Assessing a Digital Medicine System in Veterans with Severe Mental Illness: A provider-randomized clinical trial (Preprint)

2021 ◽  
Author(s):  
Sarah Gonzales ◽  
Olaoluwa O. Okusaga ◽  
J. Corey Reuteman-Fowler ◽  
Megan M. Oakes ◽  
Jamie N. Brown ◽  
...  
Keyword(s):  
Mental Illness ◽  
Medication Adherence ◽  
Positive Impact ◽  
Structured Interviews ◽  
Open Label ◽  
Treatment As Usual ◽  
Provider Perspectives ◽  
Medication Refill ◽  
Digital Medicine ◽  
Patient And Provider Perspectives

BACKGROUND Suboptimal medication adherence is a significant problem for patients with serious mental illness (SMI). Measuring medication adherence through subjective and objective measures can be challenging, time consuming and inaccurate. OBJECTIVE We evaluated a digital medicine system (DMS) compared to treatment as usual (TAU) on adherence to oral aripiprazole and patient and provider perspectives on the feasibility and acceptability of a DMS. METHODS This open-label, 2-site, provider-randomized trial assessed aripiprazole refill adherence in Veterans with schizophrenia, schizoaffective disorder, bipolar disorder, or major depressive disorder. We randomized 26 providers such that their patients either received TAU or DMS for a period of 90 days. Semi-structured interviews with patients and providers were used to examine feasibility and acceptability of using the DMS. RESULTS We enrolled 46 patients across 2 Veterans Affairs (VA) sites: (21 in DMS and 25 in TAU). There was no difference in medication refill over 3 and 6 months, respectively (82% and 75% DMS vs. 86% and 82% TAU). The DMS arm had 85% days covered during the period they were engaged with the DMS (144 days on average). Interviews with patients (n=14) and providers (n=5) elicited themes salient to using the DMS. Patient themes included: pre-enrollment adherence strategies and interest in the DMS, positive impact on medication adherence, system usability challenges, support needs, and suggested design/functionality improvements. Provider themes included: concerns for patient medication adherence and interest in the DMS, concerns with the DMS, DMS dashboard usability, challenges of the DMS, and suggestions to increase provider use. CONCLUSIONS There was no observed difference in refill rates. Among those who engaged in the DMS arm, refill rates were relatively high (85%). The qualitative analyses highlighted areas for further refinement of the DMS. CLINICALTRIAL NCT03881449

scholarly journals Detection of Behavioral Anomalies in Medication Adherence Patterns Among Patients With Serious Mental Illness Engaged With a Digital Medicine System (Preprint)

2020 ◽  
Author(s):  
Jonathan Knights ◽  
Zahra Heidary ◽  
Jeffrey M Cochran
Keyword(s):  
Clinical Trial ◽  
Mental Illness ◽  
Medication Adherence ◽  
Anomaly Detection ◽  
Serious Mental Illness ◽  
Clinical Trial Data ◽  
Trial Data ◽  
Reference Sequence ◽  
Sequence Of Events ◽  
Digital Medicine

BACKGROUND Adherence to medication is often represented in the form of a success percentage over a period of time. Although noticeable changes to aggregate adherence levels may be indicative of unstable medication behavior, a lack of noticeable changes in aggregate levels over time does not necessarily indicate stability. The ability to detect developing changes in medication-taking behavior under such conditions in real time would allow patients and care teams to make more timely and informed decisions. OBJECTIVE This study aims to develop a method capable of identifying shifts in behavioral (medication) patterns at the individual level and subsequently assess the presence of such shifts in retrospective clinical trial data from patients with serious mental illness. METHODS We defined the term <i>adherence volatility</i> as <i>“the degree to which medication ingestion behavior fits expected behavior based on historically observed data”</i> and defined a contextual anomaly system around this concept, leveraging the empirical entropy rate of a stochastic process as the basis for formulating anomaly detection. For the presented methodology, each patient’s evolving behavior is used to dynamically construct the expectation bounds for each future interval, eliminating the need to rely on model training or a static reference sequence. RESULTS Simulations demonstrated that the presented methodology identifies anomalous behavior patterns even when aggregate adherence levels remain constant and highlight the temporal dependence inherent in these anomalies. Although a given sequence of events may present as anomalous during one period, that sequence should subsequently contribute to future expectations and may not be considered anomalous at a later period—this feature was demonstrated in retrospective clinical trial data. In the same clinical trial data, anomalous behavioral shifts were identified at both high- and low-adherence levels and were spread across the whole treatment regimen, with 77.1% (81/105) of the population demonstrating at least one behavioral anomaly at some point in their treatment. CONCLUSIONS Digital medicine systems offer new opportunities to inform treatment decisions and provide complementary information about medication adherence. This paper introduces the concept of <i>adherence volatility</i> and develops a new type of contextual anomaly detection, which does not require an a priori definition of <i>normal</i> and allows expectations to evolve with shifting behavior, removing the need to rely on training data or static reference sequences. Retrospective analysis from clinical trial data highlights that such an approach could provide new opportunities to meaningfully engage patients about potential shifts in their ingestion behavior; however, this framework is not intended to replace clinical judgment, rather to highlight elements of data that warrant attention. The evidence provided here identifies new areas for research and seems to justify additional explorations in this area.

