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McCoy RG, Van Houten HK, Ross JS, Montori VM, Shah ND. HbA1c overtesting and overtreatment among US adults with controlled type 2 diabetes, 2001-13: observational population based study. BMJ 2015; 351:h6138. [PMID: 26646052 PMCID: PMC4673101 DOI: 10.1136/bmj.h6138] [Citation(s) in RCA: 42] [Impact Index Per Article: 4.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Accepted: 10/30/2015] [Indexed: 01/19/2023]
Abstract
STUDY QUESTION What is the extent and effect of excessive testing for glycated hemoglobin (HbA1c) among adults with controlled type 2 diabetes? METHODS A retrospective analysis of data from a national administrative claims database included commercially insured individuals in the USA, 2001-13. Study patients were aged 18 years or older, had type 2 diabetes with stable glycemic control (two consecutive tests showing HbA1c<7.0% within 24 months), did not use insulin, had no history of severe hypoglycemia or hyperglycemia, and were not pregnant. HbA1c testing frequency was measured within 24 months after the second (index) HbA1c test, and classified as guideline recommended (≤ 2 times/year), frequent (3-4 times/year), and excessive (≥ 5 times/year). Changes in treatment regimen were ascertained within three months of the index test. STUDY ANSWER AND LIMITATIONS Of 31,545 patients in the study cohort (mean age 58 years; mean index HbA1c 6.2%), HbA1c testing frequency was excessive in 6% and frequent in 55%. Despite good glycemic control at baseline, treatment was further intensified by addition of glucose lowering drugs or insulin in 8.4% of patients (comprising 13%, 9%, and 7% of those tested excessively, frequently, and per guidelines, respectively; P<0.001). Compared with guideline recommended testing, excessive testing was associated with treatment intensification (odds ratio 1.35 (95% confidence interval 1.22 to 1.50)). Excessive testing rates remained unchanged in 2001-08, but fell significantly after 2009. The odds of excessive testing was 46% lower in 2011 than in 2001-02. The study population is not representative of all US patients with type 2 diabetes because it was restricted to commercially insured adults with stable and controlled diabetes not receiving insulin treatment. The study design did not capture the underuse of HbA1c testing. WHAT THIS STUDY ADDS In this US cohort of adults with stable and controlled type 2 diabetes, more than 60% received too many HbA1c tests, a practice associated with potential overtreatment with hypoglycemic drugs. Excessive testing contributes to the growing problem of waste in healthcare and increased patient burden in diabetes management. FUNDING, COMPETING INTERESTS, DATA SHARING NDS and RGM are funded partly by the Agency for Healthcare Research and Quality (R18HS18339) and AcademyHealth Delivery System Science Fellowship (2013), respectively. No competing interests declared. Additional data are available from mccoy.rozalina@mayo.edu.
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Affiliation(s)
- Rozalina G McCoy
- Division of Primary Care Internal Medicine, Department of Medicine, Mayo Clinic, Rochester, MN 55905, USA Division of Health Care Policy and Research, Department of Health Sciences Research, Mayo Clinic
| | - Holly K Van Houten
- Division of Health Care Policy and Research, Department of Health Sciences Research, Mayo Clinic Robert D and Patricia E Kern Center for Science of Health Care Delivery, Mayo Clinic
| | - Joseph S Ross
- Section of General Internal Medicine and Robert Wood Johnson Clinical Scholars Program, Department of Internal Medicine, Yale School of Medicine, New Haven, CT, USA Center for Outcomes Research and Evaluation, Yale-New Haven Hospital, New Haven
| | - Victor M Montori
- Division of Endocrinology Metabolism and Nutrition, Department of Medicine, Mayo Clinic Knowledge and Evaluation Research Unit, Mayo Clinic
| | - Nilay D Shah
- Division of Health Care Policy and Research, Department of Health Sciences Research, Mayo Clinic Optum Labs, Cambridge, MA, USA
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Kuehl DR, Berdahl CT, Jackson TD, Venkatesh AK, Mistry RD, Bhargavan-Chatfield M, Raukar NP, Carr BG, Schuur JD, Kocher KE. Advancing the Use of Administrative Data for Emergency Department Diagnostic Imaging Research. Acad Emerg Med 2015; 22:1417-26. [PMID: 26575944 DOI: 10.1111/acem.12827] [Citation(s) in RCA: 8] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/07/2015] [Accepted: 07/09/2015] [Indexed: 01/18/2023]
Abstract
Administrative data are critical to describing patterns of use, cost, and appropriateness of imaging in emergency care. These data encompass a range of source materials that have been collected primarily for a nonresearch use: documenting clinical care (e.g., medical records), administering care (e.g., picture archiving and communication systems), or financial transactions (e.g., insurance claims). These data have served as the foundation for large, descriptive studies that have documented the rise and expanded role of diagnostic imaging in the emergency department (ED). This article summarizes the discussions of the breakout session on the use of administrative data for emergency imaging research at the May 2015 Academic Emergency Medicine consensus conference, "Diagnostic Imaging in the Emergency Department: A Research Agenda to Optimize Utilization." The authors describe the areas where administrative data have been applied to research evaluating the use of diagnostic imaging in the ED, the common sources for these data, and the strengths and limitations of administrative data. Next, the future role of administrative data is examined for answering key research questions in an evolving health system increasingly focused on measuring appropriateness, ensuring quality, and improving value for health spending. This article specifically focuses on four thematic areas: data quality, appropriateness and value, special populations, and policy interventions.
