Skip to main content
Full access
Commentary
Published Online: 1 November 2008

What Explains the Diffusion of Treatments for Mental Illness?

Scientific advances have fueled both gains in health care outcomes and growth in health care costs (1) . Health care for mental illness is no exception, with significant improvements in the treatment of schizophrenia, depression, and other common disorders during the last 20 years. As a general rule, diffusion and implementation of research-based health care proceeds slowly (2) . Rates of dissemination and adoption of new treatments are influenced by many factors other than patient needs and scientific evidence. For example, marketing, profit motives, health care cultures, professional and guild issues, spending levels, organizational structures, financing mechanisms, and implementation difficulties all have been shown to influence diffusion (3) . Because these factors exhibit great diversity across states, regions, and local agencies, it is perhaps not surprising that health care exhibits a high degree of geographic variation (4) .
Yet the gap between evidence-based practices and usual services is enormous in mental health (58) . For example, the Schizophrenia Patient Outcomes Research Team found that less than 30% of schizophrenia outpatients receiving care in two large public mental health systems were prescribed an antipsychotic medication within the recommended dose range and even smaller percentages received evidence-based psychosocial treatments (9) . The National Comorbidity Study Replication showed that less than half of persons with mental disorders received any treatment in the past 12 months, only one-third of treatments met minimal standards of adequacy, and those in rural areas as well as racial and ethnic minorities and elders were less likely to receive any treatment (7) .
Mental health services are similar to general health care services in many ways, particularly for treatments combining medications with a variety of related office visits. Mental health services differ, however, in the relative importance of psychosocial treatments, such as psychotherapy and psychiatric rehabilitation. Unlike new medications, in which implementation of new practices is supported and encouraged by marketing efforts, psychosocial treatments rarely are encouraged by commercial marketing. As is the case with medications that have gone “off patent” and are available in generic form, there is little economic incentive to market psychosocial services since they are not under patent protection.
In this article, we explore the importance of diffusion and the variation in adoption of high- and low-value treatment strategies in health care, with a focus on three factors that influence diffusion: informational or attitudinal barriers, funding mechanisms, and profitability. We conclude with several proposals designed to better understand and improve the diffusion of evidence-based treatments for mental illness.

Determinants of Diffusion and Adoption

The earliest studies of diffusion arose in agriculture, where researchers puzzled as to why new technological improvements in farming, such as hybrid corn, were so slow to be adopted by farmers (10) . This was viewed by rural sociologists as evidence of seemingly irrational resistance to change that might be mediated by education and past experiences with adopting new products (3) . By contrast, economists such as Griliches (10) stressed the importance of the profit motive: farmers in Iowa, who innovated early, made more money from adopting hybrid corn than the late adopters in Arkansas. Thus, a long-standing tension in the literature exists between the economic models that stress rational models of nonadoption and the sociological models where the inability to adopt a new technology reflects the difficulty of changing behavior from the old habits to something new (11) .
While the economics literature has largely ignored issues of collective decision-making, other studies have focused on the organizational barriers facing not-for-profit institutions seeking to adopt cost-saving or efficiency-enhancing innovations (12, 13) . We selected three general factors from the literature to explain the speed of diffusion. The first, favored by sociologists, is informational or attitudinal barriers; either potential adopters either do not know about the innovation’s benefits or they know that the innovation is useful but still fail to adopt. Two examples are considered: beta-blockers and naltrexone. We consider the contributing effect of a lack of marketing for each medication.
The second relates to financial and resource constraints on government funding. Our examples are multisystemic therapy (MST) and the IMPACT model for geriatric depression. The final category is the profitability of the treatment, where economic incentives can exert an independent influence on the speed of diffusion. Here, we consider the rapid diffusion of second-generation antipsychotics due to aggressive marketing. These factors are not mutually exclusive, and when all three are aligned—informational and resource barriers are low and profitability is high—new innovations with real clinical benefits diffuse in months rather than in years, as in the case of tetracycline in the 1950s (14) . These factors are not meant to be exhaustive; Essock et al. (15), for example, have identified a variety of other barriers in not-for-profit institutions such as skepticism about scientific methods or issues of professional or consumer authority in vying for control.

