<clinical_study>
  <!-- This xml conforms to an XML Schema at:
    https://clinicaltrials.gov/ct2/html/images/info/public.xsd -->
  <required_header>
    <download_date>ClinicalTrials.gov processed this data on September 20, 2023</download_date>
    <link_text>Link to the current ClinicalTrials.gov record.</link_text>
    <url>https://clinicaltrials.gov/ct2/show/NCT02996045</url>
  </required_header>
  <id_info>
    <org_study_id>JPAL-5002</org_study_id>
    <nct_id>NCT02996045</nct_id>
  </id_info>
  <brief_title>Clinical Decision Support (CDS) for Radiology Imaging</brief_title>
  <official_title>The Impact of Clinical Decision Support (CDS) for Radiology: A Randomized Control Trial</official_title>
  <sponsors>
    <lead_sponsor>
      <agency>Abdul Latif Jameel Poverty Action Lab</agency>
      <agency_class>Other</agency_class>
    </lead_sponsor>
    <collaborator>
      <agency>Aurora Health Care</agency>
      <agency_class>Other</agency_class>
    </collaborator>
    <collaborator>
      <agency>Laura and John Arnold Foundation</agency>
      <agency_class>Other</agency_class>
    </collaborator>
    <collaborator>
      <agency>Massachusetts Institute of Technology</agency>
      <agency_class>Other</agency_class>
    </collaborator>
  </sponsors>
  <source>Abdul Latif Jameel Poverty Action Lab</source>
  <oversight_info>
    <has_dmc>No</has_dmc>
  </oversight_info>
  <brief_summary>
    <textblock>
      The goal of the study is to determine whether clinical decision support (CDS) for radiology&#xD;
      affects the number, type, or appropriateness of targeted high-cost radiology imaging orders&#xD;
      (i.e. magnetic resonance (MR), computed tomography (CT), nuclear medicine (NM) and Positron&#xD;
      Emission Tomography (PET) scans). The CDS will be delivered to physicians in the Aurora&#xD;
      Health Care system. It will be delivered in Epic, an industry-standard electronic medical&#xD;
      record software, through ACR Select, which is a leading decision support tool based on the&#xD;
      American College of Radiology (ACR) Appropriateness Criteria (see&#xD;
      http://www.acr.org/Quality-Safety/Appropriateness-Criteria). The ACR Select tool rates&#xD;
      imaging orders on a scale of 1-9 with 1-3 labelled as 'usually not appropriate', 4-6 'May be&#xD;
      appropriate', and 7-9 'usually appropriate'.&#xD;
    </textblock>
  </brief_summary>
  <detailed_description>
    <textblock>
      With healthcare spending accounting for almost one-fifth of the U.S. economy and an even&#xD;
      larger share of public sector budgets, there is substantial interest in innovations in&#xD;
      healthcare delivery that can reduce the &quot;over use&quot; of resources that have no or low value to&#xD;
      patients. As a result, there is a key need for rigorous evidence on scalable interventions&#xD;
      aimed at improving the efficiency of the U.S. healthcare sector in general, and in the public&#xD;
      sector in particular, which accounts for $1.25 trillion in annual healthcare spending.&#xD;
&#xD;
      In particular, there is widespread concern in both the medical profession and the public&#xD;
      sector of the cost and health risks of &quot;over-scanning&quot;. Estimates suggest that as many as 30%&#xD;
      of imaging in the U.S. are unnecessary. Medicare direct spending on &quot;high-cost&quot; scans (e.g.&#xD;
      MRs and CTs) was about $10 billion in 2012, or about 2% of total Medicare costs ; the&#xD;
      indirect costs are likely considerably greater, since imaging often triggers additional&#xD;
      follow up care. It is also estimated that about 2 percent of cancers in the U.S. are due to&#xD;
      CT use.&#xD;
&#xD;
      Reflecting this concern, starting in 2018 Medicare will no longer reimburse for high-cost&#xD;
      scans unless ordered using an &quot;acceptable&quot; Clinical Decision Support (CDS) system. Despite&#xD;
      this upcoming policy change, the investigators know of no large-scale randomized trials on&#xD;
      the impact of CDS for imaging.&#xD;
&#xD;
      The intervention in this study provides Clinical Decision Support (CDS) for targeted&#xD;
      high-cost radiology orders, (MR, CT, NM, and PET scans), to healthcare providers treating&#xD;
      patients in settings affiliated with the Aurora Health Care system headquartered in&#xD;
      Wisconsin. CDS is a tool embedded in an order entry system that provides information and&#xD;