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Affiliation(s)
- Damon R. Kuehl
- Department of Emergency Medicine; Virginia Tech Carilion School of Medicine; Roanoke VA
| | - Carl T. Berdahl
- Department of Emergency Medicine; Los Angeles County + University of Southern California Medical Center; Los Angeles CA
| | - Tiffany D. Jackson
- Department of Emergency Medicine; University of Alabama Birmingham; Birmingham AL
| | | | - Rakesh D. Mistry
- Department of Emergency Medicine; Section of Emergency Medicine; Children's Hospital Colorado; Aurora CO
| | | | - Neha P. Raukar
- Department of Emergency Medicine; Warren Alpert Medical School of Brown University; Providence RI
| | - Brendan G. Carr
- Department of Emergency Medicine; Sidney Kimmel Medical College; Thomas Jefferson University; Philadelphia PA
| | - Jeremiah D. Schuur
- Department of Emergency Medicine; Brigham and Women's Hospital; Boston MA
| | - Keith E. Kocher
- Department of Emergency Medicine; University of Michigan School of Medicine; Ann Arbor MI
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Deshmukh AJ, Yao X, Schilz S, Van Houten H, Sangaralingham LR, Asirvatham SJ, Friedman PA, Packer DL, Noseworthy PA. Pacemaker implantation after catheter ablation for atrial fibrillation. J Interv Card Electrophysiol 2015; 45:99-105. [PMID: 26546104 DOI: 10.1007/s10840-015-0071-8] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/20/2015] [Accepted: 10/21/2015] [Indexed: 11/26/2022]
Abstract
BACKGROUND Sinus node dysfunction requiring pacemaker implantation is commonly associated with atrial fibrillation (AF), but may not be clinically apparent until restoration of sinus rhythm with ablation or cardioversion. We sought to determine frequency, time course, and predictors for pacemaker implantation after catheter ablation, and to compare the overall rates to a matched cardioversion cohort. METHODS AND RESULTS We conducted a retrospective analysis using a large US commercial insurance database and identified 12,158 AF patients who underwent catheter ablation between January 1, 2005 and December 31, 2012. Over an average of 2.4 years of follow-up, 5.6 % of the patients underwent pacemaker implantation. Using the Cox proportional hazards models, we found that risk of risks of pacemaker implantation was associated with older age (50-64 and ≥65 versus <50 years), female gender, higher CHADS2 score (≥2 and 1 versus 0), higher Charlson index (≥2 versus 0-1), certain baseline comorbidities (conduction disorder, coronary atherosclerosis, and congestive heart failure), and the year of ablation. There was no significant difference in the risk of pacemaker implantation between ablation patients and propensity score (PS)-matched cardioversion groups (3.5 versus. 4.1 % at 1 year and 8.8 versus 8.3 % at 5 years). CONCLUSION Overall, pacemaker implantation occurs in about 1/28 patients within 1 year of catheter ablation. The overall implantation rate decreased between 2005 and 2012. Furthermore, the risk after ablation is similar to cardioversion, suggesting that patients require pacing due to a common underlying electrophysiologic substrate, rather than the ablation itself.
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Affiliation(s)
- Abhishek J Deshmukh
- Cardiovascular Diseases, Mayo Clinic Heart Rhythm Section, Rochester, MN, USA
| | - Xiaoxi Yao
- Mayo Clinic Robert D. and Patricia E. Kern Center for Science of Health Care Delivery, Rochester, MN, USA
- Optum Labs, Cambridge, MA, USA
| | - Stephanie Schilz
- Mayo Clinic Robert D. and Patricia E. Kern Center for Science of Health Care Delivery, Rochester, MN, USA
| | - Holly Van Houten
- Mayo Clinic Robert D. and Patricia E. Kern Center for Science of Health Care Delivery, Rochester, MN, USA
| | - Lindsey R Sangaralingham
- Mayo Clinic Robert D. and Patricia E. Kern Center for Science of Health Care Delivery, Rochester, MN, USA
| | - Samuel J Asirvatham
- Cardiovascular Diseases, Mayo Clinic Heart Rhythm Section, Rochester, MN, USA
| | - Paul A Friedman
- Cardiovascular Diseases, Mayo Clinic Heart Rhythm Section, Rochester, MN, USA
| | - Douglas L Packer
- Cardiovascular Diseases, Mayo Clinic Heart Rhythm Section, Rochester, MN, USA
| | - Peter A Noseworthy
- Cardiovascular Diseases, Mayo Clinic Heart Rhythm Section, Rochester, MN, USA.
- Division of Cardiovascular Diseases, Mayo Clinic College of Medicine, Rochester, MN, 55902, USA.
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304
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Noseworthy PA, Yao X, Deshmukh AJ, Van Houten H, Sangaralingham LR, Siontis KC, Piccini JP, Asirvatham SJ, Friedman PA, Packer DL, Gersh BJ, Shah ND. Patterns of Anticoagulation Use and Cardioembolic Risk After Catheter Ablation for Atrial Fibrillation. J Am Heart Assoc 2015; 4:JAHA.115.002597. [PMID: 26541393 PMCID: PMC4845220 DOI: 10.1161/jaha.115.002597] [Citation(s) in RCA: 49] [Impact Index Per Article: 5.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
Abstract
Background There is significant practice variation in oral anticoagulation (OAC) use following catheter ablation for atrial fibrillation. It is not clear whether the risk of cardioembolism increases after discontinuation of OAC following catheter ablation. Methods and Results We identified 6886 patients within a large national administrative claims database who underwent catheter ablation for atrial fibrillation between January 1, 2005, and September 30, 2014. We assessed the effect of time off of OAC by CHA2DS2‐VASc score (after adjusting for other comorbidities) on risk of cardioembolism, using Cox proportional hazards models. There was an increase in the use of non–vitamin K OAC after ablation from 0% in 2005 to 69.8% in 2014. OAC discontinuation was high, with only 60.5% and 31.3% of patients remaining on OAC at 3 and 12 months, respectively. The rate of discontinuation was higher in low‐risk patients (82% versus 62.5% at 12 months for CHA2DS2‐VASc 0–1 versus ≥2, respectively; P<0.001). Stroke occurred in 1.4% of patients with CHA2DS2‐VASc ≥2 and 0.3% of those with CHA2DS2‐VASc 0 or 1 over the study follow‐up. The risk of cardioembolism in the first 3 months after ablation was increased among those with any time off OAC (hazard ratio 8.06 [95% CI 1.53–42.3], P<0.05). The risk of cardioembolism beyond 3 months was increased with OAC discontinuation among high‐risk patients (hazard ratio 2.48 [95% CI 1.11–5.52], P<0.05) but not low‐risk patients. Conclusion The overall risk of stroke in postablation patients is low; however, OAC discontinuation after ablation is common and is associated with increased risk of cardioembolism for all patients within the first 3 months and for high‐risk patients in the long term. Continuing OAC for at least 3 months in all patients and indefinitely in high‐risk patients appears to be the safest strategy.
