Outpatient surgeries in the United States account for roughly 7% of annual healthcare expenditures. To exploit substantial opportunities to improve the value of outpatient surgical care, the authors composed an evidence-based care delivery composite for national discussion and pilot testing.
Objectives: Nearly 57 million outpatient surgeries—invasive procedures performed on an outpatient basis in hospital outpatient departments (HOPDs) or ambulatory surgery centers (ASCs)—produced annually in the United States account for roughly 7% of healthcare expenditures. Although moving inpatient surgeries to outpatient settings has lowered the cost of care, substantial opportunities to improve the value of outpatient surgery remain. To exploit these remaining opportunities, we composed an evidence-based care delivery composite for national discussion and pilot testing.
Study Design: Evidence-based care delivery composite.
Methods: We synthesized peer-reviewed publications describing efforts to improve the value of outpatient surgical care, interviewed patients and clinicians to understand their most deeply felt discontents, reviewed potentially relevant emerging science and technology, and observed surgeries at healthcare organizations nominated by researchers as exemplars of efficiency and effectiveness. Primed by this information, we iterated potential new designs utilizing criticism from practicing clinicians, health services researchers, and healthcare managers.
Results: We found that 3 opportunities are most likely to improve value: 1) maximizing the appropriate use of surgeries via decision aids, clinical decision support, and a remote surgical coach for physicians considering a surgical referral; 2) safely shifting surgeries from HOPDs to high-volume, multi-specialty ASCs where costs are much lower; and 3) standardizing processes in ASCs from referral to recovery.
Conclusions: Extrapolation based on published studies of the effects of each component suggests that the proposed 3-part composite may lower annual national outpatient surgical spending by as much as one-fifth, while maintaining or improving outcomes and the care experience for patients and clinicians. Pilot testing and evaluation will allow refinement of this composite.
Am J Manag Care. 2016;22(9):e329-e335
Outpatient surgeries—surgical and nonsurgical invasive procedures performed on an outpatient basis in hospital outpatient departments (HOPDs) or freestanding ambulatory surgery centers (ASCs)—are a fast-growing segment of healthcare,1-4 fueled by improved pain management, less invasive surgical techniques, patient convenience, and lower cost.5 However, its growth also carries risks, such as more pain and longer recovery times than patients expect,6 unplanned subsequent hospital admissions,7 and overuse.8
To help US clinicians and healthcare organizations respond constructively to rising incentives to improve value, we used a method adapted from biomedical technology innovation to design an innovative care delivery “composite” offering the greatest potential to improve value to US patients and their healthcare sponsors.9
A year-long, 3-person team of postdoctoral clinicians and management scientists, supported by senior mentors from clinical practice, health services research, and healthcare management, was recruited via a national search to create the new care composite. The team conducted site visits to understand costs, quality, and patient experience at 3 institutions, all nominated by health services researchers to reflect today’s high-value “frontier” in the United States and globally. During these visits, the team compared care delivery methods for a single surgical procedure and created detailed process maps. They also observed care more broadly at several additional sites selected via “convenience” samples (eg, based on established relationships between the authors and the administrators of those facilities) to represent mainstream care. At all sites, the team sought to elicit the most deeply felt unmet needs of patients, family members, and clinicians; they intended the site selection to be as inclusive as they could design by a mix of “frontier” and “convenience” samples. In addition, the team did not rely on observations directly unless these observations were also supported by literature and/or approved by experts in the area. Yet, the team acknowledges that there is always the possibility that different site selection might have influenced the model construction.
The team conducted a literature review of efforts to improve the quality, patient experience, and total cost of outpatient surgical care. Via a series of seminars with individuals regarded as global or national leaders in their field, the team considered the applicability of relevant emerging science and technologies. Using these diverse exploration methods, the team discerned several correctable major shortfalls in value (Figure 1).