      guidance to providers on whether their intended order is &quot;appropriate&quot; and whether there are&#xD;
      more highly recommended alternatives. The randomization is at the provider level: half will&#xD;
      receive the CDS, while the remaining half of providers in the study will serve as the control&#xD;
      group.&#xD;
&#xD;
      The CDS will be delivered through the order-entry software, Epic, through ACR Select&#xD;
      software, which is a leading decision support tool based on the American College of Radiology&#xD;
      (ACR) Appropriateness Criteria.&#xD;
&#xD;
      Recommendations that appear in the CDS tool are a digitized version of guidelines created by&#xD;
      the American College of Radiology (ACR). The guidelines score the appropriateness of a scan&#xD;
      order for a given health indication, where indications include common symptoms and diagnosis&#xD;
      keywords, such as &quot;acute headache.&quot; In particular, indication-scan pairs are assigned an&#xD;
      &quot;appropriateness rating&quot; from 1-9. Scores 1-3 are 'usually not appropriate,' 4-6 are 'may be&#xD;
      appropriate,' and 7-9 are 'usually appropriate.'&#xD;
&#xD;
      To learn more about how CDS impacts ordering behavior, the investigators will investigate&#xD;
      whether those exposed to CDS orders in ways that avoids CDS, such as placing them via&#xD;
      clerical workers or choosing different indications. The investigators plan to analyze&#xD;
      outcomes across different settings (Inpatient vs Outpatient and in the ED). Last, if the&#xD;
      investigators find effects of CDS on ordering they will examine effects on utilization,&#xD;
      including length of inpatient stays.&#xD;
&#xD;
      The primary outcome is the number of imaging orders that would produce a best-practice alert&#xD;
      suggesting a change. The investigators conducted power calculations using a 7-month&#xD;
      intervention window and 6 months of pre-period data to calculate the lag of the dependent&#xD;
      variable as a control. With this control, the minimum detectible effect is a 13% reduction&#xD;
      compared to a mean of 11 scans over that time period.&#xD;
&#xD;
      In addition, the investigators will investigate the number of scans (all high-cost, high-cost&#xD;
      scoring 1-3, high-cost scoring 4-6, and substitution to low-cost scans. The investigators&#xD;
      will also investigate whether treatment group orders in ways that avoids the CDS, such as&#xD;
      placing them via clerical workers or choosing different indications. The investigators plan&#xD;
      to analyze outcomes across different settings (Inpatient vs Outpatient and in the ED). Last,&#xD;
      if the investigators find effects of CDS on ordering they will examine effects on&#xD;
      utilization, including length of inpatient stays.&#xD;
    </textblock>
  </detailed_description>
  <overall_status>Completed</overall_status>
  <start_date>December 15, 2016</start_date>
  <completion_date type="Actual">December 2018</completion_date>
  <primary_completion_date type="Actual">December 2018</primary_completion_date>
  <phase>N/A</phase>
  <study_type>Interventional</study_type>
  <has_expanded_access>No</has_expanded_access>
  <study_design_info>
    <allocation>Randomized</allocation>
    <intervention_model>Parallel Assignment</intervention_model>
    <primary_purpose>Health Services Research</primary_purpose>
  </study_design_info>
  <primary_outcome>
    <measure>number of scans ordered that would be subject to the CDS best practice alert</measure>
    <time_frame>first 365 days after CDS is turned on for the treatment group</time_frame>
    <description>Scans where the CDS would be triggered based on the criteria listed in the intervention description</description>
  </primary_outcome>
  <secondary_outcome>
    <measure>Number of high-cost scans (CT, MR, NM and PET) ordered</measure>
    <time_frame>first 365 days after CDS is turned on for the treatment group</time_frame>
  </secondary_outcome>
  <secondary_outcome>
    <measure>Number of scans ordered that would be subject to the CDS best practice alert and that ACR Select rates 1-3 (&quot;usually not appropriate&quot;)</measure>
    <time_frame>first 365 days after CDS is turned on for the treatment group</time_frame>
    <description>Scans where the CDS would be triggered and scored 1-3 based on the criteria listed in the intervention description.</description>
