Technology and Mental Health

A Clinician's Guide to Improving Outcomes

Greg M. Reger

• Publisher: Routledge
• Year: 2020
• eBook ISBN: 9780429670329
• Topic: Psychology
• Subtopic: Mental Health in Psychology
• Index: Psychology

Technology and Mental Health provides mental health clinicians with expert, practical, clinical advice on the questions and considerations associated with the adoption of mental health technology tools in the computer age.

Increasingly, clinicians want to use technology to provide clients support through smartphones and mobile applications or to reach clients in remote or rural areas. However, using these tools in practice raises many practical and ethical questions. The book explains current technological developments in therapy, including mobile apps, telemental health, and virtual reality programs. Each chapter gives real-world guidance on adopting and using technology interventions, and the book spans a wide range of populations. Providers are introduced to the evidence supporting various technology-based interventions and areas for future development. Combining theory, research, and case studies, this practical guide teaches clinicians how to integrate technology into therapeutic interventions with clients.

1 Enabling Behavioral Health Measurement-Based Care With Technology

Millard D. Brown and Jamie T. Carreno-Davidson

The behavioral health (BH) field has long been challenged by changing definitions of mental disorders, imprecise descriptions of mental phenomena, a wide variety of treatment modalities, and a broad range of training levels of BH providers. These challenges have made it difficult to consider measuring outcomes in BH treatment efforts in community settings. However, an ever-deepening body of literature has demonstrated the value of many therapeutic modalities while also finding ubiquitous common therapy factors that correlate highly with positive outcomes (Lambert & Barley, 2001). The accelerating pace and rapidly falling costs of technology now bring an unprecedented opportunity to rapidly improve the quality of BH care delivery in all communities and potentially open up new funding opportunities for BH programs by utilizing technology to implement measurement-based care (MBC) practices.

The impact of MBC on improving treatment effectiveness and efficiency is growing. Research teams led by Drs. Trivedi, Miller, and Lambert have shown how MBC practices can markedly increase the probability of response to treatment efforts (see discussion later in the chapter). One of the best-designed studies to look at MBC using medication in a depressed sample showed marked improvements in response and remission rates (Guo et al., 2015). Brickman et al. have shown that structured outcome feedback to clinicians also improves outcomes in youth (2011). However, not all research in this area has shown improvement, and more work is needed to determine key MBC implementation elements to ensure improved outcomes (Hansson, Rundberg, Österling, Öjehagen, & Berglund, 2013; Knaup, Koesters, Schoefer, Becker, & Puschner, 2009).

U.S. Army experience with a large MBC practice implementation—The U.S. Army medical leadership began planning to rebuild its BH system in 2008, as the traditional system was straining under the weight of five years of war. Over the next five years, a BH Service Line was created standardizing funding, structure, and implementation of 12 core program efforts across 50+ Army hospitals and clinics. One of the key initiatives involved building an MBC process that would support real-time care delivery and feed aggregated data to leadership to inform program effectiveness. The MBC system we built is called the Behavioral Health Data Portal (BHDP). With the support of senior Army medical leadership, the Army behavioral health team built the BHDP based on lessons learned from prior prototypes and started program implementation in 2012. Within six months, 30+ bases started using the BHDP. Within 12 months, all 50+ hospitals and clinics had started using BHDP in their BH clinics with over 30,000 questionnaires completed monthly. By 2014, we had collected questionnaires of standard clinical data and clinical measures from over 600,000 appointments. As of late 2019, we have over 5.6 million questionnaires in the BHDP, and the system is now used in BH clinics across the Army, Navy, and Air Force.

Army BH leadership uses aggregated BHDP data to monitor outcome tracking across all 50+ BH clinics for key illnesses like depressive-, anxiety-, and trauma-related disorders. The Army BH Service Line can now review what treatment actions and courses of care are most beneficial to our patients, thus further informing the ongoing development of our system of care. This capability is a key component to building a robust data-informed learning organization spanning local clinics and hospitals and up to senior Army medicine leadership. We will be detailing many lessons learned from our efforts in building and fielding an MBC program across the Army in the rest of this chapter as a means of encouraging other health systems to implement MBC practices.