Working at the Intersection of Palliative End-of-Life and Mental Health Care: Provider Perspectives

2020 ◽  
pp. 082585972095136
Author(s):  
Tanya Park ◽  
Kathy Hegadoren ◽  
Bernadette Workun
Keyword(s):  
Mental Health ◽  
Health Care ◽  
Mental Illness ◽  
Mental Health Care ◽  
End Of Life ◽  
Structured Interviews ◽  
Provider Perspectives ◽  
Mental Health Care Provider ◽  
Mental Health Challenges

Objective: Palliative, end-of-life care (PEOLC) providers are poorly resourced in addressing the needs of patients with mental health challenges, and the dying experiences of this cohort—particularly those with a comorbid, chronic and persistent mental illness (CPMI)—are poorly documented. We sought to explore the experiences of PEOLC providers with regard to caring for patients with mental health challenges, and gather insights into ways of improving accessibility and quality of PEOLC for these patients. Method: Twenty providers of PEOLC, from different disciplines, took part in semi structured interviews. The data were coded and analyzed using a reflexive, inductive-deductive process of thematic analysis. Results: The most prominent issues pertained to assessment of patients and differential diagnosis of CPMI, and preparedness of caregivers to deliver mental health interventions, given the isolation of palliative care from other agencies. Among the assets mentioned, informal relationships with frontline caregivers were seen as the main support structure, rather than the formal policies and procedures of the practice settings. Strategies to improve mental health care in PEOLC centered on holistic roles and interventions benefiting the entire palliative population, illustrating the participants saw little point in compartmentalizing mental illness, whether diagnosed or not. Significance of Results: Continuity of care and personal advocacy can significantly improve quality of life for end-of-life patients with mental health challenges, but bureaucracy and disciplinary siloing tend to isolate these patients and their caregivers. Improved interdisciplinary connectivity and innovative, hybridized roles encompassing palliation and psychiatry are 2 strategies to address this disconnect, as well as enhanced training in core mental health care competencies for PEOLC providers.

scholarly journals Characterization of activity behavior using a digital medicine system and comparison to medication ingestion in patients with serious mental illness

2021 ◽  
Vol 4 (1) ◽  
Author(s):  
Jeffrey M. Cochran ◽  
Zahra Heidary ◽  
Jonathan Knights
Keyword(s):  
Mental Illness ◽  
Medication Adherence ◽  
Serious Mental Illness ◽  
Activity Rhythm ◽  
Activity Patterns ◽  
Improve Medication Adherence ◽  
Ingestion Rates ◽  
Patient Level ◽  
Activity Intensity ◽  
Digital Medicine

AbstractActivity patterns can be important indicators in patients with serious mental illness. Here, we utilized an accelerometer and electrocardiogram incorporated within a digital medicine system, which also provides objective medication ingestion records, to explore markers of patient activity and investigate whether these markers of behavioral change are related to medication adherence. We developed an activity rhythm score to measure the consistency of step count patterns across the treatment regimen and explored the intensity of activity during active intervals. We then compared these activity features to ingestion behavior, both on a daily basis, using daily features and single-day ingestion behavior, and at the patient-level, using aggregate features and overall ingestion rates. Higher values of the single-day features for both the activity rhythm and activity intensity scores were associated with higher rates of ingestion on the following day. Patients with a mean activity rhythm score greater than the patient-level median were also shown to have higher overall ingestion rates than patients with lower activity rhythm scores (p = 0.004). These initial insights demonstrate the ability of digital medicine to enable the development of digital behavioral markers that can be compared to previously unavailable objective ingestion information to improve medication adherence.

scholarly journals Detection of Behavioral Anomalies in Medication Adherence Patterns Among Patients With Serious Mental Illness Engaged With a Digital Medicine System

10.2196/21378 ◽  
2020 ◽  
Vol 7 (9) ◽  
pp. e21378
Author(s):  
Jonathan Knights ◽  
Zahra Heidary ◽  
Jeffrey M Cochran
Keyword(s):  
Clinical Trial ◽  
Mental Illness ◽  
Medication Adherence ◽  
Anomaly Detection ◽  
Serious Mental Illness ◽  
Clinical Trial Data ◽  
Trial Data ◽  
Reference Sequence ◽  
Sequence Of Events ◽  
Digital Medicine