Informational and Attitudinal Barriers

Berwick (2) and others have called attention to the remarkably slow rate of diffusion for beta-blockers in the treatment of patients with acute myocardial infarction. Despite strong clinical evidence of effectiveness by 1985 (16), the median use (at the state level) of beta-blockers was still just 68% in 2000/2001 (17) . Why was diffusion so slow? Bradley et al. (18) found no single cause, but instead a variety of causes reflecting the professional opinions of the physicians, professional disagreement about the value of beta-blockage, or the lack of administrator or opinion leaders’ support. The rapid growth in the use of beta-blockers since the 1990s has occurred largely because of its adoption as a publicly available quality measure.
Naltrexone for the treatment of alcoholism is another example in which informational barriers matter. As of 2006, there were 29 published randomized, placebo-controlled trials of naltrexone for the treatment of alcoholism (19) . Various studies show reductions in the mean number of drinking days per week, the amount of alcohol consumed per drinking occasion, the risk of relapse to heavy drinking, and the severity of craving for alcohol.
Nevertheless, few patients in alcoholism treatment receive naltrexone, and the proportion may even be declining over time (20) . In 1992, epidemiologic data indicated that approximately 13.7 million adults in the U.S. met criteria for alcoholism (21), and in 2000, some 1.3 million Americans received treatment for alcoholism in medical care settings (22) . Yet only about 3%–13% of those in treatment received naltrexone (23) . Again, why the slow diffusion?
Mark et al. (23) found that both patients and physicians cited lack of information about the medication as the primary reason for low use—in the absence of a strong incentive to market medications that are available in generic preparations. Other reasons cited for low use included unfounded fears of addictive potential, confusion about indications, and limited patient demand and access to physicians. Also, within the “culture of abstinence” in the field of substance abuse treatment, some providers object to the use of any medications.

Financial Barriers

Multisystemic therapy (MST) is an intensive, in-home, family-based intervention for youths with severe antisocial and other clinical problems (24) . It was first developed and tested around 1990 and, with the help of a university-licensed technology transfer organization, has subsequently spread to 32 states and 10 countries (25) . The research evidence on MST shows that it consistently reduces out-of-home placements, antisocial behavior, and other clinical problems (26, 27) .
MST has spread faster and more extensively than any other evidence-based child intervention, currently to about 16,000 families a year. Nevertheless, adoption is small in relation to need, and many systems have decided not to adopt MST, with the major barriers the cost of training and financing the intervention (Medicaid reimbursement is often unavailable). Although MST has been associated with cost offsets (cost reductions along other dimensions), the responsibility for children with multiple problems is usually spread across multiple agencies and systems; hence the incentive is for agencies to shift the child to someone else’s budget. In fact, MST is more often funded by the juvenile justice system than by mental health agencies.
As well, there are other barriers; MST requires a relatively high degree of training, supervision, and continuity of workforce. Thus, start-up expenses are high, and skill requirements are at odds with those of the existing workforce. To enhance fidelity, MST implementation is controlled by the founders, which both encourages fidelity and hinders speed of adaptation to local circumstances and spread.
Another example is the IMPACT model for geriatric depression, which includes systematic diagnosis and outcome monitoring, stepped care based on an evidence-based algorithm, disease management with the help of a care manager, and shared decision-making in relation to psychosocial and pharmacological interventions (28) . The research on IMPACT is quite strong in terms of clinical outcomes (29), cost-effectiveness (30), and usability in a large system of managed care (31) . In many ways, IMPACT provides a model for evidence-based practice development, moving rapidly from efficacy to effectiveness to real-world implementation studies. Nonetheless, diffusion has thus far proceeded slowly with little uptake in most parts of the country and wide regional variation (http://impact-uw.org). As Unützer and colleagues (32) point out, IMPACT has been implemented only in a few specific areas and often because of serendipitous opportunities, such as unexpected funding generated by Proposition 63 in California (33) .

