Automating the Contextualization of Population-Based CDSS … · Automating the Contextualization of Population-Based CDSS in Tethered EHR/PHRs ... Automating the Contextualization

Hadi Kharrazi, MHI, MD, PhD

Johns Hopkins [email protected]

Automating the Contextualization of Population-Based CDSS in Tethered EHR/PHRs

DHSI/CPHIT Grand RoundOct.19.2012

© Hadi Kharrazi @ JHSPH-HPM

Automating Population-Based CDSS …

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Hadi Kharrazi reports having a financial relationship with Indiana University from Aug 2008 to Aug 2012.

Hadi Kharrazi reports having a financial relationship with Johns Hopkins University since Aug 2012.

Hadi Kharrazi reports that he has no financial relationship with any commercial interest.

Disclosures

Speaker: Hadi Kharrazi, MHI, MD, PhD

Health Sciences Informatics Grand Rounds Oct 19.2012



3

Describe the types of HIT solutions (e.g., EHR, PHR) and how they complement Population Health IT (PopHI)

Explain the relationship among CPGs, CDS languages, and CDS systems

Demonstrate how CDSS can be automated and applied in the context of PopHI

Explain how population-based CDSS can bridge the gap between clinical informatics and PubHI

Discuss the opportunities and challenges of population-based CDSS

Lecture Objectives

Title: Automating the Contextualization of Population-Based CDSS in Tethered EHR/PHRs

Speaker: Hadi Kharrazi, MHI, MD, PhD

Health Sciences Informatics Grand Rounds Oct 19.2012



4

Introduction

Backgroundo Clinical Informatics (CI)o Public Health Informatics (PubHI)o Health Information Exchange (HIE) role in PubHIo Population Health Informatics (PopHI) - bridging the gap between CI and PubHIo Clinical Decision Support (CDS) continuum and PopHI

Regenstrief Institute / INPC / Indiana HIE

Previous Worko Developing renewable CDS systemso Application and results of CDS automation in INPC RMRSo Contextualizing CDSS in CHI Platforms (PHR)o Population-based CDSS

Center for Population Health IT (CPHIT) / Future Work

Q & A

The presentation in a nutshell



5

Introduction



6

• Hadi Kharrazi, MHI, MD, PhD

“You are a doctor who is lost in cyberspace!” (my wife)

Current affiliation: JHSPH-HPM (Aug 2012)Associate Director CPHITDirector: Dr. J. Weiner

Previous affiliation: Indiana UniversityDalhousie University

Research interests: Population Health IT CDSS/QM in HIE environments

Personal Health Records (PHR)

Introduction



7

Molecular Research Health Research

Introduction

Biomedical informatics as a basic science

Basic Research

Applied Research

Biomedical informatics methods, techniques, and theories

Bioinformatics

ImagingInformatics

ClinicalInformatics

Public HealthInformatics

Consumer HealthInformatics

PopHI



8

Background



9

Background > Clinical Informatics (CI)

Administrative

Accounting

Clinical Inpatient

Clinical Outpatient

Imaging

Laboratory

Clin

ical

Inf

orm

atio

n Sy

stem

s

EHREMRJHH EPR

Clinical Informatics

CDSS

If patient has x, y then do z

QM, KD…

Non-clinical



10

EHR

2

Background > Public Health Informatics (PubHI)

Public Health InformaticsClaims

Non-clinical

Registries

Commun. Diseases

SyndromicSurveillance

Quality Measures

EHR

1

Reports

?

?

Public Health

Periodic public reports and alerts



11

EHR

n

EHR

…

Background > Health Information Exchange (HIE)

EHR

1

Other sources

HIE

Public Health Informatics

Registries

Commun. Diseases

SyndromicSurveillance

Quality Measures

Public Health



12

Population Health Info.