Background Adherence to medication is often represented in the form of a success percentage over a period of time. Although noticeable changes to aggregate adherence levels may be indicative of unstable medication behavior, a lack of noticeable changes in aggregate levels over time does not necessarily indicate stability. The ability to detect developing changes in medication-taking behavior under such conditions in real time would allow patients and care teams to make more timely and informed decisions. Objective This study aims to develop a method capable of identifying shifts in behavioral (medication) patterns at the individual level and subsequently assess the presence of such shifts in retrospective clinical trial data from patients with serious mental illness. Methods We defined the term adherence volatility as “the degree to which medication ingestion behavior fits expected behavior based on historically observed data” and defined a contextual anomaly system around this concept, leveraging the empirical entropy rate of a stochastic process as the basis for formulating anomaly detection. For the presented methodology, each patient’s evolving behavior is used to dynamically construct the expectation bounds for each future interval, eliminating the need to rely on model training or a static reference sequence. Results Simulations demonstrated that the presented methodology identifies anomalous behavior patterns even when aggregate adherence levels remain constant and highlight the temporal dependence inherent in these anomalies. Although a given sequence of events may present as anomalous during one period, that sequence should subsequently contribute to future expectations and may not be considered anomalous at a later period—this feature was demonstrated in retrospective clinical trial data. In the same clinical trial data, anomalous behavioral shifts were identified at both high- and low-adherence levels and were spread across the whole treatment regimen, with 77.1% (81/105) of the population demonstrating at least one behavioral anomaly at some point in their treatment. Conclusions Digital medicine systems offer new opportunities to inform treatment decisions and provide complementary information about medication adherence. This paper introduces the concept of adherence volatility and develops a new type of contextual anomaly detection, which does not require an a priori definition of normal and allows expectations to evolve with shifting behavior, removing the need to rely on training data or static reference sequences. Retrospective analysis from clinical trial data highlights that such an approach could provide new opportunities to meaningfully engage patients about potential shifts in their ingestion behavior; however, this framework is not intended to replace clinical judgment, rather to highlight elements of data that warrant attention. The evidence provided here identifies new areas for research and seems to justify additional explorations in this area.

scholarly journals Digital medicines for adherence support: A conceptual framework and qualitative study of adherence among chronically ill patients

2021 ◽  
Vol 27 (5) ◽  
pp. 146045822110594
Author(s):  
Anabel F Castillo ◽  
Alexander L Davis ◽  
Baruch Fischhoff ◽  
Tamar Krishnamurti
Keyword(s):  
Medication Adherence ◽  
Drug Efficacy ◽  
Chronically Ill ◽  
Structured Interviews ◽  
Change Behavior ◽  
Digital Medicine ◽  
Adherence Support ◽  
Drug Regimens ◽  
Expert Input ◽  
Chronically Ill Patients

Digital medicine programs (DMPs) are emerging technologies that use sensor-enabled medicine to detect when patients have taken their medication and then provide feedback about adherence. We use qualitative methods to understand how patients change their behavioral patterns while participating in a DMP intervention. An influence diagram outlining the factors hypothesized to affect adherence in DMPs constructed from prior scientific research and expert input was created. Subsequently, we conducted semi-structured interviews with 10 patients to see if their experience supported the relationships outlined in the model. We identified three pathways by which DMPs are likely to change behavior around medication adherence: (1) providing patients and providers with accurate, personalized information about adherence; (2) improving patient–provider interactions by structuring them around this information; and (3) facilitating routines and habits for medication use. Chronically ill patients often fail to adhere to drug regimens. Patients in a DMP intervention used the DMP-provided information to better understand drug efficacy and collaborated with their physician to develop adherence strategies. DMPs can promote medication adherence among patients who are willing to use them and may be most effective if physicians are active partners in the DMP.

scholarly journals Patient and provider perspectives on the development and resolution of prescribing cascades: a qualitative study

2020 ◽  
Vol 20 (1) ◽  
Author(s):  
Barbara J. Farrell ◽  
Lianne Jeffs ◽  
Hannah Irving ◽  
Lisa M. McCarthy
Keyword(s):  
Qualitative Study ◽  
Team Member ◽  
Healthcare Providers ◽  
Social Supports ◽  
Relevant Information ◽  
Patient Specific ◽  
Day Hospital ◽  
Structured Interviews ◽  
Provider Perspectives ◽  
Patient And Provider Perspectives