  </secondary_outcome>
  <secondary_outcome>
    <measure>Number of scans ordered that would be subject to the CDS best practice alert and that ACR Select rates 4-6 (&quot;may be appropriate&quot;)</measure>
    <time_frame>first 365 days after CDS is turned on for the treatment group</time_frame>
    <description>Scans where the CDS would be triggered and scored 4-6 based on the criteria listed in the intervention description.</description>
  </secondary_outcome>
  <other_outcome>
    <measure>Number of low-cost scans ordered</measure>
    <time_frame>first 365 days after CDS is turned on for the treatment group</time_frame>
    <description>includes X-rays, ultra sounds, mammograms, fluoroscopy and bone densitometry</description>
  </other_outcome>
  <number_of_arms>2</number_of_arms>
  <enrollment type="Actual">3511</enrollment>
  <condition>CT, MR, NM, and PET Image Orders</condition>
  <arm_group>
    <arm_group_label>Treatment</arm_group_label>
    <arm_group_type>Experimental</arm_group_type>
    <description>Clinical Decision Support (CDS)</description>
  </arm_group>
  <arm_group>
    <arm_group_label>Control</arm_group_label>
    <arm_group_type>No Intervention</arm_group_type>
    <description>Will not receive Clinical Decision Support (CDS)</description>
  </arm_group>
  <intervention>
    <intervention_type>Other</intervention_type>
    <intervention_name>Clinical Decision Support (CDS)</intervention_name>
    <description>A best practices alert (BPA) pop-up screen providing CDS will appear at physician sign-off for all scans scored 1-6, and scans scored 7-8 for which an alternative scan scored 8-9 exists.&#xD;
This screen will show the appropriateness score of the original scan order, and will display up to 7 alternative scans that are scored &gt;4 and greater than or equal to the original score for the same indications and patient characteristics. It will also display a link to relevant ACR documentation relevant to the selected scan and indication.</description>
    <arm_group_label>Treatment</arm_group_label>
  </intervention>
  <eligibility>
    <criteria>
      <textblock>
        Inclusion Criteria:&#xD;
&#xD;
          -  Medical provider actively employed at Aurora with a valid Epic log-in.&#xD;
&#xD;
          -  Is a Medical Doctor (MD), Doctors of Osteopathic Medicine (DO), podiatrist (DPM),&#xD;
             nurse practitioner (NP), physician assistant (PA), or certified nurse midwife (CNM)&#xD;
&#xD;
          -  Has imaging order permissions at Aurora Health Care.&#xD;
&#xD;
          -  Has at least one high- or low-cost imaging order in the year from November 1, 2015 to&#xD;
             November 1, 2016 or is medical resident who joined Aurora since that time.&#xD;
&#xD;
        Exclusion Criteria:&#xD;
&#xD;
          -  Opted out of the study prior to November 24, 2016&#xD;
      </textblock>
    </criteria>
    <gender>All</gender>
    <minimum_age>N/A</minimum_age>
    <maximum_age>N/A</maximum_age>
    <healthy_volunteers>No</healthy_volunteers>
  </eligibility>
  <overall_official>
    <last_name>Joseph Doyle, PhD</last_name>
    <role>Principal Investigator</role>
    <affiliation>Massachusetts Institute of Technology</affiliation>
  </overall_official>
  <overall_official>
    <last_name>Amy Finkelstein, PhD</last_name>
    <role>Principal Investigator</role>
    <affiliation>Massachusetts Institute of Technology</affiliation>
  </overall_official>
  <overall_official>
    <last_name>Sarah Reimer, MD</last_name>
    <role>Principal Investigator</role>
    <affiliation>Aurora Health Care</affiliation>
  </overall_official>
  <overall_official>
    <last_name>Laura Feeney, MA</last_name>
    <role>Principal Investigator</role>
    <affiliation>Massachusetts Institute of Technology</affiliation>
  </overall_official>
  <overall_official>
    <last_name>Sarah Abraham</last_name>
    <role>Principal Investigator</role>
    <affiliation>Massachusetts Institute of Technology</affiliation>
  </overall_official>
  <location>
    <facility>
      <name>Aurora Health Care</name>
      <address>
        <city>Milwaukee</city>
        <state>Wisconsin</state>
        <zip>53204</zip>
        <country>United States</country>
      </address>
    </facility>
  </location>
  <location_countries>
    <country>United States</country>
  </location_countries>
  <reference>
    <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>
    <PMID>24638246</PMID>
  </reference>