Profitability

It is perhaps not surprising that financial reimbursements and marketing should play a role in diffusion. As we pointed out in our introduction, only new medications under patent are especially profitable to market, so often there is limited information available to support dissemination and adoption of other types of treatment. In this section we consider one example: the rapid diffusion of second-generation antipsychotic medications. Standard treatment for schizophrenia includes antipsychotic medications as well as psychosocial interventions and ancillary services (34, 35) . Since the 1950s, antipsychotic medications have been demonstrated to be effective in treating episodes of psychosis in schizophrenia and other disorders and in preventing additional episodes of psychosis. During the 1990s, a series of “atypical” antipsychotics (also called second-generation antipsychotics) were approved for use in the U.S., and each one was introduced with the promise of greater efficacy and fewer side effects than “typical” (first-generation) antipsychotics. Prices for these new medications were over 10 times the price of customary treatments, but the pharmaceutical industry flooded practicing psychiatrists and the public with advertising, and advocates demanded access to (i.e., insurance payments for) the new medications. Early studies, largely funded by the pharmaceutical industry, indicated that the new “atypical” antipsychotics were remarkably effective (36) . As a consequence, expenditures for these second-generation medications in the U.S. rose steadily to $11.5 billion in 2006 (37) .
Government agencies agreed to pay for second-generation antipsychotics despite the lack of unbiased comparative evidence (38) . Because of constraints on mental health funding from Medicaid and state general funds, one unintended consequence of increased spending on new medications was that evidence-based and already poorly funded services, such as housing, vocational, and case management programs, were diminished (39) . Of importance, all of these changes occurred before well-designed trials compared the second-generation antipsychotics to the first-generation medications.
By 2007, in the minds of many investigators and policy analysts, the evidence had become clear that the new second-generation antipsychotic medications (besides clozapine, which is still rarely used) are no more efficacious in well-controlled trials and certainly no more effective in routine use than the older antipsychotics (e.g., 40, 41) . Moreover, the evidence continues to emerge that the new medications have different, but equally serious, side effects (42, 43) . In this case, we may conclude that the rapid adoption of the second-generation antipsychotics was hastened by the high degree of profitability of these medications, which helped to promote a belief among patients, families, practitioners, and mental health administrators that these drugs represented “best practice” treatments.
Similar examples can be found for other second-generation pharmaceuticals such as ezetimibe, used in the treatment of hyperlipidemia and a key component of Vytorin. Owing in part to active marketing efforts in the United States, ezetimibe was by 2006 four times more likely to be prescribed for lowering cholesterol in the U.S. compared to Canada (44) .

Next Steps: Research and Interventions

The examples of diffusion presented above hint at wide geographic variation in adoption, and so it is perhaps not surprising that these differences will be reflected in variations across regions at a point in time. Figure 1, from the Dartmouth Atlas of Cardiovascular Healthcare, shows the distribution of beta-blocker use among Medicare patients with acute myocardial infarction by hospital referral region, the basic regional unit of analysis in Dartmouth Atlas studies, in 1994/1995 (45) . Each dot corresponds to a hospital referral region, and the vertical axis shows the percentage of ideal acute myocardial infarction patients treated with beta-blockers at discharge. Note the wide range of adoption, with a few regions using beta-blockers for fewer than 20% of patients, while other regions are using beta-blockers for more than 90% of ideal patients (i.e., those for whom there is no evidence for ruling out the use of beta-blockers). Had beta-blockers diffused rapidly, we would have observed all the regions piled up above 90%—leading to little or no regional variation. Thus, variations at a point in time can be very informative about past regional patterns of diffusion.
Figure 1. Percent of “Ideal” Patients in Each Hospital Referral Region (HRR) Receiving Beta Blockers at Discharge Following Acute Myocardial Infarction (1994–1995) a
a Source: Dartmouth Atlas of Cardiovascular Healthcare. Each dot corresponds to an HRR. Thus, during 1994/1995, there were three regions in the U.S. where beta-blocker use was above 90%, while there were four regions in the U.S. where beta-blocker use was below 14%. Figure used with permission of The Dartmouth Atlas of Cardiovascular Health Care.
For this reason, we suggest that regional variation in the provision of effective mental health care can be a valuable marker in judging the speed of diffusion. Aside from a few studies with sometimes limited data on regional variation (4650), there is little systematic evidence for how treatment patterns differ across the U.S. in the treatment of mental illness. The evidence is certainly suggestive of large variations; Wang et al. (46), for example, found that the percentage of patients with serious mental illness who received any mental health treatment ranged from just 27.3% in the Northeast to 43.5% in the West. The study of regional variations is also complicated by differences in the underlying incidence of disease; rates of major depression varied across states from 6.7% to 10.1% (51) .
A first step would be to use a national database to develop population-based measures of resource utilization and drug treatments stratified by disease type. Examples of such databases include those maintained by the U.S. Department of Veterans Affairs (50), the Medicare data under the Social Security Disability Insurance (SSDI) program, or state-level Medicaid data. As noted above, the challenges to creating comparable measures across regions are to develop a comprehensive sample of those with mental illness and to risk-adjust adequately to account for differences in the severity of disease across regions.
Another approach to understanding better the causes of slow (or rapid) diffusion is to implement an intervention designed either to hasten (or depress) the speed of diffusion. One example would be to compare several approaches for encouraging the rapid diffusion of an effective medication such as naltrexone in general mental health practice. While we know some things about implementing effective treatments (12, 5259 ), systematic differences could still exist across regions with respect to physician and patient adoption.
For example, Skinner and Staiger (60) found a strong correlation between the adoption of hybrid corn by farmers in the 1930s and 1940s and the adoption of beta-blockers in 2000/2001. They suggested one cause for the close correlation: social capital, defined as the presence (or absence) of social networks associated with civic participation, educational attainment, and cooperation among citizens (61) . Social capital is unusually high in Iowa (a rapid adopter of both hybrid corn and beta-blockers) and low in Arkansas (a slow adopter of both). Similarly, Williams (62) found a strong (negative) association between social capital and measures of profitable health interventions with potentially low marginal benefit, such as cesarean sections and hospital days in the last 2 years of life.
To sum up, we have identified several factors that affect the process of diffusion of mental health treatments. We suggest there is considerable potential in documenting and understanding regional variations in mental health treatments, if only to understand why some regions or institutions adopt so slowly (or so rapidly). Similarly, intervening in our existing mental health care system by addressing educational or attitudinal factors, financial barriers, or profitability can help to inform how each of these factors can be harnessed to improve the quality of care, or whether the best approach is to customize the intervention to the region or institution. Valuable lessons are also provided by interventional studies in other clinical areas, some of which foundered on organizational barriers to change when trying to implement evidence-based improvements (63) . In the longer term, the objective is to better understand and overcome barriers that keep us from attaining best-quality mental health care in the United States.