Background > Population Health Informatics (PopHI)

Population Health Data-warehouse

HIE Public Health Informatics

A

B

C

Publ

ic H

ealth

Clin

ical

Inf

orm

atic

s

Personal Health

Records

Population Health Analytics

Insurance Data

Admin data repositoriesRx Data

CI

to P

ubH

I

CI

to P

ubH

I

PubH

Ito

CI

PubH

Ito

CI

EHRs

1…n



13

Background > Clinical Decision Support (CDS) Continuum

Publ

ic H

ealth

Inf

o

Clin

ical

Inf

orm

atic

s

Bio

info

rmat

ics

Org

aniz

atio

nal I

nfo.

Population Health Informatics

If patient has x, y and lives in Baltimore

then do z’

If patient has x, y and is in high risk

population then do z’

If patient has x, y and is Hispanic then do z’

If patient has x, y with insurance q then do z’

If patient has x, y, and gene w

then do z’

If patient has x, y and you don’t have an MRI

then do z’

If patient has x, y then do z

If patient has x, y, has gene w, lives in Baltimore, is high risk, is Hispanic, has insurance q, and more population health rules…, and you don’t have an MRI then do z”



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Regenstrief Institute / INPC / Indiana HIE



15

• Established in 1969 by philanthropist Sam Regenstrief

• Regenstrief receives $3 million per year in core support from the Regenstrief Foundation

• Annual operating budget of approximately $23 million derived from grants and contracts

• Logical Observation Identifiers Names and Codes (LOINC) system, a standard nomenclature that enables the electronic transmission of clinical data from laboratories

• Regenstrief Medical Records System (RMRS) was developed 35 yrs ago. RMRS has a database of 6 million patients, with 900 million on-line coded results, 20 million full reports including diagnostic studies, procedure results, operative notes and discharge summaries, and 65 million radiology images.

• Indiana Network for Patient Care (INPC) was created in 1996. It is a city-wide clinical informatics network of 11 different hospital facilities and more than 100 geographically distributed clinics and day surgery facilities

Copyright Regenstrief Institute

Regenstrief Institute > INPC



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The Indiana HIE (IHIE) includes (as of mid-2011):

• Federated Consistent Databases• 22 hospital systems ~70 hospitals• 5 large medical groups and clinics & 5 payors• Several free-standing labs and imaging centers• State and local public health agencies• 10.75 million unique patients• 20 million registration events • 3 billion coded results • 38 million dictated reports • 9 million radiology reports • 12 million drug orders• 577,000 EKG tracings• 120 million radiology images


Regenstrief Institute > Indiana HIE (IHIE)



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Regenstrief Institute > PubHI > ELR Data Completeness

Overhage JM, Grannis S, McDonald CJ. Am J Public Health. 2008 Feb



18


Regenstrief Institute > PubHI > Timeliness of Data (CC vs ICD9)



19


Regenstrief Institute > PubHI > Notifiable Condition Detector (NCD)



20


Regenstrief Institute > PubHI > Syndromic Surveillance Service (SSS)



21

Previous Work



22

AHRQ’s NGC (guideline.gov)

*Blood Pressure, Weight/Body Mass Index (BMI) - Every visit. For adults: Blood pressure target goal <130/80 mm Hg; BMI (body mass index) <25 kg/m2.

For children: Blood pressure target goal <90th percentile adjusted for age, height, and gender; BMI-for-age <85th percentile.

Foot Exam (for adults) - Thorough visual inspection every diabetes care visit; pedal pulses, neurological exam yearly.

Dilated Eye Exam (by trained expert) - Type 1: Five years post diagnosis, then every year.

Depression - Probe for emotional/physical factors linked to depression yearly; treat aggressively with counseling, medication, and/or referral.