Over the next 6 months, the team iterated a proposed innovative care composite to correct these shortfalls, with the goal of identifying opportunities that are most likely to improve value. Diverse senior mentors continuously challenged or encouraged the team’s design10 and its national impact projections. This process expanded the team’s consideration of the “adjacent possible”9—innovations used for other medical conditions, such as medical and surgical homes, and by other industries, such as an automated check-in process for surgery that is similar to airline passenger check-ins and screenings. After 6 months of continuous refinement, the team converged on a composite new “care model,” along with an estimate of its likely impact on annual US health spending after accounting for implementation and operating costs (eAppendix, available at www.ajmc.com).
The resulting 3-component composite is displayed in the Table and is summarized by the words REFINE, RE-SET, and REPLICATE, or the “Triple-R” in short. The Table also displays evidence pertaining to the quantitative impact of each component. The next section summarizes rough estimates of the impact on the annual national outpatient surgical spending from combining all 3 components after 5 foundation-building years of implementation, learning, refinement, and competency-building. These estimates are speculative since the proposed combination of elements and their national scaling are unprecedented.
REFINE: Maximize Appropriate Use of Outpatient Surgeries
Approximately 30%11-15 of all elective surgeries may be inappropriate, which is defined as surgeries in which the expected health benefits offer no clear advantage over less risky alternatives.16 Perverse financial incentives may contribute to inappropriate use,8,17,18 as can poor alignment between a patient’s overall condition, goals of care, and desired outcomes.11,12,19-21 Referring providers—generally primary care providers—often lack adequate time and support to assure better alignment.22,23 In addition, effective communication to patients of likely benefits and risks occurs in only 20% of cases,24 often resulting in unrealistic patient expectations.25 Addressing the appropriateness of a surgical referral in primary care is one way to avert surgical overuse. We discerned several combinable solutions intended to be implemented by primary care providers prior to surgical referral.
Interactive patient decision aids. These reduce surgical use for conditions associated with multiple clinically appropriate treatment options by as much as 20% and improve patient satisfaction,26 yet only 10% to 30% of eligible patients receive them.27 Roughly 500 ready-to-implement and validated decision aids are available for most high-volume outpatient surgeries, such as cataract, cholecystectomy, hernia, and spine surgeries.28
Clinical decision support. Within an electronic health record, clinical decision support can help clinicians apply guidelines, thus increasing the appropriateness of surgeries that clinicians recommend.29,30 For example, when 120 procedures at risk for overuse, identified by the Choosing Wisely31 campaign, were translated into clinical decision support tools by Cedars-Sinai Medical Center, utilization decreased by as much as 18%.32 Clinical decision support tools may reduce complications33 and increase patient satisfaction. Automated clinical decision support tools can facilitate awareness of Appropriate Use Criteria34 and are more effective when endorsed via consensus among an organization’s clinicians.35
Case coaching. Patient decision aids and clinical decision support are insufficient to delineate an appropriate decision in approximately 8% of cases.36 In such instances, referring providers could be supported by a remotely located surgeon who does not benefit financially from the referral to serve as a “case coach” to verify the adequacy and appropriateness of the proposed program of care. For example, an e-consult service adopted by a number of integrated systems, such as the Veterans Health Administration, have decreased subsequent referrals for specialist care by 20% to 40%.36-39
We estimate potential net national reduction in annual US health spending from successful implementation of REFINE at $7.4 billion, or 3.5% of total annual spending on outpatient surgeries.
RE-SET: Safely Shift More Surgeries to Ambulatory Surgery Centers
Site-shifting. Despite similar outcomes, the same surgeries performed on low-risk patients in HOPDs cost much more to produce than in ASCs.8 Today, over half of US outpatient surgeries take place in HOPDs.40 This ratio can be safely changed by shifting a large number of surgeries from HOPDs to ASCs, as already occurs in other medically advanced nations.41 Based on the payment differential between sites,42 the Washington Ambulatory Surgery Center Association estimated that CMS could save $25 billion over a 10-year period with such a shift.40 We predict there may be additional savings due to differences in procedure and recovery duration.8,43
Expanded ASC hours. Expanding ASC operating room hours to 18 hours a day, 7 days a week would substantially boost throughput in multi-specialty ASCs. Human factors research suggests that such a shift could be safely implemented. Expansion of hours has been tested in other labor- and process-driven industries, such as aviation,9 and in healthcare settings in wealthy and poor countries. Narayana Health in India produces coronary artery bypass graft surgery with low mortality rates for less than $2000,44 in part, by spreading fixed costs over a larger patient base by expanded hours of operation.45 Similar cost reductions can be achieved in the United States.46 Because cognitive function and performance diminishes between the hours of midnight and 6 AM,4718 hours per day may be the maximum expansion of operating room hours without jeopardizing clinical outcomes. Research on volume-outcome relationships suggests that outcomes may also improve (Table).45,48,49
We estimate net national reduction in annual US health spending from RE-SET to be $26.2 billion, or 12.5% of annual current US spending on outpatient surgeries.