We will first explore the evidence for MBC in BH care delivery. We will then discuss how technology use is critical to maximizing the full potential of MBC efforts. Using our lessons learned from real-world MBC implementation efforts over the past ten years, we will detail core implementation principles that are necessary to consider for successful MBC implementation efforts. Finally, we will explore common concerns about MBC, highlight emerging technologies, and finish with a review of how MBC can be the key link to accessing larger funding streams needed to transform how BH care is delivered.

Throughout this chapter, we will use the term MBC utilized by Dr. Scott and colleagues to refer to a procedure that can be broadly defined as the use of systematic data collection to monitor client progress and directly inform care decisions (Scott & Lewis, 2015). The data collected during the initial interview is used as an aid in developing a more complete diagnostic picture that includes supporting initial diagnostic specificity and establishing a clinical severity baseline. Additional measurements are utilized on a regular basis throughout an episode of care to monitor real-time progress toward treatment goals, to inform whether treatment efforts need to be adjusted, and to determine when treatment is successful.

Why Should We Use MBC?

Diagnostic support and increased accuracy—A fully implemented MBC system has the ability to markedly improve the consistency and quality of diagnoses within a clinical setting. Historically, diagnostic inter-rater agreement between clinicians has been as low as 45% when standardized tools have not been used (Basco et al., 2000). This lack of diagnostic precision may lead to increased diagnostic errors of commission and omission that will subsequently impact the efficacy of any treatment efforts and threaten the crucial establishment of a strong therapeutic alliance. Unrecognized comorbid disorders can easily derail the best treatment efforts. When providers attempt to collect interview data, they are often unaware of how the phrasing, cadence, and tone utilized can affect the patient’s responses and thus potentially affect the accuracy and completeness of their data collection. Further, provider nonverbal behaviors can also impact their interview data collection. Despite a provider’s best effort, a patient’s fear of shame can also cause relevant sensitive data to not be shared in interviews. This is likely why some research has shown more accurate data collection from computer questioning rather than from providers (Carr, Ghosh, & Ancill, 1983). The implementation of standardized questions and data collection can improve inter-rater agreement rates toward 75–80% (Miller, 2001).

Tracking treatment progress—Over the last 20 years, several research groups have shown that tracking clinical outcomes with clinical scales on a frequent basis can significantly improve the probability of treatment response. For example, Dr. Lambert’s group tested how OQ-45 utilization impacted clinical outcomes. Over several studies, his team varied whether OQ-45 results were shown in real-time to no one, only the therapist, or to the therapist and the patient. Their results show that sharing weekly OQ-45 results to the therapist improves outcomes over usual care, but showing data to the therapist and the patient improves outcomes nearly 300% more often than usual care (Shimokawa, Lambert, & Smart, 2010). Dr. Miller created the Outcome Rating Scale (ORS) to improve the feasibility of outcome tracking within outpatient clinics. His team showed similar results with the three-question ORS as found in Dr. Lambert’s previous work (Miller, Duncan, Brown, Sparks, & Claud, 2003). In the Army, system-wide outcome results for depression and PTSD improved by over 25% during the first several years of implementation of BHDP even though most of the leadership’s efforts were focused on MBC implementation and other system structural changes and little attention was paid to altering the provider–patient interaction. These combined findings suggest that a key ingredient may be simply implementing an MBC process that tracks progress throughout a course of treatment with real-time data available to providers and patients that informs treatment decisions.