Arden Syntax:

logic:if (creatinine is not number) or (calcium is

not number) or(phosphate is not number) then

conclude false;endif;product := calcium * phosphate;

-----------------------------------------------------CARE Language:

BEGIN BLOCK CHOLESTEROL

DEFINE "MALE_PT" {VALUE} AS "SEX" IS = 'M'DEFINE "CHOLESTEROL LAST" AS LAST "CHOL TESTS" [BEFORE "DATE"] EXIST IF "CHOLESTEROL LAST" WAS AFTER 5 YEARS AGOTHEN EXIT CHOLESTEROLELSE CONTINUE

Computerized CPGs (Know.Rep.Lang.)

mysql_connect(‘localhost’, ‘root’, ‘pass’, ‘ehr');

mysql_query(‘SELECT patient_id FROM patient WHERE patient.Fname = ‘peter’ AND patient.Lname = ‘Johnson’’);

$results = mysql_fetch();

$patient_id = $results[‘patient_id’];

mysql_query (‘SELECT * FROM lab WHERE patient_id = $patient_id’ and lab_term = ‘HBA1c’);

$results = mysql_fetch();

$lab_result = $results[‘lab_term’];

IF ($lab_result > 9){ECHO “Patient has a high HBA1c level”;

}

Computerized DSS

Patient has diabetes and has not had an eye exam in two years. Based on guideline xxx you may want to consider asking for an eye consultation.

Popup alert

Previous Work > Developing Renewable CDSS

Clinical Practice Guidelines

Computerized CPG(CARE, Arden syntax)

Manual Programming for each CPG

Relational DB

One patient at the time



23

Issues with current CDSS

Often successful CDS RCTs are not replicable (renewable) in other clinical settings.

CDS knowledge interchange standards have existed for as long as clinical data interchange standards (about 2 decades), yet sharing of computable, actionable knowledge in practice is extremely limited

Current computerized knowledge languages representing CPGs are not standardized: Arden syntax, CARE, GELLO, GLIF

The majority of the current CDSS systems are hard coded, as experienced in a simpler format in CHI studies, which make them very limited due to maintenance issues.




24


Clinical Practice Guidelines

Computerized CPG(CARE, Arden syntax)

Manual Programming for each CPG

Relational DB 1

One patient at the time All patient at the time

Automated for all CPGs

CDSS SQL 1

CDSSSQL 2

CDSSSQL…

CDSSSQL n

Relational DB 2

Relational DB…

Relational DB n

NQF QM eMeasures




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Phases to Develop Renewable CDSS

1. Lexical Integration (mapping of idiosyncratic codes to standard terms)

2. Syntactic Mapping of Different Concrete Representation Languages

Parse original content as XML (e.g., Chaperon)

XSLT Transformation (e.g., SAXON)

3. Semantic Integration of Procedural Knowledge into Declarative Rules (SQL commands)

4. Pragmatic Integration (query enhancements, implementation and evaluation)




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Generating Renewable CDSS

CPG NQF

CAREArden

XMLCDSSSQL 1

CPG

Con

sort

ium

Syntactic Mapping

Semantic Integration

Relational DB 1

CDSSSQL 2

CDSSSQL…

CDSSSQL n

Relational DB 2

Relational DB…

Relational DB n

Rene

wab

le

Lexical Integration

XSLT trans.

Pragmatic Integration



27


Language Specific Language Free



28


Sample CARE Rule Block

BEGIN BLOCK CHOLESTEROL

DEFINE "MALE_PT" {VALUE} AS "SEX" IS = 'M'

DEFINE "CHOLESTEROL LAST" AS LAST "CHOL TESTS" [BEFORE "DATE"] EXIST

IF "CHOLESTEROL LAST" WAS AFTER 5 YEARS AGOTHEN EXIT CHOLESTEROLELSE CONTINUE

IF "MALE_PT" EXISTS AND "AGE" WAS >= 35THEN REMIND Male patients over the age of 35 should have their total cholesterol measured every 5 years.AND ORDER CHOLESTEROL (SCREEN)AND SAVE "UNO" AS "CHOL_SCN" {INTEGER}AND EXIT CHOLESTEROL

END BLOCK CHOLESTEROL



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Intermediate XML

<?xml version="1.0" encoding="UTF-8"?><rule xmlns="http://regenstrief.org/xcare"> <let name="AGE"> <and> <filter>

<reference name="BIRTH"/> <and> <not-equal op="NE" component="VALUE">

<literal type="integer" value="0"/> </not-equal> <less-than op="LT" component="VALUE">

<literal type="time" value="now"/> </less-than> </and>

</filter> <division>

<subtraction> <literal type="time" value="now"/> <reference name="BIRTH"/>

</subtraction> <literal type="real" value="365.25"/>….