Footnotes

Address reprint requests to Dr. Drake, Psychiatric Research Center, 2 Whipple Place, Suite 202, Lebanon, NH, 03766; [email protected] (e-mail). Commentary accepted for publication April 2008 (doi: 10.1176/appi.ajp.2008.08030334).
The authors report no competing interests.
Supported by the McArthur Foundation Network on Mental Health Policy Research, the Robert Wood Johnson Foundation, the West Foundation, and the National Institute on Aging (PO1-AG19783).
The authors thank Sherry Glied for helpful comments on an earlier draft of this manuscript

References

1.
Newhouse JP: Medical care costs: how much welfare loss? J Econ Perspect 1992; 6:3–21
2.
Berwick DM: Disseminating innovations in health care. JAMA 2003; 289:1969–1975
3.
Rogers EM: Diffusion of Innovations, 5th ed. New York, Free Press, 2003
4.
Wennberg JE: Understanding variations in healthcare delivery. N Engl J Med 1999; 340:52–53
5.
New Freedom Commission on Mental Health: Achieving the Promise: Transforming Mental Health Care in America: Final Report. U.S. Department of Health and Human Services Publication SMA-03–3832. Rockville, Md, DHHS, 2003
6.
U.S. Department of Health and Human Services: Mental Health: A Report of the Surgeon General. Rockville, Md, U.S. Department of Health and Human Services, Substance Abuse and Mental Health Services Administration, Center for Mental Health Services, National Institutes of Health, National Institute of Mental Health, 1999
7.
Wang PS, Lane M, Olfson M, Pincus HA, Wells KB, Kessler RC: Twelve-month use of mental health services in the United States. Arch Gen Psychiatry 2005; 62:629–640
8.
Frank R, Glied S: Better But Not Well. Baltimore, Md, Johns Hopkins University Press, 2006
9.
Lehman AF, Steinwachs DM, Co-investigators of the PORT Project: Patterns of usual care for schizophrenia: initial results from the Schizophrenia Patient Outcomes Research Team (PORT) Studies. Schizophr Bull 1998; 24:11–20
10.
Griliches Z: Hybrid corn: an exploration in the economics of technological change. Econometrica 1957; 25:501–522
11.
Havens AE, Rogers E: Adoption of hybrid corn: profitability, and the interaction effect. Rural Sociol 1961; 26:409–414
12.
Greenhalgh T, Robert G, MacFarlane F, Bate P, Kyriakidou O: Diffusion of innovations in service organizations: systematic review and recommendations. Milbank Q 2004; 82:581–629
13.
Tolbert PS, Zucker LG: Institutional sources of change in the formal structure of organizations: the diffusion of civil service reform 1880–1935. Admin Sci Quarterly 1983; 28:22–39
14.
Coleman JS, Katz E, Menzel H: Medical Innovation: A Diffusion Study. New York, Bobbs-Merrill, 1966
15.
Essock SM, Goldman HH, Van Tosh L, Anthony WA, Appell CR, Bond GR, Dixon L, Dunakin L, Ganju V, Gorman P, Ralph RO, Rapp CA, Teague GB, Drake RE: Evidence-based practice: setting the context and responding to concerns. Psychiatr Clin North Am 2003; 26:919–938
16.
Yusuf S, Peto R, Lewis J, Collins R, Sleight P: Beta blockade during and after myocardial infarction: an overview of the randomized trials. Prog Cardiovasc Dis 1985; 27:335–371
17.
Jencks SF, Huff ED, Cuerdon T: Change in the quality of care delivered to Medicare beneficiaries 1998–1999 to 2000–2001. JAMA 2003; 289:305–312
18.
Bradley EH, Holmboe ES, Mattera JA, Roumanis SA, Radford MJ, Krumholz HM: A qualitative study of increasing b-blocker use after myocardial infarction: why do some hospitals succeed? JAMA 2001; 285:2604–2611
19.
Pettinati HM, O’Brien CP, Rabinowitz AR, Wortman SM, Oslin DW, Kampman KM, Dackis CA: The status of naltrexone in the treatment of alcohol dependence: specific effects on heavy drinking. J Clin Psychopharm 2006; 26:610–625
20.
Ducharme LJ, Knudsen HK, Roman PM: Trends in the adoption of medications for alcohol dependence. J Clin Psychopharm 2006; 26:S13–S19
21.
Grant BF: Prevalence and correlates of drug use and DSM-IV drug dependence in the United States: results of the national longitudinal alcohol epidemiologic survey. J Subst Abuse Treat 1996; 8:195–210
22.
