Theodore Rooseveldt
In November of
2014, The ONC (Office of the National Coordinator, I liked it better when it
was ONCHIT) posted guidance regarding clinical decision support (CDS). This
guidance proposed “5 rights” that applications & implementations that
provided CDS would have to support. The five rights[1]
were defined as:
·
Right
information (evidence-based guidance, response to clinical need)
·
Right
people (entire care team – including the patient)
·
Right
channels (e.g. EHR, mobile device,
patient portal)
·
Right
intervention formats (e.g. order
sets, flow-sheets, dashboards, patient lists)
·
Right
points in the workflow (for decision making action)
The ONC also
stated that CDS is more than just the delivery of alerts. The 5 rights provide
a good context for thinking about CDS & how it can/should be used. In
particular the CDS/PI (Clinical Decision Support Collaborative/Performance
Improvement) detailed “steps” table[2]
seems quite useful, at least as a source of information on CDS.
A number of
older, but still relevant (IMHO) academic studies provide more insight into the
most effective capabilities & features of CDS systems. Specifically,
Kawamoto, et al.[3]
did a meta-analysis of the effectiveness of CDS systems in improving outcomes
& found that four features made such systems most effective (in descending
order):
1. Automatic provision of CDS as part of
clinical workflow
2. Provision of recommendations rather than
just assessment
3. Provision of CDS at the time &
location of decision making, &
4. Provision of fully automated CDS rather
than partial reliance on the provider to look up information
Other features
that were significant but not at the level of these four were:
·
No
need for additional provider data entry
·
Requesting
explanation (from provider) of why recommendation(s) were not followed
(requires more analysis of decision by provider & may change decision)
·
Provision
of CDS results to patient (acknowledgement of usefulness of system &
transparency), & finally
·
Provider
education (often not by vendor) for use & interpretation of CDS
Other studies e.g. Garg, et al.[4]
also a meta-analysis, provide highly detailed analysis of effective features
for different types of targeted CDS (diagnosis, preventive care, diabetes
treatment, etc.).
My semi-recent
experience on a federal grant has given me some perspective on this. I was
Principal Investigator on a Department of Commerce (NIST) grant to develop a
secure network with identity verification at all endpoints for clinical eReferral.
This work was done with UC San Diego Medical Center & the San Diego VA. Direct
network connections were set up for UCSD & its medical affiliates including
a set of CHCs in the Columbia River Gorge area (OR). Part of the project was
that eReferrals done through the Direct connections would include the
possibility of using the ActiveHealth Management (now owned by Aetna) application
for CDS. What we found was that providers, on both sides of the connection were
loath to use the CDS as it required: additional &/or redundant information
to be entered (usually by the provider) & going out of the Direct
connect/EHR context. In addition, providers did not like that the application
made recommendations & provided copious amounts of background for the
recommendations, but allowed for only minimal provider input & feedback.
(FYI – ActiveHealth Management never followed through on its commitments to the
grant, so this capability was only tested in prototype.) The result was clear,
however. Providers were not interested in CDS/recommendations if they were not
fully integrated into their EHR & workflow. Even though ActiveHealth
Management was considered “best-of-class” CDS at the time, it was not enough to
tempt providers to use it consistently.
Other types of
patient-centered recommendation systems may also be interesting & relevant
here, specifically the point-of-care recommendation systems under development
(& in some cases being used by) by Kaiser, Partners, Geisinger, Mayo
Clinic, Cleveland Clinic etc. These systems do operate for the provider at the
point-of-care & meet most of the criteria described by Kawamoto, that is:
·
Are
provided automatically at the time & place of decision making
·
Make
recommendations, not just summarizations/assessments, &
·
Do
not require any additional data entry.