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Affiliation(s)
- Peter A Noseworthy
- Heart Rhythm Section, Cardiovascular Diseases, Mayo Clinic, Rochester, MN (P.A.N., A.J.D., K.C.S., S.J.A., P.A.F., D.L.P., B.J.G.) Robert D. and Patricia E. Kern Center for Science of Health Care Delivery, Mayo Clinic, Rochester, MN (P.A.N., X.Y., H.V.H., L.R.S., N.D.S.)
| | - Xiaoxi Yao
- Robert D. and Patricia E. Kern Center for Science of Health Care Delivery, Mayo Clinic, Rochester, MN (P.A.N., X.Y., H.V.H., L.R.S., N.D.S.)
| | - Abhishek J Deshmukh
- Heart Rhythm Section, Cardiovascular Diseases, Mayo Clinic, Rochester, MN (P.A.N., A.J.D., K.C.S., S.J.A., P.A.F., D.L.P., B.J.G.)
| | - Holly Van Houten
- Robert D. and Patricia E. Kern Center for Science of Health Care Delivery, Mayo Clinic, Rochester, MN (P.A.N., X.Y., H.V.H., L.R.S., N.D.S.)
| | - Lindsey R Sangaralingham
- Robert D. and Patricia E. Kern Center for Science of Health Care Delivery, Mayo Clinic, Rochester, MN (P.A.N., X.Y., H.V.H., L.R.S., N.D.S.)
| | - Konstantinos C Siontis
- Heart Rhythm Section, Cardiovascular Diseases, Mayo Clinic, Rochester, MN (P.A.N., A.J.D., K.C.S., S.J.A., P.A.F., D.L.P., B.J.G.)
| | - Jonathan P Piccini
- Division of Cardiology, Duke University Medical Center, Durham, NC (J.P.P., B.J.G.)
| | - Samuel J Asirvatham
- Heart Rhythm Section, Cardiovascular Diseases, Mayo Clinic, Rochester, MN (P.A.N., A.J.D., K.C.S., S.J.A., P.A.F., D.L.P., B.J.G.)
| | - Paul A Friedman
- Heart Rhythm Section, Cardiovascular Diseases, Mayo Clinic, Rochester, MN (P.A.N., A.J.D., K.C.S., S.J.A., P.A.F., D.L.P., B.J.G.)
| | - Douglas L Packer
- Heart Rhythm Section, Cardiovascular Diseases, Mayo Clinic, Rochester, MN (P.A.N., A.J.D., K.C.S., S.J.A., P.A.F., D.L.P., B.J.G.)
| | - Bernard J Gersh
- Heart Rhythm Section, Cardiovascular Diseases, Mayo Clinic, Rochester, MN (P.A.N., A.J.D., K.C.S., S.J.A., P.A.F., D.L.P., B.J.G.) Division of Cardiology, Duke University Medical Center, Durham, NC (J.P.P., B.J.G.)
| | - Nilay D Shah
- Robert D. and Patricia E. Kern Center for Science of Health Care Delivery, Mayo Clinic, Rochester, MN (P.A.N., X.Y., H.V.H., L.R.S., N.D.S.) Optum Labs, Cambridge, MA (N.D.S.)
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305
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Oye KA, Jain G, Amador M, Arnaout R, Brown JS, Crown W, Ferguson J, Pezalla E, Rassen JA, Selker HP, Trusheim M, Hirsch G. The next frontier: Fostering innovation by improving health data access and utilization. Clin Pharmacol Ther 2015; 98:514-21. [PMID: 26234275 PMCID: PMC5052021 DOI: 10.1002/cpt.191] [Citation(s) in RCA: 23] [Impact Index Per Article: 2.6] [Reference Citation Analysis] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/20/2015] [Revised: 07/24/2015] [Accepted: 07/26/2015] [Indexed: 12/24/2022]
Affiliation(s)
- K A Oye
- Massachusetts Institute of Technology (MIT) Department of Political Science and Engineering Systems Division, Cambridge, Massachusetts, USA
| | - G Jain
- Center for Biomedical Innovation, MIT, Cambridge, Massachusetts, USA
| | - M Amador
- MIT Portugal Program, International Risk Governance Council Portugal, Portugal
| | - R Arnaout
- Department of Medicine, Beth Israel Deaconess Medical Center, Boston, Massachusetts, USA.,Department of Pathology, Harvard Medical School (HMS), Boston, Massachusetts, USA
| | - J S Brown
- Department of Population Medicine, Harvard Pilgrim Health Care Institute and HMS, Boston, Massachusetts, USA
| | - W Crown
- Optum Labs, Boston, Massachusetts, USA
| | | | - E Pezalla
- Aetna, Inc., Hartford, Connecticut, USA
| | | | - H P Selker
- Tufts Clinical and Translational Science Institute, Tufts University, and Institute for Clinical Research and Health Policy Studies, Tufts Medical Center, Boston, Massachusetts, USA
| | - M Trusheim
- Sloan School of Management, MIT, Cambridge, Massachusetts, USA
| | - G Hirsch
- Center for Biomedical Innovation, MIT, Cambridge, Massachusetts, USA
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Allison DB, Bassaganya-Riera J, Burlingame B, Brown AW, le Coutre J, Dickson SL, van Eden W, Garssen J, Hontecillas R, Khoo CSH, Knorr D, Kussmann M, Magistretti PJ, Mehta T, Meule A, Rychlik M, Vögele C. Goals in Nutrition Science 2015-2020. Front Nutr 2015; 2:26. [PMID: 26442272 PMCID: PMC4563164 DOI: 10.3389/fnut.2015.00026] [Citation(s) in RCA: 25] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/26/2015] [Accepted: 08/14/2015] [Indexed: 12/12/2022] Open
Affiliation(s)
- David B Allison
- Office of Energetics and Nutrition Obesity Research Center, School of Public Health, University of Alabama at Birmingham , Birmingham, AL , USA ; Section on Statistical Genetics, University of Alabama at Birmingham , Birmingham, AL , USA ; Department of Nutrition Sciences, University of Alabama at Birmingham , Birmingham, AL , USA ; Department of Biostatistics, University of Alabama at Birmingham , Birmingham, AL , USA
