{"title":"In this issue.","authors":"B. Gray","doi":"10.1111/milq.12012","DOIUrl":"https://doi.org/10.1111/milq.12012","url":null,"abstract":"","PeriodicalId":78777,"journal":{"name":"The Milbank Memorial Fund quarterly","volume":"61 1","pages":"219-21"},"PeriodicalIF":0.0,"publicationDate":"2013-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"85687531","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2012-01-01DOI: 10.1111/j.1468-0009.2011.00651.x
B. Gray
{"title":"In this issue.","authors":"B. Gray","doi":"10.1111/j.1468-0009.2011.00651.x","DOIUrl":"https://doi.org/10.1111/j.1468-0009.2011.00651.x","url":null,"abstract":"","PeriodicalId":78777,"journal":{"name":"The Milbank Memorial Fund quarterly","volume":"35 1","pages":"1-4"},"PeriodicalIF":0.0,"publicationDate":"2012-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"75053408","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Group practice in health care delivery takes many forms. The term “group practice” usually refers to a group of pro fessionals who combine their resources in delivering treatment care to a patient population. Some of these groups are actively involved in preventive medicine efforts. Because of heavy work loads, more and more activities are being delegated to nurses, physicians assistants and, in some cases, community-based family health workers. One common condition in all of these settings is that a group is doing the “practicing.” The effectiveness of any group in any setting is related to both its capabilities to do the work and its ability to manage itself as an interdependent group of people. The central focus of this paper will be upon the internal dynamics involved when a collection of individuals attempts to function as a group. The objective is to provide a framework that will facilitate consid eration of several important issues involved in the more effective utilization of groups in delivering health care. We will begin by drawing upon the general body of knowl edge about groups and their dynamics developed within the behavioral sciences. Several key variables known to be of prime importance in any group situation will be discussed. Next, we will discuss the particular relevance of these variables to group medical practice.
{"title":"Factors influencing the effectiveness of health teams.","authors":"I. Rubin, R. Beckhard","doi":"10.2307/3349352","DOIUrl":"https://doi.org/10.2307/3349352","url":null,"abstract":"Group practice in health care delivery takes many forms. The term “group practice” usually refers to a group of pro fessionals who combine their resources in delivering treatment care to a patient population. Some of these groups are actively involved in preventive medicine efforts. Because of heavy work loads, more and more activities are being delegated to nurses, physicians assistants and, in some cases, community-based family health workers. One common condition in all of these settings is that a group is doing the “practicing.” The effectiveness of any group in any setting is related to both its capabilities to do the work and its ability to manage itself as an interdependent group of people. The central focus of this paper will be upon the internal dynamics involved when a collection of individuals attempts to function as a group. The objective is to provide a framework that will facilitate consid eration of several important issues involved in the more effective utilization of groups in delivering health care. We will begin by drawing upon the general body of knowl edge about groups and their dynamics developed within the behavioral sciences. Several key variables known to be of prime importance in any group situation will be discussed. Next, we will discuss the particular relevance of these variables to group medical practice.","PeriodicalId":78777,"journal":{"name":"The Milbank Memorial Fund quarterly","volume":"50 3 1","pages":"317-35"},"PeriodicalIF":0.0,"publicationDate":"2011-08-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.2307/3349352","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"68581830","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2011-01-01DOI: 10.1111/j.1468-0009.2011.00640.x
B. Gray
{"title":"In this issue.","authors":"B. Gray","doi":"10.1111/j.1468-0009.2011.00640.x","DOIUrl":"https://doi.org/10.1111/j.1468-0009.2011.00640.x","url":null,"abstract":"","PeriodicalId":78777,"journal":{"name":"The Milbank Memorial Fund quarterly","volume":"1 1","pages":"339-42"},"PeriodicalIF":0.0,"publicationDate":"2011-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"89774401","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2011-01-01DOI: 10.1111/j.1468-0009.2011.00631.x
B. Gray
{"title":"In this issue.","authors":"B. Gray","doi":"10.1111/j.1468-0009.2011.00631.x","DOIUrl":"https://doi.org/10.1111/j.1468-0009.2011.00631.x","url":null,"abstract":"","PeriodicalId":78777,"journal":{"name":"The Milbank Memorial Fund quarterly","volume":"30 2 1","pages":"163-6"},"PeriodicalIF":0.0,"publicationDate":"2011-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"88295821","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2008-12-01DOI: 10.1111/j.1468-0009.2008.00534.x