REPLICATE: Standardize and Integrate Care Across an Episode
Inefficient processes, slow adoption of evidence-based practice, and fragmentation of care is thought to account for as much as 30% of US healthcare spending.50 Standardizing today’s ASC processes on those that demonstrate the highest level of value and integrating them across the entire surgical episode can further boost the value of US surgical care.51 Because ASCs avoid urgent circumstances and high-risk patients, they are especially well-suited for care-process standardization. Standardized care can incorporate 3 elements and extend from the point of referral to recovery.52,53
Clinical algorithms. These are structured, multidisciplinary plans of care that integrate clinical guidelines and protocols adjusted to fit local environments and workflow capabilities. These algorithms improve outcomes and yield an average cost savings of 18%.35,54 Checklists may ensure the use of clinical algorithms. A number of off-the-shelf options currently exist for preoperative checklists, such as those generated by organizations like Strong for Surgery,55 which focuses on patients’ preoperative behavior.55-57 Additional clinical algorithms should be designed to optimize care transitions for postdischarge care.
Standard workflows and nonlabor inputs. Clinical algorithms yield to standardized workflows that, in turn, allow lower-cost clinical team members to perform work that is currently performed by more costly health professionals. Standard workflows extend outside the procedure to encompass tasks such as discharge planning58,59 and turnovers to reduce operating room down time.60 Standardizing nonlabor inputs, such as surgical supplies, based on comparative effectiveness and price, reduces the cost of surgery and allows for volume-based price discounts from suppliers. It also simplifies purchasing and reduces the time and effort needed to tailor supplies to surgical team preferences. Such standardization may lead to cost savings of roughly 20%61 and improve quality of care by reducing variation in equipment and supplies that support staff members must master, thus reducing errors attributed to unfamiliarity.
Continuous monitoring and adjustment of clinical algorithms and workflows. Additional reduction in variation can further boost the yield from algorithms and standard workflows by continuously analyzing deviations and making further refinements. As clinician confidence builds in algorithms, information technology tools, such as patient dashboards, automated check-in,62 and preadmission assessment,63 can ease care pathway implementation and improve the clinician and patient experience of care.
We estimate that net annual US savings associated with the REPLICATE element could approach $6.3 billion, or 3% of annual spending on outpatient surgeries after a 5-year implementation and refinement period.
Major opportunities remain to improve the value of US outpatient surgical care (Figure 2). To capitalize on these opportunities, we gathered evidence from diverse sources. The validity of our forecast for lowering the cost of better surgical care hinges on the quality and transferability of the evidence that we sourced. Pilot-testing of the Triple-R will reveal synergies and friction points among component parts.
Some elements of the composite, such as the expanded hours of operation, extend beyond directly relevant evidence and rely instead on successes in plausibly similar circumstances. When operationalizing such elements, it is important to consider context-dependent implementation hurdles; for example, expanding hours in the ASC context may present implementation challenges in incorporating provider and staff preferences for certain work hours. Furthermore, some of the reported efficiency in ASCs8,42,43 may be due to incentives to finish cases quickly because staffing is not performed in shifts. Thus, adding shifts may paradoxically lengthen case and turnaround times. Incentives, such as bonus payments for off-hour shifts may mitigate this issue. Expanded hours may also pose challenges to incorporating patient preferences. In previous studies of other procedures, patients have opted for inconvenient hours if the wait time for therapy was shorter.64 However, understanding patient preferences and trade-offs in elective surgery would be valuable; additionally, discounted pricing for unfavorable times may be considered.
