Electronic Health Record

(1)

Electronic

Health

Record

Introduction

to

Biomedical

Informatics

William

Hersh

Copyright,

2010

Oregon

Health

&

Science

University

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Brailer, D. (2005). Interoperability: the key to the future health care system. Health Affairs, 24: W5‐19‐ W5‐21. http://content.healthaffairs.org/cgi/reprint/hlthaff.w5.19v1.

Brown, S., Lincoln, M., et al. (2003). VistA‐U.S. Department of Veterans Affairs national‐scale HIS. International Journal of Medical Informatics, 69: 135‐156.

Carter, J., Backman, C., et al. (2009). The Legal Health Record in the Age of E‐Discovery. Chicago, IL, Healthcare Information Management Systems Society: in press.

Colias, M. (2005). Disease registries. Hospitals & Health Networks. February 15, 2005.

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Dick, R., Steen, E., et al., eds. (1997). The Computer‐Based Patient Record: An Essential Technology for Health Care, Revised Edition. Washington, DC. National Academies Press.

Dolan, P. (2008). Is your EMR legal? A document can look like a medical record, but not meet the legal definition. American Medical News. October 13, 2008. http://www.ama‐

assn.org/amednews/2008/10/13/bisa1013.htm.

Dorr, D., Wilcox, A., et al. (2006). Implementing a multidisease chronic care model in primary care using people and technology. Disease Management, 9: 1‐15.

Dougherty, M. (2008). How legal is your EHR? Journal of AHIMA, 79(2): 25‐30.

Fischetti, L., Mon, D., et al. (2007). Electronic Health Record‐System Functional Model, Release 1, February 2007, Reader’s Guide. Ann Arbor, MI, Health Level Seven.

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Frieden, T. and Mostashari, F. (2008). Health care as if health mattered. Journal of the American Medical Association, 299: 950‐952.

Friedman, C., Shagina, L., et al. (2004). Automated encoding of clinical documents based on natural language processing. Journal of the American Medical Informatics Association, 11: 392‐402. Greenfield, S. and Kaplan, S. (2004). Creating a culture of quality: the remarkable transformation of the

Department of Veterans Affairs Health Care System. Annals of Internal Medicine, 141: 316‐318. Handler, T., Holtmeier, R., et al. (2003). HIMSS Electronic Health Record Definitional Model. Healthcare

Information Management Systems Society.

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health records: a review of the research literature. International Journal of Medical Informatics, 77: 291‐304.

Hibble, A., Kanka, D., et al. (1998). Guidelines in general practice: the new Tower of Babel? British Medical Journal, 317: 862‐863.

Hoffman, S. and Podgurski, A. (2008). Finding a cure: the case for regulation and oversight of electronic health record systems. Harvard Journal of Law & Technology, 22(1).

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Hripcsak, G., Friedman, C., et al. (1995). Unlocking clinical data from narrative reports: a study of natural language processing. Annals of Internal Medicine, 122: 681‐688.

Kolata, G. (2005). U.S. Will Offer Doctors Free Electronic Records System. New York Times. July 21, 2005.

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Kuperman, G. and Gibson, R. (2003). Computer physician order entry: benefits, costs, and issues. Annals of Internal Medicine, 139: 31‐39.

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Mamlin, B., Biondich, P., et al. (2006). Cooking up an open source EMR for developing countries: OpenMRS ‐ a recipe for successful collaboration. Proceedings of the AMIA 2006 Annual Symposium, Washington, DC. American Medical Informatics Association. 529‐533.

Maviglia, S., Zielstorff, R., et al. (2003). Automating complex guidelines for chronic disease: lessons learned. Journal of the American Medical Informatics Association, 10: 154‐165.

Metzger, J. (2004). Using Computerized Registries in Chronic Disease Care. Oakland, CA, California Health Care Foundation. http://www.chcf.org/topics/chronicdisease/index.cfm?itemID=21718. Meystre, S. and Haug, P. (2006). Natural language processing to extract medical problems from

electronic clinical documents: performance evaluation. Journal of Biomedical Informatics, 39: 589‐599.

Meystre, S., Savova, G., et al. (2008). Extracting Information from Textual Documents in the Electronic Health Record: A Review of Recent Research, 128‐144, in Geissbuhler, A. and Kulikowski, C., eds. IMIA Yearbook of Medical Informatics 2008. Stuttgart, Germany. Schattauer.

Morrissey, J. (2006). A Day in the Life of a Medical Record. Chicago, IL, National Alliance for Health Information Technology. http://nahit.org/dl/A_Day_in_the_Life.pdf.

Rishel, W., Handler, T., et al. (2005). A Clear Definition of the Electronic Health Record. Stamford, CT, Gartner Corp. http://www.gartner.com/DisplayDocument?doc_cd=130927.

