improvement– customer satisfaction– estimate that IT would yield $140-400 b savings on

$2.0 trillion health care costs (2004)


Cost/Benefit Equation• Costs are substantial, benefits vary widely• Extent of benefits dependent on many factors, but

especially on the nature and extent of complement-ary innovations

• But complementary innovations – are costly

• often require new or extra staffing

– are difficult to implement• involve organizational change and changing physician behavior

– challenge the intellectual capital of the practice• managerial, financial, organizational change, quality

improvement


EHR Cost/Benefit Summary

• EHRs are not a “sure-fire” investment• EHR is an enabling technology

– enables more effective quality improvement programs

• To maximize quality benefits from an EHR, must invest in expensive and challenging complementary innovations

• New business models may change cost/benefit equation for smaller physician groups– hope that ASP and/or open source will increase EHR

adoption isn’t panning out


Socio-economic-tech Summary

• Structural barriers to more health IT– fragmented cottage industry– nature of technology (network effects)

• Requires huge sums of money

• Requires both technical and managerial sophistication


Outline


• Class summary


Failures of Health IT

• IT projects in general– 34% successful; 51% “challenged”; 15%

absolute failures (Standish Group, 2004)

– successful rate was 16% in 1994

• Rate of IT failures in health care?– Cedars-Sinai spent $30m building their own

computer-based physician order entry (CPOE) system (early 2004)

– physician insurrection after 1 month, mothballed


Failures of Research IT?


Outline


• Class summary


Class Teaching Points• The more “computable” the information, the

more the computer can do for us• Standardization of terms absolutely critical but

not a solved problem– SNOMED most comprehensive but use is unproven

• For clinical research– coding and standardizing research variables critical– EHR and data warehouses can but don’t always help

research– much can be done today but overall infrastructure is

at “critical crossroads”


Class Teaching Points (cont.)

• To get greater EHR and health IT diffusion– “people and organizational issues” are as important

as the technical ones

• E-Health is huge and transformative– offers a new arena for research, with new pitfalls

and challenges


Take-Home Message• Need a shared infrastructure for information

systems that support both clinical research and care

• “Smarter” computers for improving quality of care, etc. bump up against the vocabulary and coding problem– fundamental informatics problems won’t be solved in the

near term

• Be excited but tempered about what computers can do for medical care and research

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March 14, 2006: I. Sim Dollars, People, Health IT Epi – 206 Medical Informatics PAMF EHR...

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