Clinical trial NCT02578290: Clinical Decision Support (CDS) for Outpatient Radiology Imaging
Official title: The Impact of Clinical Decision Support (CDS) for Outpatient Radiology Imaging: A Randomized Control Trial
Study type: INTERVENTIONAL
Overall status: TERMINATED
Phase: NA
Conditions: CT and MR Image Orders
Keywords: decision support systems
clinical
Lead sponsor: Abdul Latif Jameel Poverty Action Lab
Sponsor class: OTHER
Collaborators: name: Icahn School of Medicine at Mount Sinai
First posted: 2015-10-16
Enrollment: 2033
Sex: ALL
Standard ages: CHILD
ADULT
OLDER_ADULT
Accepts healthy volunteers: yes
Allocation: RANDOMIZED
Intervention model: PARALLEL
Primary purpose: HEALTH_SERVICES_RESEARCH

Brief summary: The goal of the study is to determine whether clinical decision support (CDS) affects the number, type, or appropriateness of targeted high-cost radiology images (i.e. MR and CT) ordered. The CDS will be delivered in Epic through ACRSelect software, which is a leading decision support tool based on the American College of Radiology (ACR) Appropriateness Criteria (see http://www.acr.org/Quality-Safety/Appropriateness-Criteria), and presents the ACR appropriateness scores for each image on a scale of 1-9 with 1-3 labelled as 'usually not appropriate', 4-6 'May be appropriate', and 7-9 'usually appropriate'.

Detailed description: With healthcare spending accounting for almost one-fifth of the U.S. economy and an even larger share of public sector budgets, there is substantial interest in innovations in healthcare delivery that can reduce the "over use" of resources that have no or low value to patients. As a result, there is a key need for rigorous evidence on scalable interventions aimed at improving the efficiency of the U.S. healthcare sector in general, and in the public sector in particular, which accounts for $1.25 trillion in annual healthcare spending (Centers for Medicare \& Medicaid Services, 2013).

In particular, there is widespread concern in both the medical profession (Callaghan et al., 2014; Sherman, 2012) and the public sector (U.S. Government Accountability Office, 2008) of the cost and health risks of "over-scanning". Estimates suggest that as many as 30% of imaging in the U.S. are unnecessary (Consumer Reports, 2015; Dehn et al., 2000; Georgiou et al., 2011). Medicare direct spending on "high-cost" scans (e.g. MRs and CTs) was about $10 billion in 2012, or about 2% of total Medicare costs (Medicare Payment Advisory Commission, 2014); the indirect costs are likely considerably greater, since imaging often triggers additional follow up care (Sherman, 2012; Shreibati and Baker, 2011). It is also estimated that about 2 percent of cancers in the U.S. are due to CT use (Brenner and Hall, 2007).

Reflecting this concern, starting in 2017 Medicare will no longer reimburse for high-cost scans unless ordered using an "acceptable" Clinical Decision Support (CDS) system (Pitts, 2014). Despite this upcoming policy change, we know of no large-scale randomized trials on the impact of CDS for imaging.

The intervention in this study provides Clinical Decision Support (CDS) for targeted high-cost radiology orders, MR and CT scans, to healthcare providers treating patients in outpatient settings affiliated with Mount Sinai Hospital and Mount Sinai Queens hospital in New York City. CDS is a tool embedded in an order entry system that provides information and guidance to providers on whether their intended order is "appropriate" and whether there are more highly recommended alternatives. The randomization is at the provider level: one group will receive the CDS, while the remaining providers will serve as the control group.

The CDS will be delivered through the order-entry software, Epic, through ACRSelect software, which is a leading decision support tool based on the American College of Radiology (ACR) Appropriateness Criteria.

Recommendations that appear in the CDS tool are a computerized version of guidelines created by the American College of Radiology (ACR). The guidelines score the appropriateness of a scan order for a given health indication, where indications include common symptoms and diagnosis keywords, such as "acute headache." In particular, indication-scan pairs are assigned an "appropriateness rating" from 1-9. Scores 1-3 are 'usually not appropriate,' 4-6 are 'may be appropriate,' and 7-9 are 'usually appropriate.'