  <reference>
    <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>
    <PMID>21385821</PMID>
  </reference>
  <reference>
    <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>
    <PMID>21517834</PMID>
  </reference>
  <reference>
    <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>
    <PMID>18046031</PMID>
  </reference>
  <reference>
    <citation>Centers for Medicare &amp; Medicaid Services, 2013. National Health Expenditures 2013 Highlights. Centers for Medicare &amp; Medicaid Services.</citation>
  </reference>
  <reference>
    <citation>Sherman, D., 2012. Stemming the tide of overtreatment in U.S. healthcare. Reuters. Feb 16, 2012.</citation>
  </reference>
  <reference>
    <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>
  </reference>
  <reference>
    <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>
  </reference>
  <reference>
    <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>
  </reference>
  <reference>
    <citation>Medicare Payment Advisory Commission, 2014. Health Care Spending and the Medicare Program. MedPAC.</citation>
  </reference>
  <reference>
    <citation>Pitts, J., 2014. The Protecting Access to Medicare Act of 2014.</citation>
  </reference>
  <verification_date>July 2020</verification_date>
  <study_first_submitted>December 15, 2016</study_first_submitted>
  <study_first_submitted_qc>December 16, 2016</study_first_submitted_qc>
  <study_first_posted type="Estimate">December 19, 2016</study_first_posted>
  <last_update_submitted>July 13, 2020</last_update_submitted>
  <last_update_submitted_qc>July 13, 2020</last_update_submitted_qc>
  <last_update_posted type="Actual">July 15, 2020</last_update_posted>
  <responsible_party>
    <responsible_party_type>Sponsor</responsible_party_type>
  </responsible_party>
  <patient_data>
    <sharing_ipd>No</sharing_ipd>
  </patient_data>
  <!-- Results have not yet been posted for this study                                          -->
</clinical_study>


download date: ClinicalTrials.gov processed this data on September 20, 2023
link text: Link to the current ClinicalTrials.gov record.
url: https://clinicaltrials.gov/ct2/show/NCT02996045
org study id: JPAL-5002
nct id: NCT02996045
lead sponsor: 
collaborator: 
collaborator: 
collaborator: 
has dmc: No
The goal of the study is to determine whether clinical decision support (CDS) for radiology
      affects the number, type, or appropriateness of targeted high-cost radiology imaging orders
      (i.e. magnetic resonance (MR), computed tomography (CT), nuclear medicine (NM) and Positron
      Emission Tomography (PET) scans). The CDS will be delivered to physicians in the Aurora
      Health Care system. It will be delivered in Epic, an industry-standard electronic medical
      record software, through ACR Select, 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). The ACR Select tool rates
      imaging orders on a scale of 1-9 with 1-3 labelled as 'usually not appropriate', 4-6 'May be
      appropriate', and 7-9 'usually appropriate'.
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.

      In particular, there is widespread concern in both the medical profession and the public
      sector of the cost and health risks of "over-scanning". Estimates suggest that as many as 30%
      of imaging in the U.S. are unnecessary. 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 ; the
      indirect costs are likely considerably greater, since imaging often triggers additional
      follow up care. It is also estimated that about 2 percent of cancers in the U.S. are due to
      CT use.

      Reflecting this concern, starting in 2018 Medicare will no longer reimburse for high-cost
      scans unless ordered using an "acceptable" Clinical Decision Support (CDS) system. Despite
      this upcoming policy change, the investigators 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, CT, NM, and PET scans), to healthcare providers treating
      patients in settings affiliated with the Aurora Health Care system headquartered in
      Wisconsin. 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: half will
      receive the CDS, while the remaining half of providers in the study will serve as the control
      group.

      The CDS will be delivered through the order-entry software, Epic, through ACR Select
      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 digitized 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.'