Safran, C., Bloomrosen, M., et al. (2007). Toward a national framework for the secondary use of health data: an American Medical Informatics Association white paper. Journal of the American Medical Informatics Association, 14: 1‐9.

Sager, N., Friedman, C., et al. (1987). Medical Language Processing: Computer Management of Narrative Data. Reading, MA. Addison‐Wesley.

Tierney, W., Rotich, J., et al. (2007). The AMPATH medical record system: creating, implementing, and sustaining an electronic medical record system to support HIV/AIDS care in western Kenya. Studies in Health Technology and Informatics: 372‐376.

Weed, L. (1969). Medical Records, Medical Education, and Patient Care. Chicago. Year Book.

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Electronic

Health

Record

Introduction to Biomedical Informatics

William Hersh

Copyright, 2010

Oregon Health & Science University

1

History

and

perspective

of

the

medical

record

• Data can be organized as

–Practitioner (physician)‐centered

–Patient‐centered

• Orientations (not( mutuallyy exclusive)) include

–Time‐oriented – organized chronologically

–Department‐oriented – organized by department

–Problem‐oriented – organized by focus on problems

2

History

and

perspective

(cont.)

• Earliest medical records were physician‐oriented

• Hippocrates said over 2,500 years ago that the

medical record should

–Accurately reflect course of disease

–Indicate possible causes of disease

• Before era of widespread medical diagnostic testing,

record consisted mostly of observations

Some

early

innovations

came

from

Mayo

Clinic

• All physicians kept all their notes in leather‐

bound ledger

• In 1907, moved from physician‐centered to

patient‐centered record p

• Mayo was also pioneer in defining data

elements to be recorded, forerunner of

modern efforts in epidemiology and

classification

4

Some

terminology

of

the

medical

record

• Electronic health record (EHR) – subsumes

–Electronic medical record (EMR)

–Computer‐based patient record (CPR)

• Some other terms of note

–Practice management system (PMS)

–Patient registry

–Personal health record (PHR)

–Problem‐oriented medical record (POMR)

–Legacy systems

5

The

problem

‐

oriented

medical

record

• Proposed by Weed (1969)

• All entries grouped under particular problems

• An encounter for each problem is organized under

four headings

–SSubjective – what patient reportsubjective what patient reports

–Objective – what clinician observes or measures

–Assessment – what clinicians assesses

–Plan – what clinician plans to do

• Most common usage is to have entire encounter

(5)

The

modern

‐

day

medical

record

• Mixture of patient‐and problem‐oriented

approaches

• In general, each provider or institution maintains its

own record

• The creator of the medical record is assumed to beThe creator of the medical record is assumed to be

its “owner,” although patients can request access

• In the United States, is still predominantly paper‐

based

–Or even worse, it is “hybrid,” with some data on paper, some electronic, and some on both media

7

Flow

of

information

in

primary

care

practice

(Bates,

2002)

8

Additional

challenges

in

the

modern

era

• Coordinating care requires better communication among providers (Dorr, 2006)

• Increasing cost of care requires justification and documentation of expenditures

• Patients change plans, so their records should be portable

–US spends several $B per year copying medical records • Informed consumers desire more participation in care

decisions, which includes access to their records

–They also want security and other protections of their information • No single vendor has complete solution

–Many take “best of breed” approach to matching components

9

Some

limitations

of

the

paper

‐

based

record

• Summarized by Dick (1997), recently exemplified by

–“A Day in the Life…” (Morrissey, 2006)

–“In the Dark…” (Litvin, 2007)

• Singleg user – one personp at a time

• Disorganized – especially for complex patients

• Incomplete – reports missing or lost, some providers

not sharing their reports with the rest

• Insecure – no audit trail, easily copied or stolen

10

And

then

there

is

handwriting…

• Can you decipher these orders?

11

Some

limitations

(cont.)

• Illegibility

–Coumadin vs. Avandia (courtesy of David Bates,

MD)

–25 U/hr vs. 25 cc/hr vs. ??? (courtesy of Rainu

Kaushal, MD, MPH)

(6)

But there are still many

conveniences with paper

and/or writing…

13

Definitions

and

attributes

of

the

EHR

–

some

key

documents

• IOM, Key Capabilities of an Electronic Health Record

System (2003)

–Consensus based on IOM reports

• HIMSS, Electronic Health Record Definitional Model

(Handler, 2003)

( , )

• HL7, System Functional Model (HL7, 2004; Fischetti,

2007)

–Attempt to define features and functions in an EHR system

• Gartner “Clear Definition” (Rishel, 2005)

• Research review of definitions, content, and use

(Häyrinen, 2007)