| | - Josep Bassaganya-Riera
- Nutritional Immunology and Molecular Medicine Laboratory, Virginia Bioinformatics Institute, Virginia Tech , Blacksburg, VA , USA
| | - Barbara Burlingame
- Deakin University , Melbourne, VIC , Australia ; American University of Rome , Rome , Italy
| | - Andrew W Brown
- Office of Energetics and Nutrition Obesity Research Center, School of Public Health, University of Alabama at Birmingham , Birmingham, AL , USA
| | - Johannes le Coutre
- Nestlé Research Center , Lausanne , Switzerland ; Organization for Interdisciplinary Research Projects, The University of Tokyo , Tokyo , Japan ; École Polytechnique Fédérale de Lausanne , Lausanne , Switzerland
| | - Suzanne L Dickson
- Institute of Neuroscience and Physiology, The Sahlgrenska Academy at the University of Gothenburg , Gothenburg , Sweden
| | - Willem van Eden
- Department of Infectious Diseases and Immunology, Faculty of Veterinary Medicine, Utrecht University , Utrecht , Netherlands
| | - Johan Garssen
- Faculty of Science, Utrecht Institute for Pharmaceutical Sciences, Utrecht University , Utrecht , Netherlands
| | - Raquel Hontecillas
- Nutritional Immunology and Molecular Medicine Laboratory, Virginia Bioinformatics Institute, Virginia Tech , Blacksburg, VA , USA
| | - Chor San H Khoo
- North American Branch of International Life Sciences Institute , Washington, DC , USA
| | | | - Martin Kussmann
- École Polytechnique Fédérale de Lausanne , Lausanne , Switzerland ; Nestlé Institute of Health Sciences SA , Lausanne , Switzerland
| | - Pierre J Magistretti
- Division of Biological and Environmental Sciences and Engineering, King Abdullah University of Science and Technology , Thuwal , Saudi Arabia ; Laboratory of Neuroenergetics and Cellular Dynamics, Brain Mind Institute, École Polytechnique Fédérale de Lausanne , Lausanne , Switzerland
| | - Tapan Mehta
- Department of Health Services Administration, Nutrition Obesity Research Center, University of Alabama at Birmingham , Birmingham, AL , USA
| | - Adrian Meule
- Department of Psychology, University of Salzburg , Salzburg , Austria
| | - Michael Rychlik
- Analytical Food Chemistry, Technische Universität München , Freising , Germany
| | - Claus Vögele
- Research Unit INSIDE, Institute for Health and Behaviour, University of Luxembourg , Luxembourg , Luxembourg
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307
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Grande D, Asch DA, Wan F, Bradbury AR, Jagsi R, Mitra N. Are Patients With Cancer Less Willing to Share Their Health Information? Privacy, Sensitivity, and Social Purpose. J Oncol Pract 2015; 11:378-83. [PMID: 26265174 PMCID: PMC4575401 DOI: 10.1200/jop.2015.004820] [Citation(s) in RCA: 20] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/20/2022] Open
Abstract
PURPOSE Growing use of electronic health information increases opportunities to build population cancer databases for research and care delivery. Understanding patient views on reuse of health information is essential to shape privacy policies and build trust in these initiatives. METHODS We randomly assigned nationally representative participants (N = 3,336) with and without prior cancer to six of 18 scenarios describing different uses of electronic health information. The scenarios varied the user, use, and sensitivity of the information. Participants rated each scenario on a scale of 1 to 10 assessing their willingness to share their electronic health information. We used conjoint analysis to measure the relative importance of each attribute (ie, use, user, and sensitivity). RESULTS Participants with and without a prior diagnosis of cancer had a similar willingness to share health information (0.27; P = .42). Both cancer and noncancer participants rated the purpose of information use as the most important factor (importance weights, 67.1% and 45.6%, respectively). For cancer participants, the sensitivity of the information was more important (importance weights, 29.8% v 1.2%). However, cancer participants were more willing to share their health information when the information included more sensitive genetic information (0.48; P = .015). Cancer and noncancer respondents rated uses and users similarly. CONCLUSION The information sharing preferences of participants with and without a prior diagnosis of cancer were driven mainly by the purpose of information reuse. Although conventional thinking suggests patients with cancer might be less willing to share their health information, we found participants with cancer were more willing to share their inherited genetic information.