Donna M. Zulman, S. Vijan, G. Omenn, R. Hayward
CONTEXT Preventive medicine has historically favored reducing a risk factor by a small amount in the entire population rather than by a large amount in high-risk individuals. The use of multivariable risk prediction tools, however, may affect the relative merits of this strategy. METHODS This study uses risk factor data from the National Health and Nutrition Examination Survey III to simulate a population of more than 100 million Americans aged thirty or older with no history of CV disease. Three strategies that could affect CV events, CV mortality, and quality-adjusted life years were examined: (1) a population-based strategy that treats all individuals with a low- or moderate-intensity intervention (in which the low-intensity intervention represents a public health campaign with no demonstrable adverse effects), (2) a targeted strategy that treats individuals in the top 25 percent based on a single risk factor (LDL), and (3) a risk-targeted strategy that treats individuals in the top 25 percent based on overall CV risk (as predicted by a multivariable prediction tool). The efficiency of each strategy was compared while varying the intervention's intensity and associated adverse effects, and the accuracy of the risk prediction tool. FINDINGS The LDL-targeted strategy and the low-intensity population-based strategy were comparable for CV events prevented over five years (0.79 million and 0.75 million, respectively), as were the risk-targeted strategy and moderate-intensity population-based strategy (1.56 million and 1.87 million, respectively). The risk-targeted strategy, however, was more efficient than the moderate-intensity population-based strategy (number needed to treat [NNT] 19 vs. 62). Incorporating a small degree of treatment-related adverse effects greatly magnified the relative advantages of the risk-targeted approach over other strategies. Reducing the accuracy of the prediction tool only modestly decreased this greater efficiency. CONCLUSIONS A population-based prevention strategy can be an excellent option if an intervention has almost no adverse effects. But if the intervention has even a small degree of disutility, a targeted approach using multivariable risk prediction can prevent more morbidity and mortality while treating many fewer people.
{"title":"The relative merits of population-based and targeted prevention strategies.","authors":"Donna M. Zulman, S. Vijan, G. Omenn, R. Hayward","doi":"10.1111/j.1468-0009.2008.00534.x","DOIUrl":"https://doi.org/10.1111/j.1468-0009.2008.00534.x","url":null,"abstract":"CONTEXT\u0000Preventive medicine has historically favored reducing a risk factor by a small amount in the entire population rather than by a large amount in high-risk individuals. The use of multivariable risk prediction tools, however, may affect the relative merits of this strategy.\u0000\u0000\u0000METHODS\u0000This study uses risk factor data from the National Health and Nutrition Examination Survey III to simulate a population of more than 100 million Americans aged thirty or older with no history of CV disease. Three strategies that could affect CV events, CV mortality, and quality-adjusted life years were examined: (1) a population-based strategy that treats all individuals with a low- or moderate-intensity intervention (in which the low-intensity intervention represents a public health campaign with no demonstrable adverse effects), (2) a targeted strategy that treats individuals in the top 25 percent based on a single risk factor (LDL), and (3) a risk-targeted strategy that treats individuals in the top 25 percent based on overall CV risk (as predicted by a multivariable prediction tool). The efficiency of each strategy was compared while varying the intervention's intensity and associated adverse effects, and the accuracy of the risk prediction tool.\u0000\u0000\u0000FINDINGS\u0000The LDL-targeted strategy and the low-intensity population-based strategy were comparable for CV events prevented over five years (0.79 million and 0.75 million, respectively), as were the risk-targeted strategy and moderate-intensity population-based strategy (1.56 million and 1.87 million, respectively). The risk-targeted strategy, however, was more efficient than the moderate-intensity population-based strategy (number needed to treat [NNT] 19 vs. 62). Incorporating a small degree of treatment-related adverse effects greatly magnified the relative advantages of the risk-targeted approach over other strategies. Reducing the accuracy of the prediction tool only modestly decreased this greater efficiency.\u0000\u0000\u0000CONCLUSIONS\u0000A population-based prevention strategy can be an excellent option if an intervention has almost no adverse effects. But if the intervention has even a small degree of disutility, a targeted approach using multivariable risk prediction can prevent more morbidity and mortality while treating many fewer people.","PeriodicalId":78777,"journal":{"name":"The Milbank Memorial Fund quarterly","volume":"10 1","pages":"557-80"},"PeriodicalIF":0.0,"publicationDate":"2008-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"88866033","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2008-12-01DOI: 10.1111/j.1468-0009.2008.00535.x
