Online Appendix 1
In this online appendix, we provide further detail about the context, development, and
implementation of our intervention to improve cardiac biomarker ordering at Johns Hopkins
Bayview Medical Center. We have included factors that may influence the generalizability of our
process and results to other settings and institutions.
Context of implementation
Johns Hopkins Bayview is a 555-bed academic medical center in Baltimore, MD. In
general, the insurance breakdown for patients is 30% Medicare, 30% Medicaid, 25% with
commercial insurance, and 15% self-pay. Between 2009 and 2012, 61% of patients admitted to
the hospital or visiting the emergency department self-identified as white or Caucasian, 28% as
black or African American, 5% as Hispanic, and 7% as other race/ethnicity or could not or chose
not to self-identify. Between 2009 and 2012 an average of 4,225 patients presented to the
hospital or Emergency Department with a reason for visit of chest pain annually.
Adult inpatient services at Johns Hopkins Bayview include internal medicine, surgery
(including all major sub-specialties except cardiac), neurology, psychiatry, chemical dependence,
and obstetrics and gynecology. Approximately 50% of admissions go to internal medicine and
internal medicine subspecialty services with the remaining going to neuropsychiatric and
surgical services (Appendix Table 1). Ninety percent of patients are admitted via the Emergency
Department. Johns Hopkins Bayview has four intensive care units: a twelve bed medical
intensive care unit, a twelve bed cardiac intensive care unit, an eight bed neuro-intensive care
unit, a ten bed surgical intensive care unit and a ten bed burn/wound ICU.
1
Providers included Johns Hopkins Bayview Medical Center-based Department of
Medicine housestaff, housestaff for other departments rotating primarily from Johns Hopkins
Hospital, and attendings, nurse practitioners, and physician assistants in all departments (See
Appendix Table 2 for breakdown of provider type by year). Admissions to General Internal
Medicine services were split between a large hospitalist group and teaching services comprised
of housestaff. The proportion of admissions going to the hospitalist group increased from 50% to
60% from 2009 to 2012.
Johns Hopkins Bayview Medical Center employed a client-server version of Meditech
(Westwood, MA) for computer provider order entry throughout the study timeframe. Available
orders for cardiac biomarkers were troponin-I, total CK, and total CK and CK-MB fraction
ordered together (CK-MB could not be ordered independent of total CK). Before the study
intervention, there were twenty order sets containing orders for CK/CK-MB, two for CK and
eight with troponin orders. There was also an order for nurses to “Start CK Curve for CP, PRN”
that was found in two order sets, or could also be ordered individually. For many patients, the
first one or two orders for cardiac biomarkers were initiated by emergency department providers.
In the pre-intervention period, 87% of cardiac biomarkers ordered were for patients admitted to
General Internal Medicine, Cardiology, or Pulmonary services.
Intervention development and implementation
In the Fall of 2009, Physicians for Responsible Ordering was formed by a group of
providers committed to reducing low value inpatient diagnostic test ordering at Johns Hopkins
Bayview Medical Center. The group consisted of residents, faculty, and leadership from a broad
array of departments, including: internal medicine, emergency medicine, pathology, and hospital
2
administration. Based on clinical experience, we identified cardiac biomarkers as an initial
target. Our approach to developing the intervention is summarized in Appendix Table 3.
Prior to instituting changes to the provider order entry system, we sent all providers at Johns
Hopkins Bayview an e-mail that outlined the mission and goals of Physicians for Responsible
Ordering, a summary of the cardiac biomarker ordering guideline, a copy of the quick reference
pocket-sized card (main text Figure 1), and a review of changes to cardiac biomarker ordering
planned for Meditech. We also engaged in direct outreach to Internal Medicine and Emergency
Department providers whom most frequently ordered cardiac biomarkers. Physician members of
the study team briefed groups of providers on the initiative and provided an opportunity to ask
questions. This information was typically communicated in 5-10 minute sessions as part of
regularly scheduled administrative meetings. Specifically, we conducted meetings with at an
internal medicine housestaff morning report, a monthly internal medicine housestaff
administrative lunch meeting, and monthly administrative meetings for hospitalists, MICU and
CICU nursing staff, and the emergency department. We estimate that anywhere from 25%-75%
of providers in these groups were present for these meetings.
We monitored the implementation and success of the intervention in two ways. First, we
obtained quarterly reports on ordering of cardiac biomarker testing and compared them to preintervention results. We also kept open lines of communication with providers and addressed any
clinical concerns that may have appeared as a result of the guideline changes. No changes to the
intervention were made as a result of this monitoring process. Noting that in July 2012 there
would be turnover of residency staff throughout the hospital, new internal medicine residents
were briefed on the goals of the program and ordering guidelines during their orientation.
3
We believe several additional contextual factors may have contributed to the success of
our intervention. One of our study team members was on the cardiology faculty, and we included
faculty members, including the Chief of the Division of Cardiology in development of our
guideline. We engaged hospital and departmental leadership from the start of the planning
process who encouraged our efforts and professed support for the project to providers hospitalwide. We provided updates through presentations at Medicine Grand Rounds, departmental
meetings, and direct conversations with departmental leaders. We believe leadership support of
this initiative was instrumental in inducing provider behavior change.
Resources
The resources required to design and implement the intervention were primarily related to
individuals’ time. Guideline development and execution of the chart review required
approximately 60 hours from an Internal Medicine resident team member and 20 hours from a
Cardiology faculty team member. A team member from the Internal Medicine Hospitalist faculty
who is also the provider liaison with Meditech spent 15 hours reviewing order sets and facilitated
making changes to order sets and arranging for incorporation of the warning messages within the
order entry system. An analyst from Information Services spent 10 hours building and testing the
changes. An Internal Medicine Resident team member and Cardiology faculty team member
spent approximately 5 hours each in creating materials for and conducting educational sessions
with providers. An analyst from the Department of Care Management provided expertise on the
content of the Meditech data repository and executed data queries, requiring approximately 40
hours of analyst time. These services were provided in-kind, comprising administrative time
directed toward institutional quality improvement initiatives. We obtained two small grants of
4
which initial funding has been used to offset printing of quick reference cards (less than $200)
and a statistician to consult on conducting the interrupted times series analysis for this
manuscript.
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Appendix Table 1. Distribution of adult inpatient admissions by service at Johns Hopkins
Bayview from January 2009 through October 2012.
Admissions
Service
number/month
%
General Internal Medicine
606
30.5%
Internal Medicine Observation
217
10.9%
Cardiology
108
5.4%
Pulmonary
72
3.6%
Chemical Dependency Unit
221
11.1%
Psychiatry
76
3.8%
Neurology
75
3.8%
Obstetrics and Gynecology
155
7.8%
General Surgery
155
7.8%
Surgery Observation
64
3.2%
Orthopedic Surgery
83
4.2%
Neurosurgery
58
2.9%
Urology
24
1.2%
Trauma
12
0.6%
Plastic Surgery
8
0.4%
Otolaryngology
7
0.4%
Burn/wound
43
2.2%
6
Appendix Table 2. Breakdown of type of providers with ordering privileges at Johns Hopkins
Bayview Medical Center annually from 2009-2012.
2009
2010
2011
2012
Full-time faculty MDs
504
488
502
537
JHBMC Residents
75
71
80
80
JHH Resident Rotators*
75
75
75
75
Midlevel providers (NPs or PAs)
220
237
229
268
* Residents primarily based at Johns Hopkins Hospital who complete inpatient clinical rotations
at Johns Hopkins Bayview Medical Center.
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Appendix Table 3. Summary of process to develop intervention.
Task
Dates
Description
1. Chart Review (Estimate current
utilization)
December 2009