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Affiliation(s)
- David Grande
- University of Pennsylvania Perelman School of Medicine; Leonard Davis Institute of Health Economics, University of Pennsylvania; Center for Health Equity Research and Promotion, Philadelphia Veterans Affairs Medical Center, Philadelphia, PA; and University of Michigan School of Medicine, Ann Arbor, MI
| | - David A Asch
- University of Pennsylvania Perelman School of Medicine; Leonard Davis Institute of Health Economics, University of Pennsylvania; Center for Health Equity Research and Promotion, Philadelphia Veterans Affairs Medical Center, Philadelphia, PA; and University of Michigan School of Medicine, Ann Arbor, MI
| | - Fei Wan
- University of Pennsylvania Perelman School of Medicine; Leonard Davis Institute of Health Economics, University of Pennsylvania; Center for Health Equity Research and Promotion, Philadelphia Veterans Affairs Medical Center, Philadelphia, PA; and University of Michigan School of Medicine, Ann Arbor, MI
| | - Angela R Bradbury
- University of Pennsylvania Perelman School of Medicine; Leonard Davis Institute of Health Economics, University of Pennsylvania; Center for Health Equity Research and Promotion, Philadelphia Veterans Affairs Medical Center, Philadelphia, PA; and University of Michigan School of Medicine, Ann Arbor, MI
| | - Reshma Jagsi
- University of Pennsylvania Perelman School of Medicine; Leonard Davis Institute of Health Economics, University of Pennsylvania; Center for Health Equity Research and Promotion, Philadelphia Veterans Affairs Medical Center, Philadelphia, PA; and University of Michigan School of Medicine, Ann Arbor, MI
| | - Nandita Mitra
- University of Pennsylvania Perelman School of Medicine; Leonard Davis Institute of Health Economics, University of Pennsylvania; Center for Health Equity Research and Promotion, Philadelphia Veterans Affairs Medical Center, Philadelphia, PA; and University of Michigan School of Medicine, Ann Arbor, MI
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Noseworthy PA, Kapa S, Haas LR, Van Houten H, Deshmuk AJ, Mulpuru SK, McLeod CJ, Asirvatham SJ, Friedman PA, Shah ND, Packer DL. Trends and predictors of readmission after catheter ablation for atrial fibrillation, 2009-2013. Am Heart J 2015; 170:483-9. [PMID: 26385031 DOI: 10.1016/j.ahj.2015.05.018] [Citation(s) in RCA: 19] [Impact Index Per Article: 2.1] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/13/2015] [Accepted: 05/30/2015] [Indexed: 01/15/2023]
Abstract
UNLABELLED As the number of patients undergoing catheter ablation for atrial fibrillation (AF) increases, there is a growing focus on optimizing the quality and efficiency of. Readmission is often considered an indicator of both quality and efficiency of care delivery. We sought to estimate rates and identify predictors of readmission after catheter ablation. METHODS AND RESULTS Using a large, national administrative claims database, we identified all AF patients who underwent catheter ablation between January 2009 and December 2013 (10,705 ablation cases). We examined incident readmission and the primary diagnosis during the readmission episode of care. We used Cox proportional hazard models to identify associations between readmission and patient and institutional characteristics. A total of 1,433 (13.4%) ablation patients were readmitted within 90 days of ablation for any cause, and 573 (5.4%) were admitted with AF as the primary diagnosis. There was a decline in all-cause (from 15.6% to 12.8%; P = .04) and AF-related (6.4%-5.0 %; P = .03) 90-day readmission over the study period. In a multivariate model, earlier year of ablation and each of 9 chronic conditions (alone or in combination) were independently associated with risk of readmission. CONCLUSIONS Between 2009 and 2013, there was a reduction in 90-day readmission rates after AF ablation, suggesting improved periprocedural care of these patients. Identifying patients at high risk for readmission after catheter ablation for AF may offer an opportunity for early intervention and, ultimately, reduction in procedural morbidity and medical costs.
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Noseworthy PA, Van Houten HK, Sangaralingham LR, Deshmukh AJ, Kapa S, Mulpuru SK, McLeod CJ, Asirvatham SJ, Friedman PA, Shah ND, Packer DL. Effect of Antiarrhythmic Drug Initiation on Readmission After Catheter Ablation for Atrial Fibrillation. JACC Clin Electrophysiol 2015; 1:238-244. [PMID: 29759312 DOI: 10.1016/j.jacep.2015.04.016] [Citation(s) in RCA: 14] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/17/2015] [Revised: 04/01/2015] [Accepted: 04/23/2015] [Indexed: 11/29/2022]
Abstract
OBJECTIVES This study sought to evaluate the impact on antiarrhythmic drug (AAD) initiation on the risk of readmission after catheter ablation for atrial fibrillation (AF) among patients not already treated with an AAD. BACKGROUND Hospital readmission, a commonly tracked indicator of quality and efficiency of care delivery, occurs in about 15% patients within 90 days of undergoing catheter ablation for AF. METHODS Using a large national administrative claims database, we identified all atrial fibrillation patients (≥18 years of age) who underwent catheter ablation between January 2005 and December 2013 (n = 7,442). We identified the subset of patients who had not been on an AAD in the 90 days before ablation (n = 2,542) and, among those, the patients in whom an AAD was initiated at discharge following the ablation (n = 519). RESULTS The readmission rate was significantly lower among patients who were initiated on an AAD compared with those who were not (11.6% vs. 16.2%, p = 0.009). The association persisted after adjustment for age, sex, Charlson index, and CHADS2 score (hazard ratio [HR]: 0.73, 95% confidence interval [CI]: 0.56 to 0.97; p = 0.03). In unadjusted time to event analysis, amiodarone (HR: 0.55, 95% CI: 0.32 to 0.94; p = 0.039) was associated with the greatest reduction in readmission whereas dronedarone, Class II agents, and Class IC agents had no statistically significant effect on readmission. AADs were discontinued in 44.5% of patients at 3 months. CONCLUSIONS Initiation of an AAD at discharge of catheter ablation is associated with a significant reduction in readmission within 90 days. Routine initiation of an AAD after catheter ablation may reduce healthcare utilization in the periablation period; however, the high rate of medication discontinuation may suggest that side effects or inefficacy may limit long-term AAD use post-ablation.
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Affiliation(s)
- Peter A Noseworthy
- Heart Rhythm Section, Cardiovascular Diseases, Mayo Clinic, Rochester, Minnesota; Robert D. and Patricia E. Kern Center for Science of Health Care Delivery, Mayo Clinic, Rochester, Minnesota.