J. Ahern, Matthew R. Jones, Erin Bakshis, S. Galea
CONTEXT Geoffrey Rose's two principal approaches to public health intervention are (1) targeted strategies focusing on individuals at a personal increased risk of disease and (2) population-wide approaches focusing on the whole population. Beyond his discussion of the strengths and weaknesses of these approaches, there is no empiric work examining the conditions under which one of these approaches may be better than the other. METHODS This article uses mathematical simulations to model the benefits and costs of the two approaches, varying the cut points for treatment, effect magnitudes, and costs of the interventions. These techniques then were applied to the specific example of an intervention on blood pressure to reduce cardiovascular disease. FINDINGS In the general simulation (using an inverse logit risk curve), lower costs of intervention, treating people with risk factor values at or above where the slope on the risk curve is at its steepest (for targeted interventions), and interventions with larger effects on reducing the risk factor (for population-wide interventions) provided benefit/cost advantages. In the specific blood pressure intervention example, lower-cost population-wide interventions had better benefit/cost ratios, but some targeted treatments with lower cutoffs prevented more absolute cases of disease. CONCLUSIONS These simulations empirically evaluate some of Rose's original arguments. They can be replicated for particular interventions being considered and may be useful in helping public health decision makers assess potential intervention strategies.
{"title":"Revisiting rose: comparing the benefits and costs of population-wide and targeted interventions.","authors":"J. Ahern, Matthew R. Jones, Erin Bakshis, S. Galea","doi":"10.1111/j.1468-0009.2008.00535.x","DOIUrl":"https://doi.org/10.1111/j.1468-0009.2008.00535.x","url":null,"abstract":"CONTEXT\u0000Geoffrey Rose's two principal approaches to public health intervention are (1) targeted strategies focusing on individuals at a personal increased risk of disease and (2) population-wide approaches focusing on the whole population. Beyond his discussion of the strengths and weaknesses of these approaches, there is no empiric work examining the conditions under which one of these approaches may be better than the other.\u0000\u0000\u0000METHODS\u0000This article uses mathematical simulations to model the benefits and costs of the two approaches, varying the cut points for treatment, effect magnitudes, and costs of the interventions. These techniques then were applied to the specific example of an intervention on blood pressure to reduce cardiovascular disease.\u0000\u0000\u0000FINDINGS\u0000In the general simulation (using an inverse logit risk curve), lower costs of intervention, treating people with risk factor values at or above where the slope on the risk curve is at its steepest (for targeted interventions), and interventions with larger effects on reducing the risk factor (for population-wide interventions) provided benefit/cost advantages. In the specific blood pressure intervention example, lower-cost population-wide interventions had better benefit/cost ratios, but some targeted treatments with lower cutoffs prevented more absolute cases of disease.\u0000\u0000\u0000CONCLUSIONS\u0000These simulations empirically evaluate some of Rose's original arguments. They can be replicated for particular interventions being considered and may be useful in helping public health decision makers assess potential intervention strategies.","PeriodicalId":78777,"journal":{"name":"The Milbank Memorial Fund quarterly","volume":"1 1","pages":"581-600"},"PeriodicalIF":0.0,"publicationDate":"2008-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"91350060","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2008-12-01DOI: 10.1111/j.1468-0009.2008.00533.x
Richard L Kravitz, N. Duan, Edmund J. Niedzinski, M. Hay, SASKIA K. Subramanian, THOMAS S. Weisner