Reviewed charts of 35 patients admitted general medicine unit on a single day
in December 2009 (none received a diagnosis of ACS).
More than 80% of patients had at least one CK and CK-MB test ordered and
more than 20% of patients had four or more CK, CK-MB or troponin tests
(Appendix Table 4).
Reviewed published evidence.
Discussed with cardiology faculty.
2. Guideline Development
January 2010 –
December 2010


3. Beta-test guideline
January 2011July 2011


4. Reviewed Meditech order sets
January 2011 –
July 2011


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Reviewed guideline with housestaff and faculty.
Iteratively revised quick-reference card (final depicted in Figure 1 in main
text).
 Obtained buy-in from those with direct contact; however, identified skepticism
of widespread acceptance without further educational efforts leading us to
focus on improving knowledge and attitudes as part of intervention.
Identified all order sets with orders for troponin, CK, or CK-MB
Contacted administrative “owners” of order sets to explain initiative and obtain
permission to remove cardiac biomarkers from these order sets (see Appendix
text for detailed list of order sets edited)
Appendix Table 4. Cardiac biomarker testing for 35 patients admitted to a general medicine
service on a single day in December 2009.
Troponin
CK
CK-MB
2.4
3.2
3.0
Patients with > 1 test, n (%)
28 (80%)
29 (83%)
28 (80%)
Patients with > 3 tests, n (%)
8 (23%)
9 (26%)
8 (23%)
Mean tests per patient
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