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Deisenhofer AK, Barkham M, Beierl ET, Schwartz B, Aafjes-van Doorn K, Beevers CG, Berwian IM, Blackwell SE, Bockting CL, Brakemeier EL, Brown G, Buckman JEJ, Castonguay LG, Cusack CE, Dalgleish T, de Jong K, Delgadillo J, DeRubeis RJ, Driessen E, Ehrenreich-May J, Fisher AJ, Fried EI, Fritz J, Furukawa TA, Gillan CM, Gómez Penedo JM, Hitchcock PF, Hofmann SG, Hollon SD, Jacobson NC, Karlin DR, Lee CT, Levinson CA, Lorenzo-Luaces L, McDanal R, Moggia D, Ng MY, Norris LA, Patel V, Piccirillo ML, Pilling S, Rubel JA, Salazar-de-Pablo G, Schleider JL, Schnurr PP, Schueller SM, Siegle GJ, Uher R, Watkins E, Webb CA, Wiltsey Stirman S, Wynants L, Youn SJ, Zilcha-Mano S, Lutz W, Cohen ZD. Implementing precision methods in personalizing psychological therapies: Barriers and possible ways forward. Behav Res Ther 2024; 172:104443. [PMID: 38086157 DOI: 10.1016/j.brat.2023.104443] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/23/2023] [Revised: 11/21/2023] [Accepted: 11/27/2023] [Indexed: 12/26/2023]
Affiliation(s)
| | | | | | | | | | | | | | | | - Claudi L Bockting
- AmsterdamUMC, Department of Psychiatry, Research Program Amsterdam Public Health and Centre for Urban Mental Health, University of Amsterdam, the Netherlands
| | | | | | | | | | | | | | - Kim de Jong
- Leiden University, Institute of Psychology, USA
| | | | | | | | | | | | | | - Jessica Fritz
- University of Cambridge, UK; Philipps University of Marburg, Germany
| | | | - Claire M Gillan
- School of Psychology, Trinity College Institute for Neuroscience, And Global Brain Health Institute, Trinity College Dublin, USA
| | | | | | | | | | | | | | | | | | | | | | | | - Mei Yi Ng
- Florida International University, USA
| | | | | | | | | | | | | | - Jessica L Schleider
- Stony Brook University and Feinberg School of Medicine Northwestern University, USA
| | - Paula P Schnurr
- National Center for PTSD and Geisel School of Medicine at Dartmouth, USA
| | | | | | | | | | | | | | | | - Soo Jeong Youn
- Reliant Medical Group, OptumCare and Harvard Medical School, USA
| | | | | | - Zachary D Cohen
- University of California, Los Angeles and University of Arizona, USA.
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Knights J, Bangieva V, Passoni M, Donegan ML, Shen J, Klein A, Baker J, DuBois H. A framework for precision "dosing" of mental healthcare services: algorithm development and clinical pilot. Int J Ment Health Syst 2023; 17:21. [PMID: 37408006 DOI: 10.1186/s13033-023-00581-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/19/2022] [Accepted: 05/18/2023] [Indexed: 07/07/2023] Open
Abstract
BACKGROUND One in five adults in the US experience mental illness and over half of these adults do not receive treatment. In addition to the access gap, few innovations have been reported for ensuring the right level of mental healthcare service is available at the right time for individual patients. METHODS Historical observational clinical data was leveraged from a virtual healthcare system. We conceptualize mental healthcare services themselves as therapeutic interventions and develop a prototype computational framework to estimate their potential longitudinal impacts on depressive symptom severity, which is then used to assess new treatment schedules and delivered to clinicians via a dashboard. We operationally define this process as "session dosing": 497 patients who started treatment with severe symptoms of depression between November 2020 and October 2021 were used for modeling. Subsequently, 22 mental health providers participated in a 5-week clinical quality improvement (QI) pilot, where they utilized the prototype dashboard in treatment planning with 126 patients. RESULTS The developed framework was able to resolve patient symptom fluctuations from their treatment schedules: 77% of the modeling dataset fit criteria for using the individual fits for subsequent clinical planning where five anecdotal profile types were identified that presented different clinical opportunities. Based on initial quality thresholds for model fits, 88% of those individuals were identified as adequate for session optimization planning using the developed dashboard, while 12% supported more thorough treatment planning (e.g. different treatment modalities). In the clinical pilot, 90% of clinicians reported using the dashboard a few times or more per member. Although most clinicians (67.5%) either rarely or never used the dashboard to change session types, numerous other discussions were enabled, and opportunities for automating session recommendations were identified. CONCLUSIONS It is possible to model and identify the extent to which mental healthcare services can resolve depressive symptom severity fluctuations. Implementation of one such prototype framework in a real-world clinic represents an advancement in mental healthcare treatment planning; however, investigations to assess which clinical endpoints are impacted by this technology, and the best way to incorporate such frameworks into clinical workflows, are needed and are actively being pursued.
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Affiliation(s)
- Jonathan Knights
- Mindstrong, Inc., 101 Jefferson Drive, Suite 228, Menlo Park, CA, 94025, USA.
| | - Victoria Bangieva
- Mindstrong, Inc., 101 Jefferson Drive, Suite 228, Menlo Park, CA, 94025, USA
| | - Michela Passoni
- Mindstrong, Inc., 101 Jefferson Drive, Suite 228, Menlo Park, CA, 94025, USA
| | - Macayla L Donegan
- Mindstrong, Inc., 101 Jefferson Drive, Suite 228, Menlo Park, CA, 94025, USA
| | - Jacob Shen
- Mindstrong, Inc., 101 Jefferson Drive, Suite 228, Menlo Park, CA, 94025, USA
| | - Audrey Klein
- Mindstrong, Inc., 101 Jefferson Drive, Suite 228, Menlo Park, CA, 94025, USA
| | - Justin Baker
- Mindstrong, Inc., 101 Jefferson Drive, Suite 228, Menlo Park, CA, 94025, USA
| | - Holly DuBois
- Mindstrong, Inc., 101 Jefferson Drive, Suite 228, Menlo Park, CA, 94025, USA
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Aafjes-van Doorn K, de Jong K. How to make the most of routine outcome monitoring (ROM): A multitude of clinical decisions and nuances to consider. J Clin Psychol 2022; 78:2054-2065. [PMID: 36041193 DOI: 10.1002/jclp.23438] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/05/2022] [Accepted: 08/11/2022] [Indexed: 11/07/2022]
Abstract
Routine outcome monitoring (ROM) involves the use of patient-reported standardized outcome measures to monitor progress throughout the course of treatment, followed by feedback of the patient's scores to the therapist. The potential benefits of ROM have been established, however, from our own experiences, we know that the implementation in clinical practice can be challenging. We therefore wanted to explore in more detail exactly how we might be able to apply ROM in difficult clinical contexts. The inspiring case illustrations in this issue of Journal of Clinical Psychology: In Session highlight the heterogeneity in ROM systems, and the way in which ROM can be used in treatment. Just as there are many ways of interpreting a survey data-point, there are also many ways in which ROM may be used to complement the treatment and supervision. Whether or not ROM is implemented may partly be determined by clinic policies and routines, but there remain a multitude of clinical decisions that require careful consideration by the individual therapist. To complement the evidence supporting the benefits of using ROM, further empirical support and clinical guidance is needed on how exactly therapists are to use ROM in their work and how ROM may be used in evidence-based practice. We make suggestions for additional uses of ROM for deliberate practice, and teletherapy practice, and look toward novel ways of assessing progress in the near future.
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Affiliation(s)
| | - Kim de Jong
- Institute of Psychology, Leiden University, Leiden, the Netherlands
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