Abstract Background Prescribing cascades occur when the side effect of a medication is treated with a second medication. The aim of the study was to understand how prescribing cascades develop and persist and to identify strategies for their identification, prevention and management. Method This qualitative study employed semi-structured interviews to explore the existence of prescribing cascades and to gather patients', caregivers' and clinicians’ perspectives about how prescribing cascades start, persist and how they might be resolved. Participants were older adults (over age 65) at an outpatient Geriatric Day Hospital (GDH) with possible prescribing cascades (identified by a GDH team member), their caregivers, and healthcare providers. Data were analyzed using an inductive content analysis approach. Results Fourteen participants were interviewed (eight patients, one family caregiver, one GDH pharmacist, three GDH physicians and one family physician) providing a total of 22 interviews about patient-specific cases. The complexity and contextually situated nature of prescribing cascades created challenges for all of those involved with their identification. Three themes impacted how prescribing cascades developed and persisted: varying awareness of medications and cascades; varying feelings of accountability for making decisions about medication-related care; and accessibility to an ideal environment and relevant information. Actions to prevent, identify or resolve cascades were suggested. Conclusion Patients and healthcare providers struggled to recognize prescribing cascades and identify when they had occurred; knowledge gaps contributed to this challenge and led to inaction. Strategies that equip patients and clinicians with resources to recognize prescribing cascades and environmental and social supports that would help with their identification are needed. Current conceptualizations of cascades warrant additional refinement by considering the nuances our work raises regarding their appropriateness and directionality.

scholarly journals Dose Administration Aid Service in Community Pharmacies: Characterization and Impact Assessment

Pharmacy ◽  
2021 ◽  
Vol 9 (4) ◽  
pp. 190
Author(s):  
André Vicente ◽  
Beatriz Mónico ◽  
Mónica Lourenço ◽  
Olga Lourenço
Keyword(s):  
Medication Adherence ◽  
Positive Impact ◽  
Qualitative Content Analysis ◽  
Treatment Success ◽  
Qualitative Description ◽  
Community Pharmacies ◽  
Structured Interviews ◽  
Dose Administration ◽  
Medication Waste ◽  
The Impact

Adherence to therapies is a primary determinant of treatment success. Lack of medication adherence is often associated with medical and psychosocial issues due to complications from underlying conditions and is an enormous waste of medical resources. Dose Administration Aid Service (DAAS) can be seen as part of the solution, allowing individual medicine doses to be organized according to the dosing schedule determined by the patient’s prescriber. The most recent systematic reviews admit the possibility of a positive impact of this service. In line with this background, the study reported in this paper aimed to characterize DAAS implementation in Portugal and understand the perceptions of pharmacists and owners of community pharmacies regarding the impact of DAAS, preferred methodology types, and State contribution. The study was guided by qualitative description methodology and reported using the consolidated criteria for reporting qualitative research (COREQ) checklist. Data were collected through semi-structured interviews with 18 pharmacists and/or owners of community pharmacies. Using qualitative content analysis, we identified categories that revealed that automated weekly methodology is the preferred methodology, because of its easiness of use and lower cost of preparation. However, the investment cost was felt to be too high by the participants considering the number of potential users for implementation in practice. Participants were also unanimous in recognizing that DAAS has a very positive impact in terms of safety and medication adherence, and the majority agreed that it also helped reduce medication waste. Implications of these findings for medication adherence are discussed.

A mixed method evaluation of an intervention for parents with mental illness

2019 ◽  
Vol 24 (4) ◽  
pp. 717-727 ◽  
Author(s):  
Darryl Maybery ◽  
Melinda Goodyear ◽  
Andrea Reupert ◽  
Jade Sheen ◽  
Warren Cann ◽  
...  
Keyword(s):  
Mental Illness ◽  
Family Functioning ◽  
Mental Illnesses ◽  
Control Treatment ◽  
Wait List ◽  
Structured Interviews ◽  
Treatment As Usual ◽  
Quasi Experimental ◽  
And Control ◽  
The Impact

Let’s Talk About Children is a manualised intervention for parents with a mental illness that aims to impact positively on family dynamics. Previous evaluations focused on parents with an affective disorder. The purpose of this study was to evaluate the intervention for parents with various mental illnesses and explore parents’ self-reported views regarding the impact of the intervention. A quasi-experimental approach was employed to compare outcomes for parents who received Let’s Talk About Children plus treatment as usual ( n = 20) with a wait list control (treatment as usual) group ( n = 19), using family functioning and parenting stress questionnaires. Questionnaires were completed 2 weeks prior to receiving the intervention and 4 to 6 weeks after the final session. The wait list parents completed the same questionnaires at two time periods, 6 weeks apart. Semi-structured interviews were conducted after the intervention. Both intervention and control groups showed improvements in parenting and family functioning. Interview data highlighted (1) increased insight, (2) normalising of the illness in the family, (3) family communication changes, (4) the importance of supporting the parenting role and (5) suggestions for additional supports. There are possible issues regarding the influence of psycho-education when giving participants information about the nature of the research.

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