CONTEXT When feasible, randomized, blinded single-patient (n-of-1) trials are uniquely capable of establishing the best treatment in an individual patient. Despite early enthusiasm, by the turn of the twenty-first century, few academic centers were conducting n-of-1 trials on a regular basis. METHODS The authors reviewed the literature and conducted in-depth telephone interviews with leaders in the n-of-1 trial movement. FINDINGS N-of-1 trials can improve care by increasing therapeutic precision. However, they have not been widely adopted, in part because physicians do not sufficiently value the reduction in uncertainty they yield weighed against the inconvenience they impose. Limited evidence suggests that patients may be receptive to n-of-1 trials once they understand the benefits. CONCLUSIONS N-of-1 trials offer a unique opportunity to individualize clinical care and enrich clinical research. While ongoing changes in drug discovery, manufacture, and marketing may ultimately spur pharmaceutical makers and health care payers to support n-of-1 trials, at present the most promising resuscitation strategy is stripping n-of-1 trials to their essentials and marketing them directly to patients. In order to optimize statistical inference from these trials, empirical Bayes methods can be used to combine individual patient data with aggregate data from comparable patients.
{"title":"What ever happened to N-of-1 trials? Insiders' perspectives and a look to the future.","authors":"Richard L Kravitz, N. Duan, Edmund J. Niedzinski, M. Hay, SASKIA K. Subramanian, THOMAS S. Weisner","doi":"10.1111/j.1468-0009.2008.00533.x","DOIUrl":"https://doi.org/10.1111/j.1468-0009.2008.00533.x","url":null,"abstract":"CONTEXT\u0000When feasible, randomized, blinded single-patient (n-of-1) trials are uniquely capable of establishing the best treatment in an individual patient. Despite early enthusiasm, by the turn of the twenty-first century, few academic centers were conducting n-of-1 trials on a regular basis.\u0000\u0000\u0000METHODS\u0000The authors reviewed the literature and conducted in-depth telephone interviews with leaders in the n-of-1 trial movement.\u0000\u0000\u0000FINDINGS\u0000N-of-1 trials can improve care by increasing therapeutic precision. However, they have not been widely adopted, in part because physicians do not sufficiently value the reduction in uncertainty they yield weighed against the inconvenience they impose. Limited evidence suggests that patients may be receptive to n-of-1 trials once they understand the benefits.\u0000\u0000\u0000CONCLUSIONS\u0000N-of-1 trials offer a unique opportunity to individualize clinical care and enrich clinical research. While ongoing changes in drug discovery, manufacture, and marketing may ultimately spur pharmaceutical makers and health care payers to support n-of-1 trials, at present the most promising resuscitation strategy is stripping n-of-1 trials to their essentials and marketing them directly to patients. In order to optimize statistical inference from these trials, empirical Bayes methods can be used to combine individual patient data with aggregate data from comparable patients.","PeriodicalId":78777,"journal":{"name":"The Milbank Memorial Fund quarterly","volume":"25 1","pages":"533-55"},"PeriodicalIF":0.0,"publicationDate":"2008-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"82503001","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2008-12-01DOI: 10.1111/j.1468-0009.2008.00536.x
Sarah E. Gollust, S. Schroeder, K. Warner
CONTEXT Counseling smokers to quit smoking and providing them with pharmaceutical cessation aides are among the most beneficial and cost-effective interventions that clinicians can offer patients. Yet assistance with quitting is not universally covered by health plans or offered by all clinicians. Analysis of stakeholders' perspectives and interests can identify the barriers to more widespread provision of cessation services and suggest strategies for the public policy agenda to advance smoking cessation. METHODS Review of literature and discussions with representatives of stakeholders. FINDINGS All stakeholders-health plans, employers, clinicians, smokers, and the government-face barriers to broader smoking cessation activities. These range from health plans' perceiving that covering counseling and pharmacotherapy will increase costs without producing commensurate health care savings, to clinicians' feeling unprepared and uncompensated for counseling. Like other preventive measures aimed at behavior, efforts directed at smoking cessation have marginal status among health care interventions. State governments can help correct this status by increasing Medicaid coverage of treatment and expanding coverage for state employees. The federal government can promote the adoption of six initiatives recommended by a government subcommittee on cessation: set up a national quit line, develop a media campaign to encourage cessation, include cessation benefits in all federally funded insurance plans, create a research infrastructure to improve cessation rates, develop a clinician training agenda, and create a fund to increase cessation activities through a new $2 per pack cigarette excise tax. Both the federal and state governments can increase cessation by adopting policies such as the higher cigarette tax and laws prohibiting smoking in workplaces and public places. CONCLUSIONS Public policy efforts should assume greater social responsibility for smoking cessation, including more aggressive leadership at the state and federal levels, as well as through advocacy, public health, and clinician organizations.