Of course, even
though these systems make recommendations based on the analysis of many
thousands - & in some cases millions – of patient records, they are only as
good as the quality of the data that they analyze. Based on our experience with
data quality in the Path-2-Analytics[5]
project, we can make some assumptions regarding the quality of a data set with
millions of records, the compilation of which started 12-15 years ago. Kaiser,
for instance, has 9M+ patient records dating from 2003. These types of systems
will become extremely important over the next 5-8 years, but today they are
more conceptually important than functionally important.
OK – so what’s
the bottom line here? The ONC, CMS etc. will continue to mandate that providers
begin using clinical decision systems in their clinical practice, but what
leverage do these systems actually provide? & how can providers &
healthcare organizations select CDS systems that will improve their practice?
The ONC defines CDS as: “Clinical
decision support (CDS) provides clinicians, staff, patients or other
individuals with knowledge and person-specific information, intelligently
filtered or presented at appropriate times, to enhance health and health care.
CDS encompasses a variety of tools to enhance decision-making in the clinical
workflow. These tools include computerized alerts and reminders to care
providers and patients; clinical guidelines; condition-specific order sets;
focused patient data reports and summaries; documentation templates; diagnostic
support, and contextually relevant reference information, among other tools.”[6]
The Stage 2
Meaningful Use measure for CDS (2016-17) is:[7]
“Measure1:Implement five clinical decision support
interventions related to four or more clinical quality measures at a relevant
point in patient care for the entire EHR reporting period. Absent four clinical
quality measures related to an EP’s scope of practice or patient population,
the clinical decision support interventions must be related to high priority
health conditions.
Measure
2: The EP has enabled and implemented the functionality for drug ‐ drug and drug allergy interaction
checks for the entire EHR reporting period.”
As ONC points
out, this set of capabilities requires the ability to access & analyze
patient-specific clinical data, some type of reasoning mechanism (rules, big
data analysis, human intervention etc.) to interpret the data & construct
recommendations, create care plans, assess current care etc. & the ability
to present information & inferences in an understandable & useful way.
It should also
be pointed out that the Stage 2 criteria are also linked to interventions
related to to the CMS clinical quality measures, that is some action must be
taken based on input from a CDS (alert, etc.) that is related to a quality
measure as described by CMS[8].
There are currently 64 of these measures ranging from HbA1c measures to
colorectal cancer screening to pediatric asthma screening
There are many
standalone CDS products, most of which are focused on a specific clinical area.
Examples are: interpretation of tumor imaging for specific types of tumors,
interpretation of retinal imaging for treatment of macular degeneration,
tracking of quality measures for individual patients & alerting provider
when measures fall outside of guidelines & many, many others. There are
very few general CDS systems, the best-known being ActiveHealth Management, a
system that relied on a set of clinicians & human experts that continuously
reviewed medical journals & other material to extract the latest
information on treatment patterns & outcomes. This information was then
translated into a rule set that was used to interact with provider input (or
automated input through CDS) regarding specific patients. Treatment
recommendations & treatment plans were then suggested to the provider. Very
few CDS except for ActiveHealth & the point-of-care recommendation systems
using big data analytics work like this. Most are much more targeted. Most CDS
function provided by EHRs is in the form of reminders & alerts, although
this is starting to change.
So how does a
healthcare organization select a CDS that will work to improve outcomes &
not just meet meaningful use requirements?
Here are some steps:
1. Determine what data you have available to
serve as the base for decision support. This will include your EHR & PM
systems as well as other internal data. It may also include external data as
relevant. External data may include clinical data from other sources (HIE,
eReferral etc.) or public &/or private clinical datasets.
2. Determine what type of decision support
you require & who the recipient of this support will be. Are you looking
for general point-of-care diagnostic & treatment planning support (order
sets, procedure lists etc.)? or are you looking for specific support in
particular types of image interpretation or other highly specialized support?
3. Develop a set of expectations, goals,
requirements, users & use cases for your CDS system.
4. Evaluate what CDS capabilities you already
have in your EHR or other HIT systems. Identify the gaps in the capabilities
you have versus your requirements.