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SQL Statement (adaptable for new schemas)

SELECT id,sys_id_value,numeric_value,time_value,text_value,boolean_value,code_value,data_type,institution_id,patient_id,service_code,primary_time FROM(

select t_from.* from (SELECT id,sys_id_value,numeric_value,time_value,text_value,boolean_value,code_value,data_type,institution_id,patient_id,service_code,primary_time ,

myrankFROM(

SELECT * FROM(SELECT

id,sys_id_value,numeric_value,time_value,text_value,boolean_value,code_value,data_type,institution_id,patient_id,service_code,primary_time, rank() OVER

(PARTITION BY INSTITUTION_ID,PATIENT_ID ORDER BY primary_time DESC) myrank FROM (select TO_NUMBER(NULL) as id,To_NUMBER(NULL) as sys_id_value,1 as numeric_value,DATE_OF_BIRTH as time_value, TO_CHAR(NULL) as text_value,To_CHAR(null) as boolean_value,TO_CHAR(NULL) as code_value,'T' as data_type, …



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Limitations

The process does not start directly with free text CPG. Knowledge Representation languages such as CARE or Arden are required.

If originating CPG does not conform to a standard terminology, lexical integration is required again.

Fuzzy or probabilistic representational languages are currently not incorporated.

Mismatched data granularity or ignored data sets will create database integration complicated.

Each CDSS SQL is mutually exclusive of other CDSS thus limiting the merger of SQLs that can result in a combined CDSS.




32

Application Areas

Clinical: CDSS reminder rules (e.g., CPOE integration) popup CDSS alerts for providers

Population-based:

o Healthcare quality measures (e.g., NQF, ACO) Quality monitoring dashboard

o Consumer Health Informatics (e.g., tethered PHRs)

o Public Health Informatics (PHI) (e.g., notifiable complex conditions)

o Transition of care (e.g., CCD-based CDSS for inpatient to outpatient)




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Previous Work > CDSS Application in EHR

RMRS sample database was used in this research

a subset of the INPC database

CLINICAL_VARIABLE 42,666,350 records

PATIENT 453,518 (unique)

INSTITUTION 118 providers

CARE KRL 74 Rules (SQL)



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Rules Population One patient*

CD4_1.sql 59 0.18CHOLESTEROL_1.sql 180 0.11CHOLESTEROL_2.sql 1 0.12DIABETES_1.sql 541 0.56DIABETES_2.sql 1561 0.47DIABETES_3.sql 182 0.40DIABETES_4.sql 121 0.40DIABETES_5.sql 1620 1.51

…. … …

TCL_2.sql 67 0.12TCL_3.sql 69 0.18TCL_4.sql 61 0.17TETANUS_SHOT_1.sql 1 0.07WHOLE_1.sql 127 0.16WHOLE_2.sql 129 0.13

Average (sec) 1249 0.73

Runtime per second

* each row shows average of 20 individual runs



35


Some rules are never hit for institute 1 (not shown in this diagram)