Substance Abuse and Mental Health Services Administration: National Household Survey on Drug Abuse. http://www.oas.xamsa.gov/nhsda.htm
23.
Mark TL, Kranzler HR, Poole VH, Hagen CA, McLeod C, Crosse S: Barriers to the use of medications to treat alcoholism. Am J Addict 2003; 12:281–294
24.
Henggeler SW, Schoenwald SK, Borduin CM, Rowland MD, Cunningham PB: Multisystemic Treatment of Antisocial Behavior in Children and Adolescents. New York, Guilford, 1998
25.
Schoenwald SK: Toward evidence-based transport of evidence-based treatments: MST as an example. J Child Adolesc Subst Abuse Treat (in press)
26.
Henggeler SW, Melton GB, Smith LA: Family preservation using multisystemic therapy: an effective alternative to incarcerating serious juvenile offenders. J Consult Clin Psychol 1992; 60:953–961
27.
Henggeler SW, Melton GB, Smith LA, Schoenwald SK, Hanley J: Family preservation using multisystemic therapy: long-term follow-up to a clinical trial with serious juvenile offenders. J Child Family Studies 1993; 2:283–293
28.
Unützer J, Katon W, Williams JW, Callahan CM, Harpole L, Hunkeler EM, Hoffing M, Arean P, Hegel MT, Schoenbaum M, Oishi SM, Langston CA: Improving primary care for depression in late life: the design of a multicenter randomized trial. Med Care 2001; 39:785–799
29.
Unützer J, Katon W, Callahan CM, Williams JW, Hunkeler E, Harpole L, Hoffing M, Della Penna RD, Noel PH, Lin EH, Arean PA, Hegel MT, Tang L, Belin TR, Oishi S, Langston: Collaborative care management of late-life depression in the primary care setting. JAMA 2002; 288:2836–2845
30.
Katon W, Robinson P, VonKorff M, Lin E, Bush T, Ludman E, Simon G, Walker CA: A multifaceted intervention to improve treatment of depression in primary care. Arch Gen Psychiatry 2005; 53:924–932
31.
Grypma L, Haverkamp R, Little S, Unutzer J: Taking an evidence-based model of depression care from research to practice: making lemonade out of depression. Gen Hosp Psychiatry 2006; 28:101–107
32.
Unützer J, Powers D, Katon W, Langston C: From establishing an evidence-based practice to implementation in real-world settings: IMPACT as a case study. Psychiatr Clin North Am 2005; 28:1079–1092
33.
Scheffler RM, Adams N: Millionaires and mental health: Proposition 63 in California. Health Affairs 2005; W5:212–224
34.
Lehman AF, Kreyenbuhl J, Buchanan RW, Dickerson FB, Dixon LB, Goldberg R, Green-Paden LD, Tenhula WN, Boerescu D, Tek C, Sandson N, Steinwachs DM: The schizophrenia patient outcomes research team (PORT): updated treatment recommendation 2003. Schizophr Bull 2004; 30:193–217
35.
Lehman AF, Steinwachs DM, and the Co-Investigators of the PORT Project: Translating research into practice: the schizophrenia Patient Outcomes Research Team (PORT) treatment recommendations. Schizophr Bull 1998; 24:1–10
36.
Davis JM, Chen N, Glick ID: A meta-analysis of the efficacy of second-generation antipsychotics. Arch Gen Psychiatry 2003; 60:553–564
37.
Buono D: IMS study finds decline in prescription drug market growth. Drug Store News. April 21, 2008. Findarticles.com, 13 Aug, 2008. http://findarticles.com/articles/mi_m3374/P/is_5_30ai_n25407255
38.
Owens DC: What CATIE did: some thoughts on implications deep and wide. Psychiatr Serv 2008; 59:530–533
39.
Rosenheck RA, Leslie DL, Doshi JA: Second-generation antipsychotics: cost-effectiveness, policy options, and political decision making. Psychiatr Serv 2008; 59:515–520
40.
Jones PB, Barnes TRE, Davies L, Dunn G, Lloyd H, Hayhurst KP, Murray RM, Markwick A, Lewis SW: Randomized controlled trial of the effect on quality of life of second-vs-first-generation antipsychotic drugs in schizophrenia. Arch Gen Psychiatry 2006; 63:1079–1087
41.
Lieberman JA, Stroup TS, McEvoy JP, Swartz MS, Rosenheck RA, Perkins DO, Keefe SE, Davis SM, Davis CE, Lebowitz BD, Severe J, Hsiao JK: Effectiveness of antipsychotic drugs in patients with chronic schizophrenia. N Engl J Med 2006; 353:1209–1223
42.
