• No results found

Mining an EHR for Quality Data:

N/A
N/A
Protected

Academic year: 2021

Share "Mining an EHR for Quality Data:"

Copied!
38
0
0

Loading.... (view fulltext now)

Full text

(1)

Mining an EHR for Quality Data:

Practice Culture, Workflow &

Technical Issues

Bill Jones, Deborah Mielke, M.D., Theresa L. Cleary, RN

October 23, 2009

(2)

Theresa L. Cleary, RN

Clinical Informatics Nurse

(3)

OCHC’s History & Mission

The mission of Open

Cities Health Center is to

provide culturally

competent primary and

preventive health care

and related services to

all people throughout the

Twin Cities metropolitan

area.

The goal is to improve

the health and well-being

of high-risk and

vulnerable populations

through the provision of

affordable medical and

dental care and related

services.

(4)

Who Does OCHC Serve?

65% of our patients have

income levels at or below

the Federal Poverty Level.

31% of our patients are

uninsured.

Over two-thirds are

people of color.

39% African American

21% South East Asian

5% Hispanic/Latino

1% Native American

13% are better served in a

language other than

English.

62% of our patients are

female.

(5)

An Organic Approach to EHR

Implementation

"Many times we let barriers stop us in our

tracks - we have to go around them and

keep on walking. There are a lot of people

that are cheering for you to be successful."

(6)

Executive EHR Committee

Medical Director, Deborah Mielke, MD

IT Director, Bill Jones

Health Information Manager, Pamela Akins

Clinical Informatics Nurse, Theresa L. Cleary, RN

Clinical Director, Sherry Pittman, RN

(7)

OCHC Receives MN e-Health

Implementation Grant

12/1/2007 – MN e-Health Grant project

begins.

Clinical Informatics Nurse joins EHR

committee.

(8)

OCHC’s EHR/HIT Vision

“To have a fully

integrated,

interoperable system

that incorporates

evidence-based

standards to enhance

workflow, promote

patient safety, and elicit

clinical outcomes to

improve patient care.”

(9)

A Stepwise Approach to

Implementation

Asthma Template Measures

Diabetic Flow Sheet

(10)

Building the Foundation….

Create policies for quality reporting based

on funding requirements and/or quality

measures.

Define/Maintain Clinical documentation

standards.

Select the right tools to support effective

documentation.

Strengthen the knowledge and

understanding of staff regarding the value

of their clinical data documentation toward

population health monitoring and

(11)
(12)

Disease Specific Flow sheets

Summary of quality

measures

Retrievable and

Reportable

Standardizes

documentation

Patient centered

approach

Warnings included

for omissions

(13)

Balancing act…..

Revise workflows to

ensure user-friendly

process for capturing

comprehensive data.

Keep EHR code sets

up to date to ensure

compliance and HL7

exchange.

Perform routine

(14)

Deborah Mielke, MD

Medical Director

(15)

How do we use EMR in real

practice?

Data needs to be entered into the

computer by support staff and

providers

How and where the data is entered

affects the ability to use this data in

reports

How the system retrieves the data

also affects the reports

(16)
(17)

How is data entered into EMR?

Clinical Reference Items- Vitals, labs

Preventative Health Module

Immunization module

Prescription writer

Template trees***

Free text ***

(18)

Preventative Health Maintenance

(PHM) Reminders & Reports

(19)

Clinical Decision Support

Specialized visit templates and flow

sheets

Online references & calculators

Best practice order sets

(20)
(21)

Data forms that are not

retrievable

Template trees

Free text

Dictations

Hand written notes

Scanned items

(22)

S:

This patient is in today for follow-up

on his multiple medical problems. Patient has a

history of renal insufficiency and is being

followed by nephrology at this time. The

patient states that he refuses to have dialysis.

Patient also has a history of heart disease. He

has a history of hypertension, history of

elevated cholesterol, he has had abdominal

aortic aneurysm repair in the past, degenerative

arthritis of multiple joints and obesity. Patient

also comes in today complaining of erectile

dysfunction.

O:

Vitals: Age: 70. Weight: 300 pounds.

Height: 5’11”. Temperature: 97.4. Blood

pressure: 150/72. Pulse: 76. Respirations: 20.

Generally, the patient is an obese male seen in

no distress. Lungs: Clear. Heart: Rhythm

regular. No murmurs or gallops heard. GI:

Abdomen is obese. No masses or tenderness.

Extremities: There is trace edema in the lower

extremities.

A:

1) Diabetes Mellitus, Type II.

2) Arteriosclerotic heart disease.

(23)

4) Hyperlipidemia

5) Renal insufficiency, severe.

6) Status post aortic aneurysm repair.

7) Anemia, probably associated with

renal insufficiency.