Applying all applicable CARE rules for Institute-1

0

10000

20000

30000

40000

50000

60000

70000

OBFO

RM_1.sql

OBFO

RM_3.sql

OBFO

RM_2.sql

OBFO

RM_5.sql

MEDICIN

E_2.sql

PREVENT_PAP_1.sql

PREVENT_STO

OL_2.sql

CHOLESTERO

L_2.sql

CHOLESTERO

L_1.sql

PREVENT_M

AM_2.sql

PNVX2_1.sql

FLU_SH

OT_4.sql

HEPVAX_B_1.sql

FLU_SH

OT_3.sql

PEDS_10.sql

PEDS_11.sql

PEDS_1.sql

PEDS_7.sql

PEDS_8.sql

PEDS_9.sql

TETANUS_SH

OT_1.sql

LIPID_MAN

AGEM

ENT_1.sql

TCL_2.sql

FLU_SH

OT_1.sql

PREVENT_M

AM_3.sql

K_2.sql

DIABETES_1.sql

PREVENT_STO

OL_4.sql

WHO

LE_2.sql

TCL_1.sql

DIABETES_5.sql

FLU_SH

OT_5.sql

K_1.sql

PREVENT_M

AM_1.sql

OBFO

RM_6.sql

IDC_REM_1.sql

TCL_4.sql

WHO

LE_1.sql

CD4_1.sql

PREVENT_STO

OL_1.sql

TCL_3.sql

HIV_PT_1.sql

MEDICIN

E_1.sql

Institute 1: All CDS Hits (regardless of missed proportion)

applicable CARE rules



36


Academic/Primary hospitals have the highest rates of hitsThis peak might be simply because of their higher population of patients (needs population adjustment)

Number of CARE rule hits against the RMRS sample database based on each healthcare provider / institute (zero-hit institutes are removed)

0

100,000

200,000

300,000

400,000

500,000

600,000

700,000

800,000

900,000

1,000,000

11 1 25 118

124

120

20 47 2 7

103 9

116

31 109

110

102

123

22 13 125

18 14 16 21 107

119

23 46 48 114

15 5 30 32 17 29 49 40 43 56 41 44 55

Total Hits

Academic Hospitals

institutes

0

10000

20000

30000

40000

50000

60000

70000

OBFO

RM_1.sql

OBFO

RM_3.sql

OBFO

RM_2.sql

OBFO

RM_5.sql

MEDICIN

E_2.sql

PREVENT_PAP_1.sql

PREVENT_STO

OL_2.sql

CHOLESTERO

L_2.sql

CHOLESTERO

L_1.sql

PREVENT_M

AM_2.sql

PNVX2_1.sql

FLU_SH

OT_4.sql

HEPVA

X_B_1.sql

FLU_SH

OT_3.sql

PEDS_10.sql

PEDS_11.sql

PEDS_1.sql

PEDS_7.sql

PEDS_8.sql

PEDS_9.sql

TETANUS_SH

OT_1.sql

LIPID_MAN

AGEM

ENT_1.sql

TCL_2.sql

FLU_SH

OT_1.sql

PREVENT_M

AM_3.sql

K_2.sql

DIABETES_1.sql

PREVENT_STO

OL_4.sql

WHOLE_2.sql

TCL_1.sql

DIABETES_5.sql

FLU_SH

OT_5.sql

K_1.sql

PREVENT_M

AM_1.sql

OBFO

RM_6.sql

IDC_REM

_1.sql

TCL_4.sql

WHOLE_1.sql

CD4_1.sql

PREVENT_STO

OL_1.sql

TCL_3.sql

HIV_PT_1.sql

MEDICIN

E_1.sql

Institute 1: All CDS Hits (regardless of missed proportion)



37


Percentage of rule hits is suddenly smaller in the left side of the diagram (i.e., primary hospitals)

Number of CARE rule hits against the RMRS sample database based on ever healthcare provider institute (population adjusted percentage)

0.00%

10.00%

20.00%

30.00%

40.00%

50.00%

60.00%

70.00%

80.00%

90.00%

100.00%

11 1

25

118

124

120

20

47 2 7

103 9

116

31

109

110

102

123

22

13

125

18

14

16

21

107

119

23

46

48

114

15 5

30

32

17

29

49

40

43

56

41

44

55

Adj Population

Other Hospitals

Percentage of rule hits is larger in the right side of the diagram (i.e., other hospitals)