| | - Holly K Van Houten
- Robert D. and Patricia E. Kern Center for Science of Health Care Delivery, Mayo Clinic, Rochester, Minnesota
| | - Lindsey R Sangaralingham
- Robert D. and Patricia E. Kern Center for Science of Health Care Delivery, Mayo Clinic, Rochester, Minnesota
| | - Abhishek J Deshmukh
- Heart Rhythm Section, Cardiovascular Diseases, Mayo Clinic, Rochester, Minnesota
| | - Suraj Kapa
- Heart Rhythm Section, Cardiovascular Diseases, Mayo Clinic, Rochester, Minnesota
| | - Siva K Mulpuru
- Heart Rhythm Section, Cardiovascular Diseases, Mayo Clinic, Rochester, Minnesota
| | - Christopher J McLeod
- Heart Rhythm Section, Cardiovascular Diseases, Mayo Clinic, Rochester, Minnesota
| | - Samuel J Asirvatham
- Heart Rhythm Section, Cardiovascular Diseases, Mayo Clinic, Rochester, Minnesota
| | - Paul A Friedman
- Heart Rhythm Section, Cardiovascular Diseases, Mayo Clinic, Rochester, Minnesota
| | - Nilay D Shah
- Robert D. and Patricia E. Kern Center for Science of Health Care Delivery, Mayo Clinic, Rochester, Minnesota; Optum Labs, Cambridge, Massachusetts
| | - Douglas L Packer
- Heart Rhythm Section, Cardiovascular Diseases, Mayo Clinic, Rochester, Minnesota
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Dunlay SM, Haas LR, Herrin J, Schilz SR, Stulak JM, Kushwaha SS, Shah ND. Use of Post-acute Care Services and Readmissions After Left Ventricular Assist Device Implantation in Privately Insured Patients. J Card Fail 2015; 21:816-23. [PMID: 26093335 DOI: 10.1016/j.cardfail.2015.06.012] [Citation(s) in RCA: 23] [Impact Index Per Article: 2.6] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/12/2015] [Revised: 05/04/2015] [Accepted: 06/12/2015] [Indexed: 11/19/2022]
Abstract
BACKGROUND Very little is known about health care resource utilization, including post-acute care use and hospital readmissions, after left ventricular assist device (LVAD) implantation. METHODS AND RESULTS Administrative claims from a database of multiple United States health plans were used to identify patients that received an LVAD (ICD-9 code 37.66) and survived to hospital discharge from January 1-2006, through September 30-2013. Post-acute care use was defined as a skilled nursing facility or rehabilitation stay within 90 days after hospital discharge. Patients were censored at heart transplantation or end of coverage through December 31-2013. Of 583 patients (mean age 55 years, 77% male), 223 (38.3%) used post-acute care services, more commonly in patients with diabetes, who required hemodialysis, and who had LVADs implanted at hospitals in more populated areas, with more beds, and in the northeast region (P < .05 for each). The most common reasons for readmission were device complications, heart failure, and arrhythmia. Readmission risk was higher in patients who had diabetes, peripheral vascular disease, and longer hospital length of stay, but it did not differ by post-acute care use. CONCLUSIONS Use of post-acute care services varies based on hospital characteristics. We found no association between post-acute care use and readmission risk after LVAD implantation.
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Affiliation(s)
- Shannon M Dunlay
- Division of Cardiovascular Diseases, Department of Internal Medicine, Mayo Clinic, Rochester, Minnesota; Division of Health Care Policy and Research, Department of Health Sciences Research, Mayo Clinic, Rochester, Minnesota.
| | - Lindsey R Haas
- Division of Health Care Policy and Research, Department of Health Sciences Research, Mayo Clinic, Rochester, Minnesota
| | - Jeph Herrin
- Yale University School of Medicine, New Haven, Connecticut; Health Research and Educational Trust, Chicago, Illinois
| | - Stephanie R Schilz
- Division of Health Care Policy and Research, Department of Health Sciences Research, Mayo Clinic, Rochester, Minnesota
| | - John M Stulak
- Division of Cardiovascular Surgery, Department of Surgery, Mayo Clinic, Rochester, Minnesota
| | - Sudhir S Kushwaha
- Division of Cardiovascular Diseases, Department of Internal Medicine, Mayo Clinic, Rochester, Minnesota
| | - Nilay D Shah
- Division of Health Care Policy and Research, Department of Health Sciences Research, Mayo Clinic, Rochester, Minnesota
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311
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Abraham NS, Singh S, Alexander GC, Heien H, Haas LR, Crown W, Shah ND. Comparative risk of gastrointestinal bleeding with dabigatran, rivaroxaban, and warfarin: population based cohort study. BMJ 2015; 350:h1857. [PMID: 25910928 PMCID: PMC4413863 DOI: 10.1136/bmj.h1857] [Citation(s) in RCA: 258] [Impact Index Per Article: 28.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 12/18/2022]
Abstract
OBJECTIVE To determine the real world risk of gastrointestinal bleeding associated with the use of the novel oral anticoagulants dabigatran and rivaroxaban compared with warfarin. DESIGN Retrospective, propensity matched cohort study. SETTING Optum Labs Data Warehouse, a large database including administrative claims data on privately insured and Medicare Advantage enrollees. PARTICIPANTS New users of dabigatran, rivaroxaban, and warfarin from 1 November 2010 to 30 September 2013. MAIN OUTCOME MEASURES Incidence rates (events/100 patient years) and propensity score matched Cox proportional hazards models were used to estimate rates of total gastrointestinal bleeds, upper gastrointestinal bleeds, and lower gastrointestinal bleeds for the novel oral anticoagulants compared with warfarin in patients with and without atrial fibrillation. Heterogeneity of treatment effect related to age was examined using a marginal effects model. RESULTS The incidence of gastrointestinal bleeding associated with dabigatran was 2.29 (95% confidence interval 1.88 to 2.79) per 100 patient years and that associated with warfarin was 2.87 (2.41 to 3.41) per 100 patient years in patients with atrial fibrillation. In non-atrial fibrillation patients, the incidence of gastrointestinal bleeding was 4.10 (2.47 to 6.80) per 100 patient years with dabigatran and 3.71 (2.16 to 6.40) per 100 patient years with warfarin. With rivaroxaban, 2.84 (2.30 to 3.52) gastrointestinal bleeding events per 100 patient years occurred in atrial fibrillation patients (warfarin 3.06 (2.49 to 3.77)/100 patient years) and 1.66 (1.23 to 2.24) per 100 patient years in non-atrial fibrillation patients (warfarin 1.57 (1.25 to 1.99)/100 patient years). In propensity score matched models, the risk of gastrointestinal bleeding with novel oral anticoagulants was similar to that with warfarin in atrial fibrillation patients (dabigatran v warfarin, hazard ratio 0.79 (0.61 to 1.03); rivaroxaban v warfarin, 0.93 (0.69 to 1.25)) and in non-AF patients (dabigatran v warfarin, hazard ratio 1.14 (0.54 to 2.39); rivaroxaban v warfarin, 0.89 (0.60 to 1.32)). The risk of gastrointestinal bleeding increased after age 65, such that by age 76 the risk exceeded that with warfarin among atrial fibrillation patients taking dabigatran (hazard ratio 2.49 (1.61 to 3.83)) and patients with and without atrial fibrillation taking rivaroxaban (2.91 (1.65 to 4.81) and 4.58 (2.40 to 8.72), respectively). CONCLUSIONS The risk of gastrointestinal bleeding related to novel oral anticoagulants was similar to that for warfarin. Caution should be used when prescribing novel oral anticoagulants to older people, particularly those over 75 years of age.
