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Online Patient Access to Their Medical Record and Health Providers

Is Associated with a Greater Use of Clinical Services

Ted E. Palen, PhD, MD, MSPH

Colleen Ross, MS, BSN

Stan Xu, PhD

Institute for Healthcare Research

Kaiser Permanente Colorado

HMORN 2012 session C1-04

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BACKGROUND

• Studies indicate patients want online access to medical records which allow:– review test results– schedule appointments– request refills– communicate with their providers by email

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BACKGROUND (cont.)

• Institute of Medicine report, "Crossing the Quality Chasm: A New Health System for the 21st Century”– electronic patient–physician messaging as a promising technology to

improve the quality and efficiency of healthcare

• Some researchers estimate that 25-70% medical care does not require a face-to-face appointment

• Department of Veterans Affairs: eHealth technologies– important model of healthcare to incorporate into clinical practice to

increase access to care

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BACKGROUND (cont.)

• Prior studies on the impact of secure online messaging and the use of other health care services have reported conflicting results

• Early MyChart experience in Kaiser Permanente patients with access to secure online messaging– decreased office visits– increased scheduled telephone contacts– compared to non-users of the service

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OBJECTIVE• To investigate the impact of member online

access to their health record on the use of:– office visits– telephone contacts– after hour clinic visits– emergency department use– hospitalizations

• We hypothesized that as the proportion of our members using online access increased, we would experience a proportional decrease in the use of “in-person” services

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METHODS• Inclusion criteria

– registered MyHealthManager users (≥13 yrs) for longer than 6 months– used at least one MyHealthManager feature– were continuously enrolled as KPCO members during the study period

• Study period– For the MyHealthManager user cohort

• Pre-registration period was 12 months before registration date• Post-registration period was 12 months after registration date

• Propensity scoring matched cohorts of non-MyHealthManager users – identify a matched cohort of non-MyHealthManager users and used the

matched MyHealthManager users registration date as index date for analysis

• Pre-registration period was 12 months before index date• Post-registration period was 12 months after index date

• Analysis– calculate the difference in use of services between the pre- and post-

periods– assess statistical significance with the generalized estimating equations

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METHODS• Since implementation of the MHM in KPCO in April 2006

member use increased– 25% at end of 2006 to >50% by June of 2009– 53.8% (375,620) as of June 2009

• Initial study cohort (≥ 13 years of age)– 83,804 non-MHM users– 89,340 MHM users

• Propensity score matching used to refine cohorts• Age• Gender• Race• Chronic health condition (Asthma, Diabetes, CAD, CHF)• Stratified by index year and office visits

– Refined cohorts each contained 46,925 members

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RESULTS

MHM user• 53.8% female• Ave. age 43.3 years

– < 20 yrs. 7.5%– 20-39 yrs. 36.9%– 40-59 yrs. 41.9%– 60+ yrs. 13.7%

• Race– White 51.8%– Hispanic 11.3%– Black 3.8%– Other 4.0%– Unknown 29.1%

non-MHM user• 52.8 % female• Ave. age 42.2 years

– < 20 yrs. 7.5%– 20-39 yrs. 37.1%– 40-59 yrs. 41.8%– 60+ yrs. 13.6%

• Race– White 52.1%– Hispanic 11.3%– Black 3.8%– Other 4.0%– Unknown 28.8%

46,925 KPCO members ≥ 13 years of age

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RESULTS (cont.)

MHM user• Chronic Health Conditions

– None 85.1% (N = 39,951)– 1 13.4% (N =

6,291)– 2 1.3% (N =

591)– 3 0.2% (N =

88)– 4 0.01% (N =

4)

• Chronic Condition– Asthma 7.9% (N = 3,726)– Diabetes 6.0% (N = 2,834)– CAD 1.6% (N = 749)– CHF 0.9% (N = 444)

non-MHM user• Chronic Health Conditions

– None 85.4% (N = 40,065)– 1 13.1% (N =

6,138)– 2 1.3% (N =

615)– 3 0.2% (N =

100)– 4 0.01% (N =

7)

• Chronic Condition– Asthma 7.8% (N = 3,677)– Diabetes 5.9% (N = 2,790)– CAD 1.6% (N = 762)– CHF 1.0% (N = 467)