Marder SR, Essock SM, Miller AL, Buchanan RW, Casey DE, Davis JM, Kane JM, Lieberman JA, Schooler NR, Covell NR, Stroup S, Weissman EM, Wirshing DA, Hall CS, Pogach L, Pi-Sunyer X, Bigger JT Jr, Friedman A, Kleinberg D, Tevich SJ, Davis B, Shon S: Physical health monitoring of patients with schizophrenia. Am J Psychiatry 2004; 161:1334–1349
43.
McEvoy JP, Meyer J, Goff D, Nasrallah H, Davis SM, Sullivan L, Meltzer HY, Stroup TS, Lieberman JA: Prevalence of the metabolic syndrome in patients with schizophrenia: results from CATIE and comparison with national estimates from NHANES III. Schizophr Res 2005; 80:19–32
44.
Jackevicius CA, Tu JV, Ross JS, Ko DT, Krumholz HM: Use of ezetimibe in the United States and Canada. N Engl J Med 2008; 358:1819–1828
45.
Wennberg D (ed): Dartmouth Atlas of Cardiovascular Healthcare. Dartmouth Medical School and the American Hospital Association, 2000
46.
Wang PS, Demier O, Kessler RC: Adequacy of treatment for serious mental illness in the United States. Am J Public Health 2002; 92:92–98
47.
Semke J, Perdue S: Regional patterns of service for individuals with severe mental illness. Admin Policy Ment Health Serv Res 1998; 25:541–547
48.
Becker T, Vázquez-Barquero JL: The European perspective of psychiatric reform. Acta Psychiatrica Scandinavica 2001; 104:8–14
49.
Kramer TL, Daniels AS, Zieman GL, Williams C, Dewan NA: Psychiatric practice variations in the diagnosis and treatment of major depression. Psychiatr Serv 2000; 51:336–340
50.
Ashton C: Geographic variations in utilization rates in Veterans Affairs Hospitals and clinics. N Engl J Med 1999; 340:32–39
51.
Wright D, Sathe N, Spagnola K: State estimates of substance use from the 2004–2005 National Surveys on Drug Use and Health: DHHS Publication SMA 07-4235, NSDUH Series H-31. Rockville, Md, Substance Abuse and Mental Health Services Administration, Office of Applied Sciences, 2007
52.
Baer JS, Ball SA, Campbell BK, Miele GM, Schoener EP, Tracy K: Training and fidelity monitoring of behavioral interventions in multi-site addictions research. Drug Alcohol Depend 2007; 87:107–118
53.
Dietrich AJ, Oxman TE, Williams JW, Schulberg HC, Bruce ML, Lee PW, Barry S, Raue PJ, Lefever JJ, Heo M, Rost K, Kroenkie K, Gerrity M, Nutting PA: Re-engineering systems for the treatment of depression in primary care: cluster randomized controlled trial. Br Med J 2004; 329:602–605
54.
Dopson S, Fitzgerald L, Ferlie E, Gabby J, Locock L: No magic targets: changing clinical practice to become more evidence based. Manage Rev 2002; 37:35–47
55.
Gotham HJ: Advancing the implementation of evidence-based practices into clinical practice: How do we get there from here? Prof Psycho Res Pract 2006; 37:606–613
56.
Hermann RC: Improving Mental Healthcare: A Guide to Measurement–Based Quality Improvement. Washington, DC, American Psychiatric Press, 2005
57.
McHugo GM, Drake RE, Whitley R, Bond GR, Campbell K, Rapp CA, Goldman HH, Lutz W, Finnerty M: Fidelity outcomes in the National Implementing Evidence-Based Practices Project. Psychiatr Serv 2007; 58:1279–1284
58.
Miller WR, Sorensen JL, Selzer JA, Brigham GS: Disseminating evidence-based practices in substance abuse treatment: a review with suggestions. J Subst Abuse Treat 2006; 31:25–39
59.
Shojania KG, Grimshaw JM: Evidence-based quality improvement: the state of the science. Health Affairs 2005; 24:138–150
60.
Skinner J, Staiger D: Technological diffusion from hybrid corn to beta blockers, in Hard-to-Measure Goods and Services: Essays in Honor of Zvi Griliches. Edited by Berndt E, Hulten CM. Chicago, University of Chicago Press and NBER, 2007
61.
Putnam R: Bowling Alone: The Collapse and Revival of American Community. New York, Simon & Schuster, 2000
62.
Williams J: Geographic variations in health care utilization: the effects of social capital and self-interest. AARP Working Paper, Washington, DC, 2007
63.
Landon BE, Wilson IB, McInnes K, Landrum MB, Hirschhorn L, Marsden PV, Gustafson D, Cleary PD: Effects of a quality improvement collaborative on the outcome of care of patients with HIV infection: the EQHIV study. Ann Intern Med 2004; 140:887–896