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Affiliation(s)
- Neena S Abraham
- Division of Gastroenterology and Hepatology, Department of Medicine, Mayo Clinic, 13400 East Shea Boulevard, Scottsdale, AZ, 85259, USA Division of Health Care Policy and Research, Department of Health Services Research, Mayo Clinic, Rochester, MN, USA Mayo Clinic Robert D and Patricia E Kern Center for the Science of Health Care Delivery, Rochester, MN, USA
| | - Sonal Singh
- Division of General Internal Medicine, Johns Hopkins School of Medicine, Baltimore, MD, USA Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health and Center for Drug Safety and Effectiveness, Baltimore, MD, USA
| | - G Caleb Alexander
- Division of General Internal Medicine, Johns Hopkins School of Medicine, Baltimore, MD, USA Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health and Center for Drug Safety and Effectiveness, Baltimore, MD, USA
| | - Herbert Heien
- Mayo Clinic Robert D and Patricia E Kern Center for the Science of Health Care Delivery, Rochester, MN, USA
| | - Lindsey R Haas
- Mayo Clinic Robert D and Patricia E Kern Center for the Science of Health Care Delivery, Rochester, MN, USA
| | | | - Nilay D Shah
- Division of Health Care Policy and Research, Department of Health Services Research, Mayo Clinic, Rochester, MN, USA Mayo Clinic Robert D and Patricia E Kern Center for the Science of Health Care Delivery, Rochester, MN, USA
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312
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Marshall DA, Burgos-Liz L, IJzerman MJ, Crown W, Padula WV, Wong PK, Pasupathy KS, Higashi MK, Osgood ND. Selecting a dynamic simulation modeling method for health care delivery research-part 2: report of the ISPOR Dynamic Simulation Modeling Emerging Good Practices Task Force. VALUE IN HEALTH : THE JOURNAL OF THE INTERNATIONAL SOCIETY FOR PHARMACOECONOMICS AND OUTCOMES RESEARCH 2015; 18:147-60. [PMID: 25773550 DOI: 10.1016/j.jval.2015.01.006] [Citation(s) in RCA: 73] [Impact Index Per Article: 8.1] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/28/2023]
Abstract
In a previous report, the ISPOR Task Force on Dynamic Simulation Modeling Applications in Health Care Delivery Research Emerging Good Practices introduced the fundamentals of dynamic simulation modeling and identified the types of health care delivery problems for which dynamic simulation modeling can be used more effectively than other modeling methods. The hierarchical relationship between the health care delivery system, providers, patients, and other stakeholders exhibits a level of complexity that ought to be captured using dynamic simulation modeling methods. As a tool to help researchers decide whether dynamic simulation modeling is an appropriate method for modeling the effects of an intervention on a health care system, we presented the System, Interactions, Multilevel, Understanding, Loops, Agents, Time, Emergence (SIMULATE) checklist consisting of eight elements. This report builds on the previous work, systematically comparing each of the three most commonly used dynamic simulation modeling methods-system dynamics, discrete-event simulation, and agent-based modeling. We review criteria for selecting the most suitable method depending on 1) the purpose-type of problem and research questions being investigated, 2) the object-scope of the model, and 3) the method to model the object to achieve the purpose. Finally, we provide guidance for emerging good practices for dynamic simulation modeling in the health sector, covering all aspects, from the engagement of decision makers in the model design through model maintenance and upkeep. We conclude by providing some recommendations about the application of these methods to add value to informed decision making, with an emphasis on stakeholder engagement, starting with the problem definition. Finally, we identify areas in which further methodological development will likely occur given the growing "volume, velocity and variety" and availability of "big data" to provide empirical evidence and techniques such as machine learning for parameter estimation in dynamic simulation models. Upon reviewing this report in addition to using the SIMULATE checklist, the readers should be able to identify whether dynamic simulation modeling methods are appropriate to address the problem at hand and to recognize the differences of these methods from those of other, more traditional modeling approaches such as Markov models and decision trees. This report provides an overview of these modeling methods and examples of health care system problems in which such methods have been useful. The primary aim of the report was to aid decisions as to whether these simulation methods are appropriate to address specific health systems problems. The report directs readers to other resources for further education on these individual modeling methods for system interventions in the emerging field of health care delivery science and implementation.
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Affiliation(s)
- Deborah A Marshall
- Health Services & Systems Research, Department of Community Health Sciences, Cumming School of Medicine, University of Calgary, Calgary, AB, Canada.