Percentage > creates higher patient risk at a certain instituteRaw number > forms internal policies / economical road maps

0

100,000

200,000

300,000

400,000

500,000

600,000

700,000

800,000

900,000

1,000,000

11 1 25

118

124

120

20

47 2 7

103 9

116

31

109

110

102

123

22

13

125

18

14

16

21

107

119

23

46

48

114

15 5 30

32

17

29

49

40

43

56

41

44

55

Total Hits

institutes



38


Institute versus Rules (Raw Numbers) (zero rules / zero institutes are removed)

institutes

rules



39


Manual process of deciphering the rules (hit-means-miss OR hit-means-hit-only)The lower the better (less missed)

Missed applicable CARE rules for institute-1(CDSS-generated QM fingerprint)

0.00%

5.00%

10.00%

15.00%

20.00%

25.00%

30.00%

HIV_PT_1.sql

MEDICIN

E_1.sql

PREVENT_M

AM_1.sql

TCL_3.sql

WHOLE_1.sql

DIABETES_1.sql

PEDS_8.sql

FLU_SH

OT_5.sql

TCL_2.sql

TETANUS_SHO

T_1.sql

MEDICIN

E_2.sql

TCL_4.sql

FLU_SH

OT_4.sql

K_1.sql

OBFO

RM_5.sql

PREVENT_STO

OL_2.sql

CHOLESTERO

L_2.sql

FLU_SH

OT_3.sql

PREVENT_STO

OL_1.sql

PREVENT_PAP_1.sql

DIABETES_5.sql

OBFO

RM_1.sql

PEDS_10.sql

PNVX2_1.sql

OBFO

RM_6.sql

OBFO

RM_2.sql

CHOLESTERO

L_1.sql

WHOLE_2.sql

PREVENT_STO

OL_4.sql

LIPID_MAN

AGEMEN

T_1.sql

PEDS_11.sql

IDC_REM_1.sql

PEDS_9.sql

FLU_SH

OT_1.sql

OBFO

RM_3.sql

K_2.sql

CD4_1.sql

TCL_1.sql

PEDS_1.sql

PEDS_7.sql

PREVENT_M

AM_3.sql

PREVENT_M

AM_2.sql

HEPVAX_B_1.sql

Institute 1: Percentage of Hit / Miss Rules

Percent

%13.49Avg


~12.3%



40

0.00%

5.00%

10.00%

15.00%

20.00%

25.00%

30.00%

CD4_1.sql

FLU_SH

OT_1.sql

HIV_PT_1.sql

IDC_REM_1.sql

OBFO

RM_6.sql

PEDS_7.sql

PEDS_9.sql

TCL_4.sql

WHOLE_2.sql

PREVENT_PAP_1.sql

FLU_SH

OT_4.sql

PEDS_1.sql

PEDS_8.sql

OBFO

RM_2.sql

TETANUS_SHO

T_1.sql

PEDS_11.sql

OBFO

RM_3.sql

MEDICIN

E_2.sql

CHOLESTERO

L_1.sql

CHOLESTERO

L_2.sql

FLU_SH

OT_3.sql

K_2.sql

K_1.sql

PREVENT_M

AM_3.sql

OBFO

RM_1.sql

PEDS_10.sql

PNVX2_1.sql

OBFO

RM_5.sql

TCL_2.sql

LIPID_MAN

AGEM

ENT_1.sql

HEPVAX_B_1.sql

PREVENT_STO

OL_4.sql

PREVENT_STO

OL_2.sql

PREVENT_M

AM_2.sql

Institute 2: Inverse Percentage of Hit / Miss Rules

Percent

%3.47Avg


Manual process of deciphering the rules (hit-means-miss OR hit-means-hit-only)The lower the better (less missed)

Missed applicable CARE rules for institute-2(CDSS-generated QM fingerprint)


~5.5%



41


The effect size of the missed rules depends on the population size.

