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Sean R. Tunis is responsible for making evidence-based national coverage policies; setting quality standards for Medicare and Medicaid providers; and leading quality measurement and improvement activities for the CMS. Over his career, he has devoted considerable attention to the role of cost effectiveness in guiding decision making. Given his stature as a thought leader on these issues, we felt he was the ideal person to comment on the topics raised in this issue of the Journal. The potential use of economic analysis in healthcare decision making has strong natural appeal as it becomes clear how an individual's healthcare decisions can affect, through insurance coverage, the ability of others to obtain the care they need.
Healthcare spending in the United States continues to rise faster than inflation and worker salaries. Financial pressures force difficult decisions about whether to increase premiums, reduce benefits, or reallocate resources.
Using analytic methods that are designed to maximize the value of the health services purchased for a given level of spending makes intuitive sense. The failure to adopt such a framework given the current economic realities in healthcare seems, at best, unwise. The two articles by Neumann 1 and Bloom 2 that appear elsewhere in this issue illustrate this point quite well. The application of economic analysis in healthcare decision making, however, particularly with regard to medical necessity, has proven to be highly controversial.
Evidence varies regarding the extent to which decision makers actually use economic analysis, a point that Bernard Bloom discusses in this issue of the Journal.
Significant disagreement exists over the appropriate locus for medical decision making, the integrity of the process used by payers to make these decisions, and the proper role of scientific evidence. Locus of Decision Making One important source of tension regarding decisions about medical necessity is the degree of deference accorded to judgments and preferences of patients and their physicians. The idealized view is that clinicians and patients arrive at optimal choices by carefully considering the pros and cons of the existing alternatives, free of all factors other than clinical risks and benefits and individual patient preferences.
In this model, clinicians attempt to identify the clinical intervention that best meets the patient's need, separating this clinical decision from any consideration of what the indirect impact of this decision might be. Only at the level of these individual decisions, it is argued, can all the subtle variations in clinical presentation and social context be thoroughly understood and factored into determinations about what is the best medically necessary care for the patient.
Unquestionably, clinical decisions are best made by informed patients consulting with their clinicians. However, many outside factors that have little to do with clinical benefits and preferences also influence healthcare decisions. Patients are exposed to an increasing volume of direct-to-consumer advertising and to healthcare information from the Internet and other sources that are of variable objectivity and quality.
Clinicians also receive a large volume of information from commercial entities and the media that may not reflect a carefully balanced perspective on the value of medical alternatives. Several decades of health services research, documenting widespread underuse, overuse, and misuse of healthcare technologies and services, demonstrate that reliance on judgments at the level of individual patient encounters does not ensure that medically necessary care will be consistently provided.
The backlash against managed care in the s was primarily driven by the powerful concern of patients and policy makers over the idea that those who pay for healthcare services could override the decisions of clinicians and patients regarding diagnosis, treatment, and specialist referral.
In legal proceedings, challenges to noncoverage decisions are generally received with considerable sympathy by judges and juries who see sick patients with limited options for getting well having their doctors overruled by organizations whose primary functions are administrative, not clinical.
States continue to pass laws mandating that specific services be covered in order to prevent insurers from limiting access to certain technologies and services, and requiring independent appeals mechanisms outside the control of the payer for medical necessity decisions. Yet, HSIIs do consume resources — resources that could be invested in other interventions to benefit patients or providers. We believe that the donors and national governments should insist on greater transparency in the resources devoted to reducing medical errors and substandard care.
The solution to this is for health system policy-makers to mandate that economic analyses of HSIIs be conducted to produce evidence that an HSII is acceptably cost-effective or cost-saving before resources are expended to implement it, particularly at large scale.
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It is clear that there are multiple competing demands on health-care resources in all settings, and by definition this is especially the case in low- and middle-income settings. It could be viewed as ethically dubious in such settings to implement an HSII without examination of its economic consequences because, without knowing if the intervention is affordable, sustainable, or acceptably efficient, information on its effectiveness alone is not useful.