14

IOM

key

capabilities

of

EHR

• Health information and data

• Result management

• Order management

• Decision support

• Electronic communication and connectivity

• Patient support

• Administrative processes

• Reporting and population health management

All

functions

should

address

five

health

care

quality

criteria

• Improve patient safety

• Support delivery of effective patient care

• Facilitate management of chronic conditions

• Improve efficiency

• Have feasibility of implementation

16

(Some)

key

features

of

EHR

(Frieden,

2008)

• Standardized essential data elements

• Incorporate data from outside systems

• Facilitate medication reconciliation

• Registry reporting

• Clinical decision support at point of care

• Automated quality measurement

• Support for patient self‐management

• Interface with public health services

• Systematic outcomes evaluation

• Linking to community resources

17

Other

definitions

and

attributes

for

the

EHR

• Data flow

• Practice management systems

• Registries

(7)

EHR

data

flow

(typically

in

hospitals

or

large

clinics)

Departmental

system Data

Warehouse Additional financial and

administrative data 19 Data Repository Departmental system Departmental system Regional and national systems Repository is logical

place for EHR data and its access

Practice

management

systems

• Handle non‐clinical functions of medical practice,

e.g., –Scheduling –Billing Eligibility verification –Eligibility verification • Selection: ACP, 2007

• Often a key issue: Should a practice use same vendor

for EHR and PMS?

–Pro: Integration, possible cheaper cost

–Con: One or other might not have all desired functionality

20

Registries

• More limited form of EHR

–Can be separate from EHR or extract of data from it

• Typically oriented to one or small number of

diseases, most often chronic diseases l f

• Usual functions

–Patient reports – status of monitored conditions

–Exception reports – outliers, overdue for care

–Aggregate reports – how is care team delivering recommended care

• Overviews: Metzger, 2004; Colias, 2005

21

The

legal

EHR

• Key attributes of legal medical record are

discoverability and disclosure (Amatayakul, 2001)

• Legal EHR defined in several publications (HIMSS,

2006; Dougherty, 2008) 2006; Dougherty, 2008)

• Major concerns for purchaser of legal EHR are

how system deals with authorship, changeability,

and audit for accuracy and validity (Dolan, 2008)

• Growing calls for regulation of EHRs (Hoffman,

2008) and “e‐discovery” (Carter, 2009)

22

Benefits

and

challenges

of

EHR

• Benefits

–Improved physician, nursing,

and other care

–Clinical decision support

–Personal health records

• Challenges

– Data quality

– Data usability

– Implementation

– Standards and

–Health information exchange

–Public health informatics

–Quality assessment

–Clinical research

interoperability

– Privacy, confidentiality, and

security

(8)

Potential

benefits

of

the

EHR

• Multi‐user ubiquitous access to patient data

• Multiple views of data

• Better communication with other providers

and with patients and with patients

• Gamut of secondary uses of clinical data

25

Multi

‐

user

ubiquitous

access

to

patient

data

• Paper record limited to single user at single

location

• Electronic record can be accessed by more

than one user at a time than one user at a time

• Electronic record can be accessed from any

location connected to the computer system or

network

26

Multiple

views

of

data

• Different users prefer different “views” of the

data

• Views include

–ListsLists

–Flowsheets

–Graphs

• Computers are also good at showing trends of

data

EHR

can

enhance

communication

for

other

providers

and

patients

• Medical record often serves as a means for providers to communicate

–Not only among physicians

but also nurses, pharmacists,

and other ancillary staff

• A growing number of EHRs are linked to personal health records (PHRs; AMIA‐ AHIMA, 2006), which allow patients to

Vi th i d

and other ancillary staff • Next step is health

information exchange (HIE), where “data follows the patient” in care process

– View their records

– Communicate with their

providers (Slack, 2004)

– Access their health system,

e.g., fill prescriptions,

schedule appointments, etc.

28

Secondary

uses

of

clinical

data

(Safran,

2007)

• Re‐use of data from EHR for other purposes

• Focus of Unit 6, although each of these areas is

about more than use of clinical data

• These areas include

–Personal health records – patients managing their healthPersonal health records patients managing their health and health care

–Public health – reporting, biosurveillance

–Quality measurement and analysis

–Health information exchange – “data following the patient”

–Clinical research – advancing research through data in systems

29

Clinical

decision

support

• The raison d’être of the EHR?