8) Degenerative arthritis of multiple

sites, primarily of the knees and ankles.

9) Erectile dysfunction.

P:

Patient will be restarted on

Furosemide 80 mg. daily. He also was given

samples of Cialis 20 mg. every 36 hours as

needed. Continue Plavix 75 mg. daily, Norvasc

10 mg. daily, Cozaar 100 mg., Coreg 25 mg.

twice daily, Prilosec 20 mg. daily, Clonidine

0.2 mg. twice daily, Simvastatin 40 mg. daily,

and NovoLog mix insulin

(24)
(25)

How is the data retrieved?

Some one has to ask the right

questions

Too much data is not useful

Total counts of patients not useful

What population are you looking at?

Numerator and Denominator

(26)

Useful reports and data

Patient registries

Dashboards for providers

UDS reports

Pay for performance reports

(27)

How are we doing with these?

Problem with registries- too much

data, incomplete, need to add and

change over time.

Provider dashboards- Data linked to

diagnosis and is not consistent

MN community measures- Our

reports missing data that is in

dictations and not retrievable

(28)

What is working?

Prenatal registry using information

from prenatal module

Ongoing data collection for diabetic

patients

Beginning to build a diabetic registry

List of children needing shots

Reporting “influenza like illness” to

CDC

(29)
(30)
(31)
(32)
(33)
(34)

Considerations

1.

Data Integrity

2.

Work back from report to data

3.

Continuous verification (don’t expect

to be notified of process changes)

(35)

A

1c

A1

c

BP

BP

LD

L

LD

L

S

m

ok

e

S

m

ok

e

E

ye

F

o

ot

M

/

A

100%

7

90%

40%

70%

12%

70%

90%

80%

% of

registr

y with

A1c

data

A1

c

>

On

e

A1

c

% of

regis

try

with

2

A1c'

s

BP

#

% of

regist

ry

with

a BP

enter

ed

BP

<1

30/

80

% of

BP

with

SYS

<13

0/80

lipi

ds

tes

ted

%

of

regi

stry

with

ldl

entr

y

LD

L<

10

0

% of

lipid

s

<10

0

do

cu

me

nte

d

sm

oki

ng

%

ask

ed

abo

ut

sm

oki

ng

cur

ren

t

sm

ok

er

%of

regis

try

who

smo

ke

ey

e

ex

a

m

% of

regis

try

with

eye

exa

m

Fo

ot

ex

a

m

% of

regis

try

with

foot

exa

m

m

i

c

r

o

t

e

s

t

e

d

a

s

pi

ri

n

u

s

e

%

usin

g

aspir

in

ther

apy

Sel

f

Mg

mt

71% 7.4 208 38% 527 97%

26

5

50% 140 26% 80 57% N/A N/A N/A N/A N/ A N/A N/ A N/A 72% 7.2 205 38% 527 97%

27

0

51% 185 34% 88 48% N/A N/A N/A N/A N/ A N/A N/ A N/A 82% 7.7 394 47% 833 100% 323 39% 284 34% 142 50% 830 99% 212 25% 30 4% 27 3% 25 3% 37 83% 7.7 391 47% 834 99% 312 37% 252 30% 130 52% 827 99% 210 25% 29 3% 23 3% 17 2% 49 83% 7.7 404 48% 845 100% 327 39% 249 29% 128 51% 841 99% 208 24% 29 3% 23 3% 14 2% 53 83% 7.9 390 45% 860 99% 332 39% 229 26% 126 55% 854 99% 198 23% 28 3% 30 3% 15 2% 60 84% 7.5 398 46% 869 99% 346 40% 244 28% 133 55% 865 99% 192 22% 27 3% 33 4% 18 2% 69 84% 7.8 395 46% 859 99% 329 38% 237 27% 129 54% 856 99% 196 23% 27 3% 31 4% 16 2% 72 84% 7.8 409 47% 872 100% 327 38% 278 32% 158 57% 864 99% 193 22% 27 3% 32 4% 16 2% 74 85% 7.8 405 46% 868 100% 324 37% 313 36% 176 56% 862 99% 188 22% 26 3% 41 5% 27 3% 80 86% 7.8 429 49% 872 100% 337 39% 345 39% 195 57% 867 99% 189 22% 27 3% 39 4% 24 3% 87 85% 7.9 415 49% 847 100% 336 40% 366 43% 199 54% 840 99% 189 22% 25 3% 43 5% 32 4% 101 84% 7.9 422 49% 851 100% 343 40% 392 46% 218 56% 843 99% 195 23% 48 6% 47 6% 38 4% 102 84% 7.9 449 51% 867 99% 358 41% 424 49% 241 57% 861 99% 193 22% 68 8% 75 9% 72 8% 111

(36)

Age

s

24-59

HIV

Test

ed

(age

24-59)

in

last

12m

o.