Missed rate by institute

institutes

rules

RulesMissed %



42


Web Interface (http://aurora.regenstrief.org:8080/ClinicalKnowledgeHub/)



43


Preventive Care Quality Improvement Reminder Forms

Applying all CARE rules against the RMRS sample database




44

Previous Work > CDSS Application in PHR



45

Previous Work > CDSS Application in PHR

Dynamically generated CDSS SQL commands

CHI Framework (Tx Group)



46

Traditional PubHI versus CDSS-based PopHI

Previous Work > Population-based CDSS

Item Traditional CDSS-based

Focus n-Patient-at-a-time 1-Patient-at-a-time

Model Statistical (probabilistic) CPG (rule-based)

Updatability Delayed (Hard coded) Immediate (Dynamic)

HIE Integration Possible Already Exists

Portability Major Change Req. Minimal Change Req.

Data Source EHR Subsets (limited) Complete EHR, PHR, …

Contextualization Complex SQL Driven



47

Consider the potentials with Population-based CDSS integration Copyright Regenstrief Institute

Previous Work > Population-based CDSS

Simple statistics based on numerical statistics

PopHI will reveal the

causes, and…



48

CPHIT / Future Work



49

The Johns Hopkins Center for Population Health Information Technology

(CPHIT, or “see-fit”)

The mission of CPHIT is to improve the health and wellbeing of populations by advancing the state-of-the-art of Health Information Technology and e-health tools used by public health agencies and private health care organizations.

CPHIT’s focus will be on the application of EHRs, PHRs and other digitally-supported health improvement interventions targeted at communities, special need populations and groups of consumers cared for by integrated delivery systems.

Director: Dr. J. Weiner

www.jhsph.edu/cphit

CPHIT / Future Work

Copyright Dr. J. Weiner



50

CPHIT / Future Work


CPHIT

JH Health System

External PH/IDS Orgs.

JHU Academic Departments

and other R&D centers

JH Healthcare Solutions,

LLC

BusinessPartners

IndustryFoundations Government

CPHIT Organizational Linkages



51

2009 HITECH Policies

Sta

ge 1

Sta

ge 2

Sta

ge 3

Meaningful Use

CPHIT / Future Work

HIT-Enabled Health Reform

2011 Criteria(Capture & Share)

2013 Criteria(CDSS) 2015 Criteria

(Improved Outcome)




52

MPI

CPHIT / Future Work

Federated Consistent Databases(1) Data gathered centrally in separate physical files, “mirrors” of remote sites;

(2) Standardized at the time it comes in.

Population-based CDSS across databases

Population-based HIE

EHRs

Claims

PACS LABs

Registries

PHRs



53

ProviderHIE

EHREHR

ACO

Patient

CPHIT / Future Work

EHR

PHR

CPOE

Rx

Labs

Insurance

Imaging

Sensors…

Outpatient

CDSS

PDSS

Tethered

Public Health

Predictive Modeling

Risk Assess.

MU1

MU2

MU3

CPG

EHR

Quality Measure

Care Manage.

Pay for Perform. Po

pula

tion

Hea

lth I

nfor

mat

ics

2.0



54

CPHIT’s initial R&D Priorities: Natural language processing (NLP) and pattern recognition that improve

population based interventions.

Effective approaches for integration of consumer-focused m-health/PHRs/ e-health with provider-focused EHRs.

Development of “e-measures” of population health status, risk, safety/quality using EHR/HIT systems.

EHR based tools to identify high risk populations for preventive and/or chronic care outreach.

Approaches for integrating current functions of public health agencies into interoperable EHR networks.

Approaches for applying HIT to population-based evidence-generation / research.

CPHIT / Future Work




55

Thank you

Q & A

Documents

Automating the Contextualization of Population-Based CDSS … · Automating the Contextualization of Population-Based CDSS in Tethered EHR/PHRs ... Automating the Contextualization