• Decision support uses EHR data to provide

context‐specific advice, alerts, and reminders,

such as

–Assisting with choices in diagnosis and therapy

–Detecting problematic situations, such as medication

errors or drug‐drug interactions

• Is best delivered at point of care, especially when

decisions are being made

–Computerized physician/provider order entry (CPOE)

(9)

Types

of

decision

support

• Information display – showing general or patient‐

specific information in context of situation

• Reminder systems – reminding clinicians to perform

actions, such as preventive measures

Al l i i i l li i l i i

• Alerts – alerting to critical clinical situations, e.g.,

interacting drugs, abnormal lab value

–Growing concern over “alert fatigue”

• Clinical practice guidelines – guiding treatment to

provide normalized care based on best evidence

31

Guidelines

exemplify

paper

vs.

electronic

conundrum

• Paper guidelines are easy to

use if appropriate one(s) can

be found (Hibble, 1998)

• But probably more likely to

be followed if part of

decision logic of EHR

• Although codifying

guidelines and underlying

evidence is challenging

(Maviglia, 2003)

32

But

there

are

challenges…

• Data quality – documentation not always a

priority for clinicians (RTI, 2007)

• Data usability

–Much data “locked” in clinical narrative (Hripcsak,

1995)

–Much research over several decades attempting

natural language understanding of clinical narrative

(Sager, 1987; Friedman, 2004; Meystre, 2006)

–Performance of systems still not adequate for routine

operational use (Meystre, 2008)

33

Challenges

(cont.)

• Implementation – challenges in making

systems useful and usable for patients,

clinicians, and others (Bates, 2003)

• Standards and interoperabilityp y – how can data

seamlessly flow and systems co‐exist (Brailer,

2006)?

• Privacy, security, and confidentiality – can we

achieve the benefits while maintaining

privacy? (Boyd, 2007)

34

EHR

examples

• Ambulatory care

–Centricity EMR (formerly Logician)

• Hospital‐based decision support

–Brighamg Integratedg Computingp g y System (BICS)( )

• Both and available as a demo: Veterans Health

Information Systems and Technology Architecture

(VISTA)

–http://www1.va.gov/cprsdemo/

• Other open‐source and additional examples

35

Example

1:

Ambulatory

care

• Logician (now Centricity EMR, GE Health, Hillsboro,

OR) is a comprehensive ambulatory EHR featuring

–Integrated view of patient data

–Clinical decision support

–Clinician order entry

–Access to knowledge resources

–Integrated communication support

(10)

37

Integrated view of patient data

38

Data can be viewed in different formats

Different formats (cont.)

40

Information can be browsed effectively

Clinical decision support can be applied

41

Queries can be made over entire practices over entire practices

(11)

Clinician order entry

43

44

Access to knowledge resources

45

Integrated communication support

Example

2:

Hospital

‐

based

decision

support

• Brigham Integrated Computing System (BICS)

–Courtesy of Jonathan Teich, MD, PhD

• Features

–Information displayp y

–Reminder systems

–Appropriate ordering

–Medication safety

• They are transitioning from text‐based (DOS) screens

46

Lab order – costs display

47

Reminder for ordering gentamicin levels

(12)

Reminder about drug substitutions

49

Appropriate ordering of radiologic studies

50

Possible drug allergies

Chemotherapy safety

52

Digoxin administration in the face of low serum potassium level

53

Templates and order sets are an example of guideline usage

(13)

Example

3:

Both

and

an

available

demo

• Veterans Health Information Systems and Technology

Architecture (VISTA)

–Available as a demo over Internet: http://www1.va.gov/cprsdemo/

• Modern EHR success story (Brown 2003) and health

• Modern EHR success story (Brown, 2003) and health

care quality success story (Greenfield, 2004)

–System being “generalized” and made available as open source software (Kolata, 2005)

–One company that has developed business around VISTA is MedSphere (www.medsphere.com) 55 Selecting 56 g a patient 57 Patient summary 58

Problem list with provider alerts 59 Medication order entry 60 Clinical note from Cardiology

(14)

Vital signs and their trends 61 62 Imaging results: EEG

Clinical reminder for elevated blood pressure

64

Template letter for results reporting

Selecting images to view

65

Other

open

‐

source

systems

• OpenEHR (www.openehr.org) – global effort to build

open‐source reference implementation

–Based on archetypes, which are formal clinical content specifications and are used and combined by templates (Beale, 2007)

( , )

• OpenMRS (www.openmrs.org) – focused on low‐

cost, simple record for developing countries (Mamlin,

2006; OpenMRS, 2007)

–Major focus on countries with large HIV/AIDS burden, currently deployed extensively in Kenya (Tierney, 2007)

Electronic Health Record

Electronic

Electronic

History

early

terminology

problem

modern

information

limitations

there

documents

capabilities

health

Other

Practice

Registries

legal

Related

Benefits

Potential

Multiple

Secondary

Clinical

Types

Guidelines

challenges…

Challenges

examples

Example

Example

Example

Other