Improving the efficiency of psychological treatment using outcome feedback technology

Delgadillo, Jaime; Overend, Karen; Lucock, Mike; Groom, Martin; Kirby, Naomi; McMillan, Dean; Gilbody, Simon; Lutz, Wolfgang; Rubel, Julian; Jong, Kim de

doi:10.1016/j.brat.2017.09.011

Cited by 69 publications

(67 citation statements)

References 33 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…First of all, ROM was not primarily implemented for accountability but predominantly intended as a beneficial adjunct to treatment (de Beurs, FIGURE 2 Scatterplots of bias due to selective inclusion, selective attrition, and bias due to all combined by response percentage. ROM can also lead to more efficient treatment delivery as outcome feedback may reduce treatment length (Delgadillo et al, 2017). Research has shown that ROM by itself can lead to better results (Boswell, Kraus, Miller, & Lambert, 2013), especially for patients at risk for treatment failure (Lambert, 2010).…”

Section: Discussionmentioning

confidence: 99%

“…Treatment failures tend to be overlooked by therapists (Hannan et al, 2005). ROM can also lead to more efficient treatment delivery as outcome feedback may reduce treatment length (Delgadillo et al, 2017). Furthermore, ROM grants patients a more active role in their own recovery (Patel, Bakken, & Ruland, 2008).…”

Section: Statistical Correction For Both Selective Sampling Mechanismmentioning

confidence: 99%

See 1 more Smart Citation

Bias through selective inclusion and attrition: Representativeness when comparing provider performance with routine outcome monitoring data

Beurs

Warmerdam²,

Twisk

2019

Clin Psychology and Psychoth

View full text Add to dashboard Cite

Background Observational research based on routine outcome monitoring is prone to missing data, and outcomes can be biased due to selective inclusion at baseline or selective attrition at posttest. As patients with complete data may not be representative of all patients of a provider, missing data may bias results, especially when missingness is not random but systematic. Methods The present study establishes clinical and demographic patient variables relevant for representativeness of the outcome information. It applies strategies to estimate sample selection bias (weighting by inclusion propensity) and selective attrition bias (multiple imputation based on multilevel regression analysis) and estimates the extent of their impact on an index of provider performance. The association between estimated bias and response rate is also investigated. Results Provider‐based analyses showed that in current practice, the effect of selective inclusion was minimal, but attrition had a more substantial effect, biasing results in both directions: overstating and understating performance. For 22% of the providers, attrition bias was estimated to be in excess of 0.05 ES. Bias was associated with overall response rate (r = .50). When selective inclusion and attrition bring providers' response below 50%, it is more likely that selection bias increased beyond a critical level, and conclusions on the comparative performance of such providers may be misleading. Conclusions Estimates of provider performance were biased by selection, especially by missing data at posttest. Results on the extent and direction of bias and minimal requirements for response rates to arrive at unbiased performance indicators are discussed.

show abstract

Section: Discussionmentioning

confidence: 99%

Section: Statistical Correction For Both Selective Sampling Mechanismmentioning

confidence: 99%

Bias through selective inclusion and attrition: Representativeness when comparing provider performance with routine outcome monitoring data

Beurs

Warmerdam²,

Twisk

2019

Clin Psychology and Psychoth

View full text Add to dashboard Cite

show abstract

“…109,110 The Northern Improving Access to Psychological Therapies (IAPT) Practice Research Network is a collaboration of psychotherapy service providers and research institutes promoting the use of data for service provision and research. 111,112 Importantly, all IAPT services in the UK, which treat over 500,000 patients each year, collect common outcome measures at each session, consisting of short patient-completed questionnaires, providing data on therapy progression. IAPT data have already delivered insights on the utility of a clinician-support system that alerts therapists to patients who are not responding as well as expected, and ongoing work is using patient characteristics to guide treatment choice.…”

Section: Treatment Evaluationmentioning

confidence: 99%

How data science can advance mental health research

et al. 2018

Self Cite

View full text Add to dashboard Cite

Accessibility of powerful computers and availability of so-called "big data" from a variety of sources means that data science approaches are becoming pervasive. However, their application in mental health research is often considered to be at an earlier stage than in other areas despite the complexity of mental health and illness making such a sophisticated approach particularly suitable. In this article we discuss current and potential applications of data science in mental health research using the UK Clinical Research Collaboration classification: underpinning research; aetiology; detection and diagnosis; treatment development; treatment evaluation; disease management; and health services research. We demonstrate that data science is already being widely applied in mental health research, but there is much more to be done now and in the future. The possibilities for data science in mental health research are substantial.

show abstract

“…There is evidence that routine outcome monitoring (ROM) has a positive influence on treatment efficacy (Lambert, ) and efficiency (Delgadillo et al., ) in mental health care. Routine measurements can be used in individual treatments by clinicians and patients to determine progress and the necessity to adapt interventions (Van, Dekker, Peen, van Aalst, & Schoevers, ), but they may also be used to evaluate the overall effectiveness of treatment programs, both within the same patient group as across diagnoses.…”

Section: Introductionmentioning

confidence: 99%

Comparative responsiveness of generic versus disorder-specific instruments for depression: An assessment in three longitudinal datasets

et al. 2018

View full text Add to dashboard Cite

show abstract

Improving the efficiency of psychological treatment using outcome feedback technology

Abstract: After adopting OF into their practice, this group of therapists attained similar clinical outcomes but within a shorter space of time and at a reduced average cost per treatment episode. We conclude that OF can improve the efficiency of stepped care.

Cited by 69 publications

References 33 publications

Bias through selective inclusion and attrition: Representativeness when comparing provider performance with routine outcome monitoring data

Bias through selective inclusion and attrition: Representativeness when comparing provider performance with routine outcome monitoring data

How data science can advance mental health research

Comparative responsiveness of generic versus disorder-specific instruments for depression: An assessment in three longitudinal datasets

Contact Info

Product

Resources

About