Top

Journal of Imaging Informatics in Medicine

Published in:

01-06-2017

Inpatient Complexity in Radiology—a Practical Application of the Case Mix Index Metric

Authors: Thusitha Mabotuwana, Christopher S. Hall, Sebastian Flacke, Shiby Thomas, Christoph Wald

Published in: Journal of Imaging Informatics in Medicine | Issue 3/2017

Abstract

With ongoing healthcare payment reforms in the USA, radiology is moving from its current state of a revenue generating department to a new reality of a cost-center. Under bundled payment methods, radiology does not get reimbursed for each and every inpatient procedure, but rather, the hospital gets reimbursed for the entire hospital stay under an applicable diagnosis-related group code. The hospital case mix index (CMI) metric, as defined by the Centers for Medicare and Medicaid Services, has a significant impact on how much hospitals get reimbursed for an inpatient stay. Oftentimes, patients with the highest disease acuity are treated in tertiary care radiology departments. Therefore, the average hospital CMI based on the entire inpatient population may not be adequate to determine department-level resource utilization, such as the number of technologists and nurses, as case length and staffing intensity gets quite high for sicker patients. In this study, we determine CMI for the overall radiology department in a tertiary care setting based on inpatients undergoing radiology procedures. Between April and September 2015, CMI for radiology was 1.93. With an average of 2.81, interventional neuroradiology had the highest CMI out of the ten radiology sections. CMI was consistently higher across seven of the radiology sections than the average hospital CMI of 1.81. Our results suggest that inpatients undergoing radiology procedures were on average more complex in this hospital setting during the time period considered. This finding is relevant for accurate calculation of labor analytics and other predictive resource utilization tools.

Centers for Medicare & Medicaid Services: NHE Fact Sheet. [cited 2016 Jul 17]; Available from: https://www.cms.gov/research-statistics-data-and-systems/statistics-trends-and-reports/nationalhealthexpenddata/nhe-fact-sheet.html

Kanzaria, H.K., J.R. Hoffman, M.A. Probst, et al.: Emergency physician perceptions of medically unnecessary advanced diagnostic imaging. Acad Emerg Med, 2015. 22(4): p. 390–8.CrossRefPubMed

Lavery, H.J., J.S. Brajtbord, A.W. Levinson, et al.: Unnecessary imaging for the staging of low-risk prostate cancer is common. Urology, 2011. 77(2): p. 274–8.CrossRefPubMed

peer60. Unnecessary Imaging: Up to $12 Billion Wasted Each Year. [cited 2016 Jul 17]; Available from: http://research.peer60.com/unnecessary-imaging/

Centers for Medicare & Medicaid Services. Comprehensive Care for Joint Replacement Model. [cited 2016 Jul 17]; Available from: https://innovation.cms.gov/initiatives/cjr

Centers for Medicare & Medicaid Services. Health Care Payment Learning and Action Network. [cited 2016 Jul 15]; Available from: https://www.cms.gov/newsroom/mediareleasedatabase/fact-sheets/2015-fact-sheets-items/2015-02-27.html

Kassing, P., M.W. Mulaik, and J. Rawson: Pricing radiology bundled CPT codes accurately. Radiol Manage, 2013. 35(2): p. 9–15.PubMed

Kirtane, M.: Ensuring the validity of labor productivity benchmarking. Healthc Financ Manage, 2012. 66(6): p. 126–8, 130, 132PubMed

Office of Statewide Health Planning and Development. Case Mix Index. [cited 2016 Jul 17]; Available from: http://www.oshpd.ca.gov/HID/Products/PatDischargeData/CaseMixIndex/

10.

Barbara W: Medicare payment for hospital outpatient services: a historical review of policy options - report to the Medicare payment advisory commission. 2005, Rand Corporation

11.

Centers for Medicare & Medicaid Services. FY 2011 Final Rule Data Files. 2011 [cited 2016 Jul 17]; Available from: https://www.cms.gov/Medicare/Medicare-Fee-for-Service-Payment/AcuteInpatientPPS/FY-2011-IPPS-Final-Rule-Home-Page-Items/CMS1237932.html

12.