We estimate that the potential for annual nationwide savings is roughly $40 billion net of implementation costs, or 19% of current annual spending on outpatient surgeries and more than 1% of total annual US healthcare spending. To achieve such savings, the Triple-R uses disruptive elements that would require structural and cultural shifts in the healthcare system. One such element is shifting procedures to ASCs despite current economic incentives to keep them in HOPDs. Our composite is designed with value-based payment, tiered networks, and reference pricing in mind, where such a tradeoff is indeed financially encouraged. However, even in other types of systems, market competition may ultimately work in favor of ASCs due to the low price, better convenience, and better quality. In addition, shifting higher turnover cases to ASCs will open up capacity at HOPDs, and allow them to streamline inputs and specialize their labor and care. Even with the shift, HOPDs will continue to produce a significant percentage of outpatient procedures (eg, complex procedures or procedures on medically complex patients).
The Triple-R focuses broadly on all outpatient procedures, but not all procedures will generate the same value. Future pilot studies will most likely focus on a smaller group of specialties. Although this choice will be site-dependent, there may be specialties and procedures that are likely to generate relatively more value from the application of our composite, due to, for example, a high volume of outpatient surgeries that can safely be moved to ASCs within the specialty. Our preliminary analysis suggests that certain procedures within the specialty areas of orthopedics, ophthalmology, plastic surgery, gastrointestinal, and gynecology may be good candidates for future pilot testing.
Results from pilot testing and scaling the proposed composite will hinge on each organization’s culture and management capabilities. Therefore, local operational and cultural factors must be a part of any implementation. The composite is designed to target levers with the highest opportunity to lower per capita healthcare spending safely. For example, even though there are opportunities to increase the value of care in HOPDs, ambulatory surgery represents a larger cost-reduction opportunity, and therefore has been chosen as the focus of the composite. Having said that, elements of REPLICATE can be used at HOPDs to increase efficiency and improve outcomes, while elements of REFINE apply to all outpatient procedures independent of surgical location.
Extrapolation based on published studies of the effects of each component suggests that the proposed 3-part composite may lower annual national outpatient surgical spending by as much as one-fifth, while maintaining or improving outcomes and the care experience for patients and clinicians. We have begun partnerships with healthcare organizations to assess the impact of the REFINE-RESET-REPLICATE composite. As clinicians and their organizations face increasing use of value-based payment, tiered networks, and reference pricing,65 its successful implementation and refinement may help secure their financial viability.
The authors wish to thank Dani Zionts, MSPH, for reviewing the article, and Rajbinder Mann for administrative support. They also thank Craig Albanese, MD, MBA; Jeffrey Belkora, PhD; John Chardos, MD; Alana Conner, PhD; David Hopkins, PhD; Mohit Kaushal, MD; Dhruv Kazi, MD; William Kennedy, MD; Thomas Krummel, MD; Richard Levy, PhD; Harold Luft, PhD; Richard Popp, MD; Stanley Rosenschein, PhD; Kristan Staudenmayer, MD; Ming Tai-Seale, PhD, MPH; Samuel Wald, MD, MBA; Thomas Weiser, MD; Paul Wise, MD, MPH; and Donna Zulman, MD, for their guidance.
Author Affiliations: Clinical Excellence Research Center (KB, FE, MK, AM, EM, CN, TP) and Division of General Medical Disciplines (SMA, FE, AM, EM, CN), Stanford University, Stanford, CA; Department of Neurosurgery, Stanford University Medical Center (MK), Stanford, CA; Department of Pediatrics, Lucile Packard Children’s Hospital (TP), Stanford, CA.
Source of Funding: The study was supported by the Sue and Dick Levy Fund, an advised fund of the Silicon Valley Community Foundation.
Author Disclosures: The authors report no relationship or financial interest with any entity that would pose a conflict of interest with the subject matter of this article.