| | - Lina Burgos-Liz
- Cumming School of Medicine, University of Calgary, Calgary, AB, Canada
| | - Maarten J IJzerman
- Department of Health Technology & Services Research, University of Twente, Enschede, The Netherlands
| | | | - William V Padula
- Section of Hospital Medicine, University of Chicago, Chicago, IL, USA
| | - Peter K Wong
- HSHS Illinois Divisions and Medical Group, Hospital Sisters Health System, Belleville, IL, USA
| | - Kalyan S Pasupathy
- Health Care Systems Engineering Program, Mayo Clinic Robert D. and Patricia E. Kern Center for the Science of Health Care Delivery, Rochester, MN, USA
| | | | - Nathaniel D Osgood
- Department of Computer Science, University of Saskatchewan, Saskatoon, Saskatchewan, Canada; Department of Community Health & Epidemiology and Bioengineering Division, Saskatoon, Saskatchewan, Canada
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Crown WH. Potential application of machine learning in health outcomes research and some statistical cautions. VALUE IN HEALTH : THE JOURNAL OF THE INTERNATIONAL SOCIETY FOR PHARMACOECONOMICS AND OUTCOMES RESEARCH 2015; 18:137-140. [PMID: 25773546 DOI: 10.1016/j.jval.2014.12.005] [Citation(s) in RCA: 55] [Impact Index Per Article: 6.1] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/11/2014] [Accepted: 12/12/2014] [Indexed: 06/04/2023]
Abstract
Traditional analytic methods are often ill-suited to the evolving world of health care big data characterized by massive volume, complexity, and velocity. In particular, methods are needed that can estimate models efficiently using very large datasets containing healthcare utilization data, clinical data, data from personal devices, and many other sources. Although very large, such datasets can also be quite sparse (e.g., device data may only be available for a small subset of individuals), which creates problems for traditional regression models. Many machine learning methods address such limitations effectively but are still subject to the usual sources of bias that commonly arise in observational studies. Researchers using machine learning methods such as lasso or ridge regression should assess these models using conventional specification tests.
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314
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Noseworthy PA, Kapa S, Deshmukh AJ, Madhavan M, Van Houten H, Haas LR, Mulpuru SK, McLeod CJ, Asirvatham SJ, Friedman PA, Shah ND, Packer DL. Risk of stroke after catheter ablation versus cardioversion for atrial fibrillation: A propensity-matched study of 24,244 patients. Heart Rhythm 2015; 12:1154-61. [PMID: 25708883 DOI: 10.1016/j.hrthm.2015.02.020] [Citation(s) in RCA: 41] [Impact Index Per Article: 4.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/13/2014] [Indexed: 10/24/2022]
Abstract
BACKGROUND Stroke is the major cause of morbidity and mortality related to atrial fibrillation (AF). Catheter ablation for AF is effective in reducing AF burden, but its impact on long-term stroke risk is unknown. OBJECTIVE We sought to evaluate the periprocedural and long-term stroke risk after catheter ablation or cardioversion for AF. METHODS This retrospective, propensity-matched study using a national administrative claims database identified patients with AF who underwent catheter ablation and a comparison group (matched on age, sex, year of treatment, CHA2DS2-Vasc score, and Charlson index) who underwent cardioversion between 2005 and 2012. The primary end points were (1) time to first ischemic or hemorrhagic stroke or transient ischemic attack (TIA) and (2) time to first ischemic or hemorrhagic stroke excluding TIA. We compared periprocedural incident stroke (within 30 days of ablation or cardioversion) as well as total strokes between the 2 groups. RESULTS A total of 24,244 patients (12,122 patients undergoing ablation and 12,122 patients undergoing cardioversion) were included in the analysis. Incident periprocedural stroke or TIA occurred in 0.5% of the ablation group and 0.3% of the cardioversion group (P = .04). There was a significant initial risk of stroke/TIA with ablation within the first 30 days (rate ratio 1.53; P = .05). After 30 days, this risk was significantly lower in the ablation group (rate ratio 0.78; P = .03). CONCLUSION In patients with AF, there is a small periprocedural stroke risk with ablation in comparison to cardioversion. However, over longer-term follow-up, ablation is associated with a slightly lower rate of stroke.
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Affiliation(s)
- Peter A Noseworthy
- Heart Rhythm Section, Cardiovascular Diseases, Mayo Clinic, Rochester, Minnesota; Robert D. and Patricia E. Kern Center for Science of Health Care Delivery, Mayo Clinic, Rochester, Minnesota.
| | - Suraj Kapa
- Heart Rhythm Section, Cardiovascular Diseases, Mayo Clinic, Rochester, Minnesota
| | - Abhishek J Deshmukh
- Heart Rhythm Section, Cardiovascular Diseases, Mayo Clinic, Rochester, Minnesota
| | - Malini Madhavan
- Heart Rhythm Section, Cardiovascular Diseases, Mayo Clinic, Rochester, Minnesota
| | - Holly Van Houten
- Robert D. and Patricia E. Kern Center for Science of Health Care Delivery, Mayo Clinic, Rochester, Minnesota
| | - Lindsey R Haas
- Robert D. and Patricia E. Kern Center for Science of Health Care Delivery, Mayo Clinic, Rochester, Minnesota
| | - Siva K Mulpuru
- Heart Rhythm Section, Cardiovascular Diseases, Mayo Clinic, Rochester, Minnesota
| | - Christopher J McLeod
- Heart Rhythm Section, Cardiovascular Diseases, Mayo Clinic, Rochester, Minnesota
| | - Samuel J Asirvatham
- Heart Rhythm Section, Cardiovascular Diseases, Mayo Clinic, Rochester, Minnesota
| | - Paul A Friedman
- Heart Rhythm Section, Cardiovascular Diseases, Mayo Clinic, Rochester, Minnesota
| | - Nilay D Shah
- Robert D. and Patricia E. Kern Center for Science of Health Care Delivery, Mayo Clinic, Rochester, Minnesota; Optum Labs, Cambridge, Massachusetts
| | - Douglas L Packer
- Heart Rhythm Section, Cardiovascular Diseases, Mayo Clinic, Rochester, Minnesota
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315
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Use of the Optum Labs Data Warehouse to assess test ordering patterns for diagnosis of Helicobacter pylori infection in the United States. J Clin Microbiol 2015; 53:1358-60. [PMID: 25609721 DOI: 10.1128/jcm.03464-14] [Citation(s) in RCA: 11] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/30/2022] Open
Abstract
We surveyed national Helicobacter pylori diagnostic testing practices and diagnoses using commercial and Medicare medical claims data from Optum Labs (Cambridge, MA). Serologic testing for antibodies to H. pylori remains the most commonly ordered diagnostic test despite recent expert recommendations. Changes in reimbursement for serologic testing will likely drive future provider ordering practices.
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