As with individual medical interventions, studies of HSII effectiveness are necessary but not sufficient to recommend implementing the intervention in a given setting. This is not to ignore the additional difficulties of performing economic analyses on complex social system interventions. It adds to the cost and time required to evaluate the program and the expertise required to carry it out.
However, these factors cannot be excuses to omit such an important aspect of determining the applicability of the HSII. There is no place where health-care resources are inexhaustible and where difficult resource allocation decisions do not need to be made. Economic analysis of HSIIs is a vital part of the science of improvement that helps health systems advance.
Economic Analysis of Health Care Technology: A Report on Principles
EB conceived of the idea and drafted the initial version of the manuscript. LM contributed substantively to the writing and editing the manuscript and responding to reviewer comments. The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest. National Center for Biotechnology Information , U. Journal List Front Public Health v. Front Public Health. Published online Oct Author information Article notes Copyright and License information Disclaimer. Received Jul 19; Accepted Sep The use, distribution or reproduction in other forums is permitted, provided the original author s or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice.
No use, distribution or reproduction is permitted which does not comply with these terms. Abstract There is little evidence to direct health systems toward providing efficient interventions to address medical errors, defined as an unintended act of omission or commission or one not executed as intended that may or may not cause harm to the patient but does not achieve its intended outcome. Keywords: health system improvement, economic analysis, cost-effectiveness, medical errors. Introduction It is clear that medical errors carry a very high burden of disease 1 and are considered a problem to be addressed by the health system as much as physiological conditions, such as ischemic heart disease IHD and stroke, the two leading causes of mortality worldwide 2.
Economic Analysis of Risk and Uncertainty induced by Health Shocks: A Review and Extension
Evaluation of Improvement Interventions to Decrease Errors There are many reports of evaluations of interventions to improve health service delivery to decrease the incidence of errors that lead to adverse health events 7 — 9 and to increase adherence to evidence-based standards 10 — 12 , including in low- and middle-income countries 13 — Table 1 Measurement challenges and potential solutions in conducting economic evaluations of health system improvement interventions.
Measurement challenge Potential solutions Defining intervention Empirically define intervention; use modeling, Monte Carlo Simulation, and sensitivity analysis to account for low fidelity to intervention Defining effectiveness Measure all positive and potentially negative effects balancing measures as feasible; report all effects clearly Process measures versus outcomes Use epidemiological modeling e.
Open in a separate window. Defining the Intervention Health system improvement interventions to reduce errors and improve health service quality often have a degree of complexity and adaptation to local context that individual medical interventions usually do not. Defining the Effects Assuming success, HSIIs often have more than one positive effect on health outcomes of those receiving care from the heath provider unit involved. Process Measures Instead of Outcome Measures Often in HSIIs, it is more feasible and accurate to measure the effect of the intervention in process measures rather than patient health outcomes.
Costing of Effects Given that HSIIs for medical error reduction often involve multiple different health outcomes, determining the economic impact can add significantly to the complexity of data collection and modeling. Discussion and Conclusion Individual medical interventions often involve a single medication or class of medications or medical device.
Author Contributions EB conceived of the idea and drafted the initial version of the manuscript. Conflict of Interest Statement The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest. References 1. Makary M, Daniel M. Medical error — the third leading cause of death in the US.
BMJ :i World Health Organization. The Top 10 Causes of Death. Geneva, Switzerland: WHO; Broughton I. Int J Care Pathway 15 — Health Aff Millwood 30 4 — Reason J. Human Error. Cambridge: Cambridge University Press; Separate multiple e-mails with a ;. Thought you might appreciate this item s I saw at Current Opinion in Ophthalmology. Send a copy to your email. Some error has occurred while processing your request.
Please try after some time. How to interpret a healthcare economic analysis. Current Opinion in Ophthalmology16 3 , June Add Item s to:. An Existing Folder. A New Folder.
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