Authorship Information: Concept and design (SMA, KB, FE, MK, AM, CN, TP); acquisition of data (KB, FE, MK, AM, CN, TP); analysis and interpretation of data (SMA, KB, FE, MK, EM, AM, CN, TP); drafting of the manuscript (SMA, KB, FE, MK, AM, EM); critical revision of the manuscript for important intellectual content (SMA, KB, FE, AM, EM, TP); statistical analysis (CN); provision of patients or study materials (MK); obtaining funding (AM); administrative, technical, or logistic support (FE, EM, TP); and supervision (SMA, MK, AM, EM, TP).
Address Correspondence to: Feryal Erhun, PhD, Judge Business School, University of Cambridge, Trumpington S, Cambridge CB2 1AG, UK. E-mail: email@example.com.
1. Cullen KA, Hall MJ, Golosinskiy A. Ambulatory surgery in the United States, 2006. Natl Health Stat Report. 2009;Jan 28(11):1-25.
2. Manchikanti L, Parr AT, Singh V, Fellows B. Ambulatory surgery centers and interventional techniques: a look at long-term survival. Pain Physician. 2011;14(2):E177-E215.
3. CMS. National health expenditures 2013 highlights. Hearthland Institute website. https://www. https://www.heartland. org/_template-assets/documents/publications/national_health_expenditures_highlights.pdf. Accessed December 5, 2014.â€¨
4. 2013 health care cost and utilization report. Health Care Cost Institute website. http://www.healthcostinsti- tute.org/files/2013%20HCCUR%2012-17-14.pdf. Published October 2014. Accessed December 5, 2014.â€¨
5. Farrell D, Jensen E, Kocher B, et al. Accounting for the cost of US health care: a new look at why Americans spend more. McKinsey Global Institute website. Published December 2008. Accessed December 5, 2014.
6. Manohar A, Cheung K, Wu CL, Stierer TS. Burden incurred by patients and their caregivers after outpatient surgery: a prospective observational study. Clin Orthop Relat Res. 2013;472(5):1416-1426. doi: 10.1007/ s11999-013-3270-6.â€¨
7. Fox JP, Vashi AA, Ross JS, Gross CP. Hospital-based, acute care after ambulatory surgery center discharge. Surgery. 2014;155(5):743-753. doi: 10.1016/j.surg.2013.12.008.â€¨
8. Munnich EL, Parente ST. Procedures take less time at ambulatory surgery centers, keeping costs down and ability to meet demand up. Health Aff (Millwood). 2014;33(5):764-769. doi: 10.1377/hlthaff.2013.1281.â€¨
9. Platchek T, Rebitzer R, Zulman D, Milstein A. Better health, less spending: Stanford’s Clinical Excellence Research Center. Heal Manag Policy Innov. 2014;2(1):10-17.
10. Bradley EH, Curry LA, Ramanadhan S, Rowe L, Nembhard IM, Krumholz HM. Research in action: using positive deviance to improve quality of health care. Implement Sci. 2009;4(1):25. doi: 10.1186/1748-5908-4-25.â€¨
11. Chan PS, Patel MR, Klein LW, et al. Appropriateness of percutaneous coronary intervention. JAMA. 2011;306(1):53-61. doi: 10.1001/jama.2011.916.â€¨
12. Epstein NE, Hood DC. “Unnecessary” spinal surgery: a prospective 1-year study of one surgeon’s experience. Surg Neurol Int. 2011;2:83. doi: 10.4103/2152-7806.82249.â€¨
13. Arterburn D, Wellman R, Westbrook E, et al. Introducing decision aids at group health was linked to sharply lower hip and knee surgery rates and costs. Health Aff (Millwood). 2012;31(9):2094-2104. doi:10.1377/hlthaff.2011.0686.
14. Al-Khatib SM, Hellkamp A, Curtis J, et al. Non-evidence-based ICD implantations in the United States. JAMA. 2011;305(1):43-49. doi:10.1001/jama.2010.1915.