5. Research & identify CDS systems that
will fill these gaps. Make sure you determine compatibility of any systems you
are researching with your existing HIT systems. Also make sure you understand
the skills required to use these systems & what training is required
(including but not limited to vendor supplied training as your staff may need
more general training on decision making & decision support).
6. Evaluate & test the system(s) you
have identified using the real use cases you described in Step 3. Use data that
is as realistic as possible, that is as close to your clinical data as possible
(in some cases, it may be possible to actually use your own data).
7. Work with the vendor to make sure that
the system you will purchase is customizable &/or configurable the way you
need & that it is fully compatible with your other software.
8. Arrange for a test period & do a
series of tests that exercise your use cases. Work with the vendor to remediate
any issues you find.
9. Arrange for any training that the vendor
&/or external organizations can provide.
At this point you are ready
to purchase your CDS system.
OK, you say,… that seems like
a lot to go through, especially since my EHR vendor assures me that their
product provides clinical decision support that is compliant with meaningful
use, PCMH, MIPS & any other regulation or guideline you have asked about.
My experience, as I’ve already stated, is that the CDS associated with EHR
products is mostly limited to alerts that are programmed to fire when specific
clinical measures are above guidelines or when a series of values trend above
guidelines. More recently, they can also make recommendations for & enable
ePrescription of specific drugs or even propose order sets (for CPOE). This is
all good, in fact much better than not providing such capabilities at all, but
if you have determined a broader set of requirements, then your EHR may not
provide everything you need. The bottom line is CDS that actually meets the
ONC’s definition is not provided by any current EHR or in fact even by any
combination of EHR & dedicated CDS application. It is most closely
approached by the point-of-care recommendation systems that are under
development & in preliminary use by organizations such as Kaiser
Permanente, Geisinger, Partners etc. These systems combine ultra-large data
sets with advanced deep learning & pattern recognition capabilities that
will be in general use in the next 3-5 years, but today are the exception.
I believe that the best we
can do today is a combination of CDS provided by your EHR along with a more
specialized system that addresses some of the additional requirements you have
identified. EHR decision support will get better only if it is required to, but
Stage 2+ meaningful use requirements as well as the proposed Stage 3 (maintaining
the two requirements for Stage 2 with additional emphasis on appropriate
position of CDS in the clinical workflow[9])
do not push for CDS as described by the ONC except to say that CMS wants to
encourage innovative development & use of decision support beyond alerts
& notifications. We have a long way to go before CDS is a really effective
addition to the care of patients… No number of definitions, usage descriptions
of reports by Federal agencies will make it happen. Vendors will develop
effective CDS only if it is required, so it is up to providers, policy experts
& healthcare organizations to both push for this development & to
produce their own definitions, descriptions, use cases & even prototypes as
appropriate & possible. Now is the time to start…
[1] https://www.cms.gov/Regulations-and
Guidance/Legislation/EHRIncentivePrograms/Downloads/ClinicalDecisionSupport_Tipsheet-.pdf
[3] K. Kawamoto, C. Houlihan, E.A. Balas, D.F. Lobach.
Improving clinical practice using clinical decision support systems: A
systematic review of trial to identify features critical to success. BMJ,
doi:10.1136/bmj.38398.500764.8F (published 14 March 2005)
[4] A.X.
Garg, et al., Effects of computerized
decision support systems on practitioner performance & patient outcomes.
JAMA. 293(10). P. 1223-1238.
[5] Path-2-Analytics Project: Process & Results
Review, Association of Clinicians for the Underserved Annual Meeting.
Washington, D.C, June 2015.
[7]
https://www.cms.gov/Regulations-and-Guidance/Legislation/EHRIncentivePrograms/Downloads/EP_ObjectiveMeasuresTable-.pdf
[9]
https://www.healthit.gov/providers-professionals/how-attain-meaningful-use
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