Information & Authors

Information

Published In

Go to American Journal of Psychiatry
Go to American Journal of Psychiatry
American Journal of Psychiatry
Pages: 1385 - 1392
PubMed: 18981070

History

Published online: 1 November 2008
Published in print: November, 2008

Authors

Details

Robert Drake, M.D., Ph.D.
Jonathan Skinner, Ph.D.
Howard H. Goldman, MD., Ph.D.

Metrics & Citations

Metrics

Citations

Export Citations

If you have the appropriate software installed, you can download article citation data to the citation manager of your choice. Simply select your manager software from the list below and click Download.

For more information or tips please see 'Downloading to a citation manager' in the Help menu.

Format
Citation style
Style
Copy to clipboard

View Options

View options

PDF/EPUB

View PDF/EPUB

Login options

Already a subscriber? Access your subscription through your login credentials or your institution for full access to this article.

Personal login Institutional Login Open Athens login
Purchase Options

Purchase this article to access the full text.

PPV Articles - American Journal of Psychiatry

PPV Articles - American Journal of Psychiatry

Not a subscriber?

Subscribe Now / Learn More

PsychiatryOnline subscription options offer access to the DSM-5-TR® library, books, journals, CME, and patient resources. This all-in-one virtual library provides psychiatrists and mental health professionals with key resources for diagnosis, treatment, research, and professional development.

Need more help? PsychiatryOnline Customer Service may be reached by emailing [email protected] or by calling 800-368-5777 (in the U.S.) or 703-907-7322 (outside the U.S.).

Media

Figures

Other

Tables

Share

Share

Share article link

Share