      To learn more about how CDS impacts ordering behavior, the investigators will investigate
      whether those exposed to CDS orders in ways that avoids CDS, such as placing them via
      clerical workers or choosing different indications. The investigators plan to analyze
      outcomes across different settings (Inpatient vs Outpatient and in the ED). Last, if the
      investigators find effects of CDS on ordering they will examine effects on utilization,
      including length of inpatient stays.

      The primary outcome is the number of imaging orders that would produce a best-practice alert
      suggesting a change. The investigators conducted power calculations using a 7-month
      intervention window and 6 months of pre-period data to calculate the lag of the dependent
      variable as a control. With this control, the minimum detectible effect is a 13% reduction
      compared to a mean of 11 scans over that time period.

      In addition, the investigators will investigate the number of scans (all high-cost, high-cost
      scoring 1-3, high-cost scoring 4-6, and substitution to low-cost scans. The investigators
      will also investigate whether treatment group orders in ways that avoids the CDS, such as
      placing them via clerical workers or choosing different indications. The investigators plan
      to analyze outcomes across different settings (Inpatient vs Outpatient and in the ED). Last,
      if the investigators find effects of CDS on ordering they will examine effects on
      utilization, including length of inpatient stays.
allocation: Randomized
intervention model: Parallel Assignment
primary purpose: Health Services Research
measure: number of scans ordered that would be subject to the CDS best practice alert
time frame: first 365 days after CDS is turned on for the treatment group
description: Scans where the CDS would be triggered based on the criteria listed in the intervention description
measure: Number of high-cost scans (CT, MR, NM and PET) ordered
time frame: first 365 days after CDS is turned on for the treatment group
measure: Number of scans ordered that would be subject to the CDS best practice alert and that ACR Select rates 1-3 ("usually not appropriate")
time frame: first 365 days after CDS is turned on for the treatment group
description: Scans where the CDS would be triggered and scored 1-3 based on the criteria listed in the intervention description.
measure: Number of scans ordered that would be subject to the CDS best practice alert and that ACR Select rates 4-6 ("may be appropriate")
time frame: first 365 days after CDS is turned on for the treatment group
description: Scans where the CDS would be triggered and scored 4-6 based on the criteria listed in the intervention description.
measure: Number of low-cost scans ordered
time frame: first 365 days after CDS is turned on for the treatment group
description: includes X-rays, ultra sounds, mammograms, fluoroscopy and bone densitometry
arm group label: Treatment
arm group type: Experimental
description: Clinical Decision Support (CDS)
arm group label: Control
arm group type: No Intervention
description: Will not receive Clinical Decision Support (CDS)
intervention type: Other
intervention name: Clinical Decision Support (CDS)
description: A best practices alert (BPA) pop-up screen providing CDS will appear at physician sign-off for all scans scored 1-6, and scans scored 7-8 for which an alternative scan scored 8-9 exists.
This screen will show the appropriateness score of the original scan order, and will display up to 7 alternative scans that are scored >4 and greater than or equal to the original score for the same indications and patient characteristics. It will also display a link to relevant ACR documentation relevant to the selected scan and indication.
arm group label: Treatment
criteria: 
gender: All
minimum age: N/A
maximum age: N/A
healthy volunteers: No
last name: Joseph Doyle, PhD
role: Principal Investigator
affiliation: Massachusetts Institute of Technology
last name: Amy Finkelstein, PhD
role: Principal Investigator
affiliation: Massachusetts Institute of Technology
last name: Sarah Reimer, MD
role: Principal Investigator
affiliation: Aurora Health Care
last name: Laura Feeney, MA
role: Principal Investigator
affiliation: Massachusetts Institute of Technology
last name: Sarah Abraham
role: Principal Investigator
affiliation: Massachusetts Institute of Technology
facility: 
country: United States
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.
PMID: 24638246
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.
PMID: 21385821
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.
PMID: 21517834
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.
PMID: 18046031
citation: Centers for Medicare & Medicaid Services, 2013. National Health Expenditures 2013 Highlights. Centers for Medicare & Medicaid Services.
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: Medicare Payment Advisory Commission, 2014. Health Care Spending and the Medicare Program. MedPAC.
citation: Pitts, J., 2014. The Protecting Access to Medicare Act of 2014.
responsible party type: Sponsor
sharing ipd: No