%

HIV

test

ed

1

ye

ar

old

s

An

em

ia

T

-12

-15

mo

.

-36

5to

45

5d

ay

s

%

Ane

mia

test

ed

age

50 +

He

mo

cc

ult

for

ag

e

50

+

in

las

t

12

m

%

He

m

oc

ult

2n

d

birt

hd

ay

in

pri

or

mo

nth

1

9

s

h

ot

s

gi

v

e

n

%

all

Sho

ts

by

age

2

Fem

ale

age

s

21-64

F

age

d

21-64

w/

Pap

test

in

last

36

mo.

%

Pap

test

ed

ag

e 0

firs

t

visi

t

by

32

da

ys

%

Ne

wb

orn

s

w/fi

rst

visi

t by

32

day

s

ag

es

1,2

,3

act

ive

pat

ien

ts

ag

e

1,2

,3

w/l

ea

d

tes

t

las

t

12

mo

.

%

1,2,

& 3

yr.

old

s

Lea

d

test

ed

in

last

yr

ag

e 3

A

g

e

3

wi

th

>

6

W

C

C'

s

3yr

.ol

ds--%

wit

h >

6

W

CC

Fe

ma

e

age

13-25

4329 728 17% 240 104 43% 2115 112 5% 3876 2373 61% 613 278 45% 188 26 14% 5277 784 15% 238 99 42% 2127 126 6% 163 68 42% 3848 2233 58% 608 284 47% 188 19 10% 1574 5379 844 16% 252 108 43% 2191 129 6% 167 72 43% 3952 2276 58% 453 195 43% 638 302 47% 197 19 10% 162 5318 861 16% 244 105 43% 2147 130 6% 194 75 39% 3920 2309 59% 455 184 40% 633 308 49% 195 21 11% 158 5465 907 17% 260 104 40% 2216 143 6% 196 74 38% 4023 2359 59% 460 194 42% 662 314 47% 198 19 10% 161 5455 936 17% 262 103 39% 2234 146 7% 183 56 31% 4025 2388 59% 451 176 39% 645 296 46% 195 17 9% 159 5470 974 18% 247 99 40% 2254 151 7% 182 49 27% 4032 2410 60% 480 196 41% 624 267 9% 196 23 12% 159 5480 1017 19% 252 98 39% 2264 155 7% 24 9 38% 4046 2428 60% 499 199 40% 625 268 43% 195 20 10% 162 5497 1064 19% 257 99 39% 2297 159 7% 24 10 42% 4066 2447 60% 473 205 43% 622 256 41% 190 21 11% 1622 5541 1095 20% 260 95 37% 2334 165 7% 21 6 29% 4102 2432 59% 461 199 43% 628 245 39% 185 18 10% 162 5597 1163 21% 254 97 38% 2348 162 7% 11 2 18% 4139 2421 58% 470 201 43% 599 246 41% 173 20 12% 163 5628 1133 20% 253 88 35% 2374 162 7%

27

6

22% 4125 2438 59% 444 197 44% 604 244 40% 173 22 13% 164 5443 1144 21% 262 87 33% 2416 156 6% 24 8 33% 4148 2431 59% 435 178 41% 626 238 38% 177 20 11% 1622 5701 1123 20% 272 110 40% 2424 152 6% 25 7 28% 4156 2435 59% 431 190 44% 638 251 39% 184 16 6% 161

(37)

Electronic Medical Records provides

an opportunity to quickly monitor/audit

patients and see how well they and

we are doing

This is not an easy process but it is

worth every hour and every dollar we

spend on it.

(38)

Questions?

Please contact:

[email protected]

[email protected]

References

Related documents

This occurs to a certain degree in Ti-834 (Figure 5.8b)), but Figure 5.8c) gives an example of the more pronounced effect observed in Ti-6Al-4V. It is clear that the response

NIST defines cloud computing as [1] “a model for enabling convenient, on-demand network access to a shared pool of configurable computing resources (e.g., networks, servers,

• How do bicycle facilities access or bike network impact

I can’t give any logical reason why I tell this joke except that I think it’s really a funny joke, it always makes people laugh, and through the years I have not received any

Ardashir, a Magus, rejuvenates Zoroastrianism The revival of Zoroastrianism continues with unabating zeal The Pahlavi works are written by many hands in successive periods The

Distributions of production and related workers by employer expenditures as a percent of gross payroll, for selected supplementary compensation practices, meatpacking

This study examined the relationship between service quality dimensions (reliability, tangibles, responsiveness, assurance and empathy) and customer satisfaction among mobile phone