15. Schroeck FR, Hollingsworth JM, Kaufman SR, Hollenbeck BK, Wei JT. Population based trends in the surgical treatment of benign prostatic hyperplasia. J Urol. 2012;188(5):1837-1841. doi:10.1016/j.juro.2012.07.049.
16. Leape LL. Unnecessary surgery. Annu Rev Public Health. 1992;13:363-383.
17. Hollingsworth JM, Ye Z, Strope SA, Krein SL, Hollenbeck AT, Hollenbeck BK. Physician-ownership of ambulatory surgery centers linked to higher volume of surgeries. Health Aff (Millwood). 2010;29(4):683-689. doi: 10.1377/hlthaff.2008.0567.â€¨
18. Hollenbeck BK, Dunn RL, Suskind AM, Zhang Y, Hollingsworth JM, Birkmeyer JD. Ambulatory surgery centers and outpatient procedure use among Medicare beneficiaries. Med Care. 2014;52(10):926-931. doi: 10.1097/MLR.0000000000000213.â€¨
19. Froehlich F, Pache I, Burnand B, et al. Performance of panel-based criteria to evaluate the appropriateness of colonoscopy: a prospective study. Gastrointest Endosc. 1998;48(2):128-136.
20. Hannan EL, Samadashvili Z, Cozzens K, et al. Appropriateness of diagnostic catheterization for suspected coronary artery disease in New York State. Circ Cardiovasc Interv. 2014;7(1):19-27. doi: 10.1161/ CIRCINTERVENTIONS.113.000741.â€¨
21. Delaune J, Everett W. Waste and inefficiency in the U.S. health care system—clinical care: a compre- hensive analysis in support of system-wide improvements. New England Healthcare Institute website. http:// www.nehi.net/writable/publication_files/file/waste_clinical_care_report_final.pdf. Published February 2008. Accessed December 10, 2014.â€¨
22. Fowler FJ, Levin CA, Sepucha KR. Informing and involving patients to improve the quality of medical decisions. Health Aff (Millwood). 2011;30(4):699-706. doi: 10.1377/hlthaff.2011.0003.â€¨
23. Friedberg MW, Chen PG, Van Busum KR, et al. Factors affecting physician professional satisfaction and their implications for patient care, health systems, and health policy. RAND Corporation website. http://www. rand.org/pubs/research_reports/RR439.html. Published 2013. Accessed January 21, 2015.â€¨
24. Shared decision making between patients and providers has promise, but obstacles remain. RAND Corporation website. http://www.rand.org/health/feature/shared_decision_making.html. Accessed December 5, 2014.
25. Rothberg MB, Pekow PS, Lahti M, Brody O, Skiest DJ, Lindenauer PK. Antibiotic therapy and treatment failure in patients hospitalized for acute exacerbations of chronic obstructive pulmonary disease. JAMA. 2010;303(20):2035-2042. doi: 10.1001/jama.2010.672.
26. Stacey D, Bennett CL, Barry MJ, et al. Decision aids for people facing health treatment or screening decisions. Cochrane Database Syst Rev. 2011;Oct 5(10):CD001431. doi: 10.1002/14651858.CD001431.pub3.â€¨
27. Friedberg MW, Van Busum K, Wexler R, Bowen M, Schneider EC. A demonstration of shared decision making in primary care highlights barriers to adoption and potential remedies. Health Aff (Millwood). 2013;32(2):268-275. doi: 10.1002/14651858.CD001431.pub3.â€¨
28. Patient decision aids: alphabetical list of decision aids by health topic. The Ottawa Hospital Research Institute website. http://decisionaid.ohri.ca/AZlist.html. Accessed January 30, 2015.â€¨
29. McGinn TG, McCullagh L, Kannry J, et al. Efficacy of an evidence-based clinical decision support in primary care practices: a randomized clinical trial. JAMA Intern Med. 2013;173(17):1584-1591. doi: 10.1377/hlthaff.2012.1084.
30. McLeod W, Eidus R, Stewart EE. Clinical decision support: using technology to identify patients’ unmet needs. Fam Pract Manag. 2012;19(2):22-28.