Centers for Medicare & Medicaid Services. Acute Inpatient PPS. [cited 2016 Jul 17]; Available from: https://www.cms.gov/Medicare/Medicare-Fee-for-Service-Payment/AcuteInpatientPPS/index.html?redirect=/acuteinpatientpps/

13.

Centers for Medicare & Medicaid Services. Addendum A and Addendum B Updates. [cited 2016 Jul 17]; Available from: https://www.cms.gov/Medicare/Medicare-Fee-for-Service-Payment/HospitalOutpatientPPS/Addendum-A-and-Addendum-B-Updates.html

14.

American College of Emergency Physicians. APC (Ambulatory Payment Classifications). [cited 2016 Jul 17]; Available from: http://www.acep.org/Clinical---Practice-Management/APC-%28Ambulatory-Payment-Classifications%29-FAQ/

15.

Lahey Hospital & Medical Center. [cited 2016 Jul 17]; Available from: http://www.lahey.org/

16.

Epic. [cited 2016 Jul 15]; Available from: http://www.epic.com/

17.

Summary of the HIPAA Privacy Rule. [cited 2016 Jul 15]; Available from: http://www.hhs.gov/hipaa/for-professionals/privacy/laws-regulations/

18.

Cesta, T.: Follow these sure-fire tips to sort through data and measure outcomes in your department. Hosp Case Manag, 2011. 19(5): p. 71–3.PubMed

19.

Ajijola, O.A., E.A. Macklin, S.A. Moore, et al.: Inpatient vs. elective outpatient cardiac resynchronization therapy device implantation and long-term clinical outcome. Europace, 2010. 12(12): p. 1745–9.CrossRefPubMed

20.

Dickstein, K.: Inpatients are sicker than outpatients; finally it’s evidence-based. Europace, 2010. 12(12): p. 1662–3.CrossRefPubMed

21.

Duszak, R., Jr. and L.R. Muroff: Measuring and managing radiologist productivity, part 1: clinical metrics and benchmarks. J Am Coll Radiol, 2010. 7(6): p. 452–8.CrossRefPubMed

22.

Duszak, R., Jr. and L.R. Muroff: Measuring and managing radiologist productivity, part 2: beyond the clinical numbers. J Am Coll Radiol, 2010. 7(7): p. 482–9.CrossRefPubMed

23.

Katz, S. and G. Melmed: How Relative Value Units Undervalue the Cognitive Physician Visit: A Focus on Inflammatory Bowel Disease. Gastroenterol Hepatol (N Y), 2016. 12(4): p. 240–4.

24.

HL7.org. Welcome to FHIR. [cited 2016 Jul 15]; Available from: https://www.hl7.org/fhir/

Title: Inpatient Complexity in Radiology—a Practical Application of the Case Mix Index Metric
Authors: Thusitha Mabotuwana
Christopher S. Hall
Sebastian Flacke
Shiby Thomas
Christoph Wald
Publication date: 01-06-2017
Publisher: Springer International Publishing
Published in: Journal of Imaging Informatics in Medicine / Issue 3/2017
Print ISSN: 2948-2925
Electronic ISSN: 2948-2933
DOI: https://doi.org/10.1007/s10278-017-9944-y

At a glance: The STEP trials

Springer Medicine

Inpatient Complexity in Radiology—a Practical Application of the Case Mix Index Metric

Abstract

At a glance: The STEP trials

Springer Medicine

Abstract

Please log in to get access to this content

Other articles of this Issue 3/2017

Educational Material for 3D Visualization of Spine Procedures: Methods for Creation and Dissemination

Improving Patient Safety: Avoiding Unread Imaging Exams in the National VA Enterprise Electronic Health Record

Clinical Applications of a CT Window Blending Algorithm: RADIO (Relative Attenuation-Dependent Image Overlay)

Automating Perforator Flap MRA and CTA Reporting

Using Twitter to Assess the Public Response to the United States Preventive Services Task Force Guidelines on Lung Cancer Screening with Low Dose Chest CT

Value-Based Assessment of Radiology Reporting Using Radiologist-Referring Physician Two-Way Feedback System—a Design Thinking-Based Approach