{"title":"Helping smokers quit: understanding the barriers to utilization of smoking cessation services.","authors":"Sarah E. Gollust, S. Schroeder, K. Warner","doi":"10.1111/j.1468-0009.2008.00536.x","DOIUrl":"https://doi.org/10.1111/j.1468-0009.2008.00536.x","url":null,"abstract":"CONTEXT\u0000Counseling smokers to quit smoking and providing them with pharmaceutical cessation aides are among the most beneficial and cost-effective interventions that clinicians can offer patients. Yet assistance with quitting is not universally covered by health plans or offered by all clinicians. Analysis of stakeholders' perspectives and interests can identify the barriers to more widespread provision of cessation services and suggest strategies for the public policy agenda to advance smoking cessation.\u0000\u0000\u0000METHODS\u0000Review of literature and discussions with representatives of stakeholders.\u0000\u0000\u0000FINDINGS\u0000All stakeholders-health plans, employers, clinicians, smokers, and the government-face barriers to broader smoking cessation activities. These range from health plans' perceiving that covering counseling and pharmacotherapy will increase costs without producing commensurate health care savings, to clinicians' feeling unprepared and uncompensated for counseling. Like other preventive measures aimed at behavior, efforts directed at smoking cessation have marginal status among health care interventions. State governments can help correct this status by increasing Medicaid coverage of treatment and expanding coverage for state employees. The federal government can promote the adoption of six initiatives recommended by a government subcommittee on cessation: set up a national quit line, develop a media campaign to encourage cessation, include cessation benefits in all federally funded insurance plans, create a research infrastructure to improve cessation rates, develop a clinician training agenda, and create a fund to increase cessation activities through a new $2 per pack cigarette excise tax. Both the federal and state governments can increase cessation by adopting policies such as the higher cigarette tax and laws prohibiting smoking in workplaces and public places.\u0000\u0000\u0000CONCLUSIONS\u0000Public policy efforts should assume greater social responsibility for smoking cessation, including more aggressive leadership at the state and federal levels, as well as through advocacy, public health, and clinician organizations.","PeriodicalId":78777,"journal":{"name":"The Milbank Memorial Fund quarterly","volume":"58 1","pages":"601-27"},"PeriodicalIF":0.0,"publicationDate":"2008-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"73669201","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2008-01-01DOI: 10.1111/j.1468-0009.2008.00538.x
B. Gray
{"title":"Milbank quarterly.","authors":"B. Gray","doi":"10.1111/j.1468-0009.2008.00538.x","DOIUrl":"https://doi.org/10.1111/j.1468-0009.2008.00538.x","url":null,"abstract":"","PeriodicalId":78777,"journal":{"name":"The Milbank Memorial Fund quarterly","volume":"1 1","pages":"529-32"},"PeriodicalIF":0.0,"publicationDate":"2008-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"80155449","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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