31. About. Choosing Wisely website. http://www.choosingwisely.org/about-us/. Accessed April 8, 2015.â€¨
32. Begley S. Medicare pays billions of dollars for wasteful procedures—study. Reuters website. http://www.reuters.com/article/2014/05/12/us-usa-healthcare-medicare-idUSBREA4B0SX20140512. Published 2014. Accessed April 8, 2015.
33. Strauss CE, Porten BR, Chavez IJ, et al. Real-time decision support to guide percutaneous coronary intervention bleeding avoidance strategies effectively changes practice patterns. Circ Cardiovasc Qual Outcomes. 2014;7(6):960-967. doi: 10.1161/CIRCOUTCOMES.114.001275.â€¨
34. Appropriate use criteria. American Academy of Orthopaedic Surgeons website. http://www.aaos.org/ auc/?ssopc=1. Published 2015. Accessed April 8, 2015.â€¨
35. James BC. Implementing practice guidelines through clinical quality improvement. Front Health Serv Manage. 1993;10(1):3-37, 54-56.
36. UC awards four grants to expand health care innovations. University of California website. http://health. universityofcalifornia.edu/2014/03/10/uc-awards-four-grants-to-expand-health-care-innovations/. Published 2014. Accessed April 8, 2015.â€¨
37. Rosenthal L. Electronic specialist consultations reduce unnecessary referrals and wait times for specialty appointments for uninsured and underinsured patients. Agency for Healthcare Research and Quality website. https://innovations.ahrq.gov/profiles/electronic-specialist-consultations-reduce-unnecessary-referrals-and- wait-times-specialty#contactInnovator. Updated August 13, 2014. Accessed April 8, 2015.
38. Sheridan R, Ammann HK. Mission possible: implementing eConsult in the Los Angeles County healthcare system. Blue Shield of California Foundation website. http://www.blueshieldcafoundation.org/sites/default/ files/publications/downloadable/Mission%20Possible%20-%20Implementing%20eConsult%20-%20Sept%20 2013.pdf. Published September 2013. Accessed August 2016.
39. McAdams M, Cannavo L, Orlander JD. A medical specialty e-Consult program in a VA health care system. Fed Pract. 2014;31(5):26-31.
40. ASC to HOPD conversion: costly consequences. Washington Ambulatory Surgery Center Association website. http://www.wasca.net/wp-content/uploads/2007/03/ASC-to-HOPD-Conversion-Costly-Consequences. pdf. Accessed January 10, 2015.
41. Jackson IJB, McWhinnie D, Skues M. BADS Directory of Procedures. London, UK: British Association of Day Surgery; 2013.â€¨
42. Carey K. Price increases were much lower in ambulatory surgery centers than hospital outpatient departments in 2007-12. Health Aff (Millwood). 2015;34(10):1738-1744. doi: 10.1377/hlthaff.2015.0252.
43. Trentman T, Mueller J, Gray R, Pockaj B, Simula D. Outpatient surgery performed in an ambulatory surgery center versus a hospital: comparison of perioperative time intervals. Am J Surg. 2010;200(1):64-67. doi: 10.1016/j.amjsurg.2009.06.029.â€¨
44. Khanna T, Rangan K V., Manocaran M. Narayana Hrudayalaya Heart Hospital: cardiac care for the poor (A). Harvard Bus Case 9-505-078. 2011.
45. Sosa JA, Bowman HM, Tielsch JM, Powe NR, Gordon TA, Udelsman R. The importance of surgeon experience for clinical and economic outcomes from thyroidectomy. Ann Surg. 1998;228(3):320-330.
46. Bell CM, Redelmeier DA. Enhanced weekend service: an affordable means to increased hospital procedure volume. CMAJ. 2005;172(4):503-504.
47. Rogers AE. The effects of fatigue and sleepiness on nurse performance and patient safety. In: Hughes RG, ed. Patient Safety and Quality: An Evidence-Based Handbook for Nurses. Rockville, MD: Agency for Healthcare Research and Quality (US); 2008.