Patient-centered goal setting—Regardless of therapy modality chosen for treatment, several common therapeutic factors have been shown to correlate with positive outcomes. A sampling of these factors include the following: a patient and a provider sharing an understanding of the patient’s problem; a shared understanding of the therapeutic procedures to be utilized; a strong therapeutic alliance; and a sense of hopefulness about the future (Imel & Wampold, 2008). MBC can facilitate the establishment of shared goals, improve objectivity in tracking progress toward those goals, and determining when goals have been achieved. A patient may be more likely to engage with goal setting when it is based on symptoms and functional status data he or she has provided. MBC can also help track whether a patient is on track for a positive recovery, thus allowing for a quicker modification of treatment efforts when a patient is shown to likely not be on a recovery track. Henkel and colleagues (2009) demonstrated that early treatment response of at least 20% to an antidepressant predicts improved response and remission at later time points. More recent findings show that lack of response to medication after two weeks predicts a 93% chance of no remission at 12 weeks, suggesting that early treatment change is important (Hicks et al., 2019). Without MBC, the ability to determine initial response is very difficult. Thus, routine MBC is needed to consistently detect change (or the lack thereof) in real-world clinical settings to allow full use of research findings like those described earlier. Finally, many patients continue with care longer than is necessary, thus increasing the costs of care and limiting access to care for those not able to find available treatment. MBC data can help providers determine when the patient has improved enough to consider initiating the termination process.

Improve patient engagement and psychological mindedness—Reviewing measures during each treatment session provides nuanced benefits to the patient. In-session review of completed measures shows respect for the time and effort patients have spent completing such instruments and allows patients to highlight current concerns. It also serves to reinforce the importance and utility of this task. Routinely reviewing items from clinical measures with patients teaches them psychological mindedness about their current struggles and can help organize patient experiences into coherent and understandable phenomena. Using the metrics to support a change or sustainment of the current treatment plan provides transparency in joint decision making. The MBC in-session review process further strengthens collaboration and therapeutic alliance between the provider and the patient.

MBC allows for actionable feedback in support of building learning organizations— Over the past 20 years, efforts to improve health care delivery quality have led to many health system efforts to develop learning organizations and highly reliable processes (Pronovost et al., 2006; Senge, 1990; Till, Amin, & McKimm., 2016). Real-time clinical data can be de-identified and aggregated at higher levels to inform the ongoing performance of a clinic, identify areas of program strength and weakness, and serve to identify areas of potential gaps in care. Learning organizations and high reliability efforts require ongoing systemic outcome data feeds in order to be successful. Meaningful feedback is the lifeblood of a well-functioning learning organization. Only with this data can we move beyond process outcomes and secondary indicators of treatment effectiveness and move toward truly useful quality improvement efforts by looking at actual clinical outcomes. Well-executed MBC programs can simultaneously provide this data for providers at the point of care and for leaders throughout all levels of an organization.

Why Is Technology Needed to Perform MBC?

MBC has been implemented by many BH providers using paper-based measures, and in fact, continues to be paper-based in many settings. The feasibility and simplicity of using paper is hard to match. Paper-based standard forms and clinical scales can be very valuable in collecting real-time feedback reflecting current thoughts and feelings about the session. However, the use of technology can open up many other opportunities for the amount and type of data collected, the timing of data collection, methods of data presentation to providers, and use of aggregated data to inform clinic and health system program evaluation. The proliferation of mobile computing and touch screen technology has made the use of technology ubiquitous across most age groups, thus decreasing previous hardware and usability issues. The use of technology to adopt MBC can improve the consistency of care processes, minimize friction points in patient and clinician experiences, help build routine practices in data collection, allow for real-time data analysis and display, and allow for aggregation of data for program analyses, as follows:

Consistent process of care—MBC technology use allows opportunities for data collection across multiple time points within an episode of care that can include time points between appointments. Clinics can monitor data collection needs electronically and can minimize the hassle of lost paperwork, filing of papers, transcribing data to databases, or scanning papers into an electronic health record (EHR). Screening and periodic outcome tracking via paper in clinic settings do not allow for automation and are subject to many types of errors. Without technology, clinics will likely have a difficult time determining which measures to provide a patient and when the measures should be administered to each patient. This critical issue can result in the abandonment of MBC practices or lead a team to just measure the beginning and end of care, but not at intervals throughout an episode of care. Consequently, the ability of MBC practices to inform real-time clinical decision making during the course of treatment can be quickly lost. This is a critical issue, as the most likely ingredient for MBC to improve overall outcomes is the real-time feedback to providers informing treatment decision making. Setting consistent MBC timing processes help providers focus on reviewing and analyzing data rather than scheduling data collection. When building the BHDP, we addressed this issue by developing a standard intake questionnaire across the Army. We ensured stakeholders from across the country were involved in building the questionnaire in order to maximize the consensus base when launching BHDP. We built in default clinical measure frequency settings for measures to standardize baseline data collection. Providers can override these frequencies when clinically indicated. The BHDP tracks frequency settings for each patient, allowing for an automated data collection process that does not depend on any person to decide which measures are given on the day of the appointment. These types of functions are critical to the success of MBC implementation.

Periodic data collection throughout treatment also helps remind the patient to review his or her current status compared to previous levels of functioning. This type of work can help reinforce the teaching of psychological mindedness, reinforce hopefulness about the future, and improve self-management through the structure of routine tracking of key symptoms and functionality. In our work with MBC tools, we have found that showing patients graphing of clinical measures help patients respond more accurately to MBC data collection over time and build a therapeutic alliance. At times, patients will report subjective lack of improvement despite clinical scores decreasing. We use these moments to check whether a patient is tracking actual improvement leading to a possible increase in hopefulness about recovery. Other times, patients may not report clinical measure improvement despite improvement in our clinical observations. In these cases, we can discuss the incongruity and sometimes find that patients are doing better with coping with symptoms and with overall function, even if symptom severity has not decreased. When symptom improvement is not occurring, we can review the most problematic issues to reset the treatment plan to meet the most current needs.

Minimizing friction points in patient and clinician experiences—Many providers worry that MBC implementation will affect their patient sessions. A paper MBC process involves human scoring of scales, management of paper usage by providers, translation of data into an EHR, and potential transport of the form to providers. These challenges are expensive to manage and are a frequent cause of MBC implementation failures. The use of a web-based data collection system allows for the collection of data before a treatment session, automated scoring of measures, graphing visualizations, and presentation of data in an easily reviewable manner for a busy provider. Real-time data viewing by providers is critical, as it allows providers to utilize the data at the point of care for treatment planning decisions. Avoiding the cost of staff data collection, hand-scoring of scales, and translation into medical records can often lead to an affordable web-based MBC system. Recent procedure coding changes also allow for billing for clinical measure execution that can offset MBC system costs (CPT code 96127).

Technology use allows us to build visualization tools for clinicians that cannot be achieved with a paper-based process. We can build color-coded schemes for quick reviews of hot spot areas of concern. We can mark areas of pathology v...

Table of contents

• Cover
• Half Title
• Series
• Title
• Copyright
• Dedication
• Contents
• List of Figures
• List of Tables
• List of Contributors
• Series Editor’s Foreword
• 1 Enabling Behavioral Health Measurement-Based Care With Technology
• 2 Practical Guidance on Reaching Remote Patients Through Telemental Health
• 3 Internet-Based Mental Health Interventions: Evidence, Practical Considerations, and Future Directions
• 4 Using Mobile Apps in Mental Health Practice
• 5 Use of Virtual Reality Exposure Therapy for Anxiety- and Trauma-Related Disorders
• 6 Mental Health Practice and Social Media
• 7 Innovative and Evolving Mobile Mental Health Technologies for the Treatment of Serious Mental Illness
• 8 Technology-Based Interventions for Substance Use Disorders
• 9 Utilization of Technologies to Support Patients With Eating Disorders
• 10 Mobile Technology for Tobacco Cessation
• 11 Older Adults and the Utilization of Mobile and At-Home Health Technologies for Mental Health Care
• 12 Using Technology to Promote Suicide Prevention
• 13 Ethical and Legal Issues in the Clinical Use of Technology
• 14 Improving Mental Health Outcomes With Artificial Intelligence
• 15 Shared Immersive Environments and Virtual Worlds and Their Application in Behavioral Health
• Index