Eligibility criteria: Inclusion Criteria:

* Outpatient medical provider with scan ordering permissions at Mount Sinai Hospital or Mount Sinai Queens
* Has an active ID in the Epic electronic medical record ordering system

Exclusion Criteria:

* Opted out of the study prior to October 13, 2015

Primary outcomes:
measure: number of "non-advised" scans ordered per visiting provider
timeFrame: first 365 days after CDS is turned on for the treatment group
Secondary outcomes:
measure: Number of scans ordered per visiting provider that ACR Select rates 1-3 ("usually not appropriate")
timeFrame: first 365 days after CDS is turned on for the treatment group
measure: Number of scans ordered per visiting provider that ACR Select rates 4-6 ("may be appropriate") for which an alternative scan (MR, CT, or other modality) rated 7-9 ("usually appropriate") exists
timeFrame: first 365 days after CDS is turned on for the treatment group
Other outcomes:
Locations:
city: Long Island City
country: United States
facility: Mount Sinai Queens
state: New York
city: New York
country: United States
facility: Mount Sinai Hospital
state: New York
Study officials:
affiliation: Massachusetts Institute of Technology
name: Amy Finkelstein, PhD
role: PRINCIPAL_INVESTIGATOR
affiliation: The Mount Sinai Health System
name: Madhu Mazumdar, Mazumdar
role: PRINCIPAL_INVESTIGATOR
affiliation: The Mount Sinai Health System
name: Bruce Darrow, MD, PhD
role: PRINCIPAL_INVESTIGATOR
affiliation: The Mount Sinai Health System
name: Joseph Kannry, MD
role: PRINCIPAL_INVESTIGATOR
affiliation: The Mount Sinai Health System
name: David S Mendelson, MD FACR
role: PRINCIPAL_INVESTIGATOR
affiliation: Massachusetts Institute of Technology
name: Joseph Doyle, PhD
role: PRINCIPAL_INVESTIGATOR
affiliation: Massachusetts Institute of Technology
name: Jesse Shapiro, PhD
role: PRINCIPAL_INVESTIGATOR
affiliation: Massachusetts Institute of Technology
name: Laura Feeney, MA
role: PRINCIPAL_INVESTIGATOR
References:
citation: Centers for Medicare & Medicaid Services, 2013. National Health Expenditures 2013 Highlights. Centers for Medicare & Medicaid Services.
citation: Callaghan BC, Kerber KA, Pace RJ, Skolarus LE, Burke JF. Headaches and neuroimaging: high utilization and costs despite guidelines. JAMA Intern Med. 2014 May;174(5):819-21. doi: 10.1001/jamainternmed.2014.173. No abstract available.
citation: Sherman, D., 2012. Stemming the tide of overtreatment in U.S. healthcare. Reuters. Feb 16, 2012.
citation: U.S. Government Accountability Office, 2008. Medicare Part B Imaging Services: Rapid Spending Growth and Shift to Physician Offices Indicate Need for CMS to Consider Additional Management Practices [WWW Document]. URL http://www.gao.gov/products/GAO-08-452 (accessed 2.23.15).
citation: Consumer Reports, 2015. Surprising Dangers of CT Scans and X-rays - Consumer Reports [WWW Document]. URL http://www.consumerreports.org/cro/magazine/2015/01/the-surprising-dangers-of-ct-sans-and-x-rays/index.htm (accessed 2.25.15).
citation: Dehn, T.G., O'Connell, B., Hall, R.N., Moulton, T., 2000. Appropriateness of imaging examinations: current state and future approaches. Imaging Econ 13, 18-26.
citation: Georgiou A, Prgomet M, Markewycz A, Adams E, Westbrook JI. The impact of computerized provider order entry systems on medical-imaging services: a systematic review. J Am Med Inform Assoc. 2011 May 1;18(3):335-40. doi: 10.1136/amiajnl-2010-000043. Epub 2011 Mar 8.
citation: Medicare Payment Advisory Commission, 2014. Health Care Spending and the Medicare Program. MedPAC.
citation: Shreibati JB, Baker LC. The relationship between low back magnetic resonance imaging, surgery, and spending: impact of physician self-referral status. Health Serv Res. 2011 Oct;46(5):1362-81. doi: 10.1111/j.1475-6773.2011.01265.x. Epub 2011 Apr 21.
citation: Brenner DJ, Hall EJ. Computed tomography--an increasing source of radiation exposure. N Engl J Med. 2007 Nov 29;357(22):2277-84. doi: 10.1056/NEJMra072149. No abstract available.
citation: Pitts, J., 2014. The Protecting Access to Medicare Act of 2014.