48. Birkmeyer JD, Siewers AE, Finlayson EVA, et al. Hospital volume and surgical mortality in the United States. N Engl J Med. 2002;346(15):1128-1137. doi: 10.1056/NEJMsa012337.
49. Hannan EL, Radzyner M, Rubin D, Dougherty J, Brennan MF. The influence of hospital and surgeon volume on in-hospital mortality for colectomy, gastrectomy, and lung lobectomy in patients with cancer. Surgery. 2002;131(1):6-15.
50. Elhauge E, ed. The Fragmentation of U.S. Health Care: Causes and Solutions. New York, NY: Oxford University Press; 2010.
51. Leung GM. Hospitals must become “focused factories”. BMJ. 2000;320(7239):942-943.
52. Vanhaecht K, Panella M, van Zelm R, Sermeus W. An overview on the history and concept of care pathways as complex interventions. Int J Care Pathways. 2010;14(3):117-123. doi: 10.1258/jicp.2010.010019.
53. James BC, Savitz LA. How Intermountain trimmed health care costs through robust quality improvement efforts. Health Aff (Millwood). 2011;30(6):1185-1191. doi: 10.1377/hlthaff.2011.0358.â€¨
54. Rotter T, Kinsman L, James E, et al. Clinical pathways: Effects on professional practice, patient outcomes, length of stay and hospital costs. Cochrane Database Syst Rev. 2010;(3):CD006632. doi: 10.1002/14651858. CD006632.pub2.
55. Strong for Surgery. CERTAIN website. http://www.becertain.org/strong_for_surgery. Accessed January 10, 2015.â€¨
56. Hulzebos EHJ, Helders PJM, Favié NJ, De Bie RA, Brutel de la Riviere A, Van Meeteren NLU. Preoperative intensive inspiratory muscle training to prevent postoperative pulmonary complications in high-risk patients undergoing CABG surgery: a randomized clinical trial. JAMA. 2006;296(15):1851-1857. doi: 10.1001/jama.296.15.1851
57. Møller AM, Villebro N, Pedersen T, Tønnesen H. Effect of preoperative smoking intervention on postoperative complications: a randomised clinical trial. Lancet. 2002;359(9301):114-117.
58. Proactive discharge planning keeps LOS low. Hospital Case Management. 2009; 17(12):185.
59. Quality and Service Improvement Tools: Discharge Planning. The NHS Institute for Innovation and Improvement website. http://www.institute.nhs.uk/quality_and_service_improvement_tools/quality_and_service_im- provement_tools/discharge_planning.html. Accessed April 10, 2015.
60. Cendán JC, Good M. Interdisciplinary work flow assessment and redesign decreases operating room turnover time and allows for additional caseload. Arch Surg. 2006;141(1):65-69; discussion 70.
61. Avansino JR, Goldin AB, Risley R, Waldhausen JHT, Sawin RS. Standardization of operative equipment reduces cost. J Pediatr Surg. 2013;48(9):1843-1849. doi: 10.1016/j.jpedsurg.2012.11.045.
62. NCR. Case study: Vanguard Urologic Institute reduces patient wait times via self-service. Health IT Outcomes website. http://www.healthitoutcomes.com/doc/reduces-patient-wait-times-via-self-service-0001. Published January 18, 2011. Accessed August 2016.â€¨
63. Dublin Surgery Center. OneMedical Passport website. http://onemedicalpassportcompany.com/wp-content/uploads/2013/08/Dublin_Surgery_case_study.pdf. Accessed August 2016.â€¨
64. Brown A, Atyeo J, Field N, Cox J, Bull C, Gebski V. Evaluation of patient preferences towards treatment during extended hours for patients receiving radiation therapy for the treatment of cancer: a time trade-off study. Radiother Oncol. 2009;90(2):247-252. doi: 10.1016/j.radonc.2008.11.019.â€¨
65. Burwell SM. Setting value-based payment goals—HHS efforts to improve U.S. health care. N Engl J Med. 2015;372(10):897-899. doi: 10.1056/NEJMp1500445.â€¨