46,925 KPCO members ≥ 13 years of age

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non-MHM User MHM User0.0

0.5

1.0

1.5

2.0

2.5

3.0

3.5

4.0

4.5

2.5 2.62.3

3.1

3.53.73.6

4.0

Office Visits Pre Office Visits PostPhone Contacts Pre Phone Contacts Post

En

cou

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rs p

er 1

2 M

on

ths

Encounters Before and After MyHealthManager Activation (NOTE: Activation Index Date Derived from User of MyHealthManager and Applied to Non-

users

* Significant change within group# Significant change between groups

#

*

*

#

*

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<50 yo non-MHM user

<50 yo MHM user ≥50 yo non-MHM User

≥50 MHM User0.0

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2.0

3.0

4.0

5.0

6.0

2.3 2.3

2.9 3.0

2.1

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Office Visit Before Office Visit AfterPhone Visit Before Phone Visit After

En

cou

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rs p

er 1

2 M

on

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Encounters Before and After MyHealthManager Activationby Age Group

# #

#*

*

*

* *

*

* Significant change within group# Significant change between groups

*

#

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No Chr

Dz no

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ser

No Chr

Dz M

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a no

n-M

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2.0

4.0

6.0

8.0

10.0

12.0

14.0

Office Visits Before Office Visits AfterPhone Visits Before Phone Visits After

En

cou

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er 1

2 M

on

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* Significant change within group# Significant change between groups

Encounters Before and After MyHealthManager Activationby Health Condition

** *

***

*

*

* *

*

#

##

#

# *

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non-MHM User MHM User0

20

40

60

80

100

120

140

160

180

200

84.7 88.278.6

100.2

182.2

140.5

181.7

150.8

63.2

50.9

63.670.8

After Hrs Before After Hrs After ED Visits BeforeED Visits After Hosp. Before Hosp. After

En

cou

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er 1

000

pat

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t-yr

sEncounters Before and After MyHealthManager Activation

by Location of Service

*

*

*

*

* Significant change within group# Significant change between groups

#

#

#

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<50 yo non-MHM User <50 yo MHM User ≥50 yo non-MHM User ≥50 yo MHM User0

20

40

60

80

100

120

140

160

180

200

90.197.7

72.5 67.8

81.9

105.9

72.2

93.4

177.8

143.3

184.7

130.9

177.7

149.2

185.4

147.4

54.6

44

84.1

62.8

50.2

66.2

91.9

81.2

After Hrs Before After Hrs After ED Visits BeforeED Visits After Hosp. Before Hosp. Afer

En

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rs p

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000

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sEncounters Before and After MyHealthManager Activation

by Age and Location of Service

*

*

*

**

*

*

#

#

#

#

#

* Significant change within group# Significant change between groups

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No Chr

Dz non

-MHM

...

No Chr

Dz MHM

Use

r

Asthm

a no

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HM ...

Asthm

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HM U

ser

Diabe

tic n

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Diabe

tic M

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CAD non

-MHM

Use

r

CAD MHM

Use

r

CHF non

-MHM

Use

r

CHF MHM

Use

r0

100

200

300

400

500

600

After Hrs Before After Hrs After ED Visits BeforeED Visits After Hosp. Before Hosp. After

En

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s

****

* Significant change within group# Significant change between groups

Encounters Before and After MyHealthManager Activationby Disease and Location of Service

* *

**

*

*

**

#

#

##

#

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CONCLUSIONS• MyHealthManager Users compared to non-Users

– rate of utilization of office and phone visits I n c r e ased– 18% increase in office visits– 9% increase in phone visits– In addition, the rates of

• After hours clinic visits• Emergency department visits• Hospitalizations

I n c r e a s e d

• This general trend was true for members:– <50 years of age and ≥50 years of age– Without chronic illnesses

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CONCLUSIONS (cont.)

• Incorporating online access into patient care may– highlight health concerns needing in-person evaluation

• Patients might have activated their access to online services in anticipation of health needs

• Patients may use this technology to– gain even better access care

• rather than to substitute for other types of contact with the health care system

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CONCLUSIONS (cont.)

• Providing patients with online access to their medical record may actually INCREASE demand for more traditional contacts with the healthcare system to address medical issues.

• Further research is needed to evaluate:– Reasons patients use online access to the healthcare services– If MHM users have different health outcomes compared to non-users– The cost/benefits of online access to healthcare services, e-visits, and

clinical decision-making– The impact on the allocation of clinical resources to deal with the

potential increase use of clinical service as eHealth applications become more widespread

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Acknowledgements

• David Ryerson, Data Specialist/SAS Programmer• J. David Powers, Biostatistician• John Steiner, MD, Dir. Institute for Healthcare Research • Cristy Geno, Project Manager

• QUESTIONS????