Continuous digital collection of patient-reported outcomes during inpatient treatment for affective disorders – implementation and feasibility

Abstract

Multivariate predictive models have revealed promising results for the individual prediction of treatment response, relapse risk as well as for the differential diagnosis in affective disorders. Yet, in order to translate personalized predictive modelling from the research context to psychiatric clinical routine, standardized collection of information of sufficient detail and temporal resolution in day-to-day clinical care is needed, based on which machine learning algorithms can be trained. Digital collection of patient-reported outcomes (PROs) is a time- and cost-efficient approach to gain such data throughout the treatment course. However, it remains unclear whether patients with severe affective disorders are willing and able to participate in such efforts, whether the feasibility of such systems might vary depending on individual patient characteristics and if digitally acquired patient-reported outcomes are of sufficient diagnostic validity. To address these questions, we implemented a system for continuous digital collection of patient-reported outcomes via tablet computers throughout inpatient treatment for affective disorders at the Department of Psychiatry at the University of Muenster. 364 affective disorder patients were approached, 66.5% of which could be recruited to participate in the study. An average of four assessments were completed during the treatment course, none of the participants dropped out of the study prematurely. 89.3% of participants did not require additional support during data entry. Need of support with tablet handling and slower data entry pace was predicted by older age, whereas depression severity at baseline did not influence these measures. Patient-reported outcomes of depression severity showed high agreement with standardized external assessments by a clinical interviewer. Our results indicate that continuous digital collection of patient-reported outcomes is a feasible, accessible and valid method for longitudinal data collection in psychiatric routine, which will eventually facilitate the identification of individual risk and resilience factors for affective disorders and pave the way towards personalized psychiatric care.

Design, Implementation and Feasibility of an Informatics Infrastructure for Standardized Data Acquisition, Transfer, Storage and Export in Psychiatric Clinical Routine

“…The iPad was then returned to the medical staff. Further details regarding the collection of patient-reported outcomes during inpatient treatment have previously been described [24]. In addition, Mopat@home was used for the collection of patient-reported outcomes following discharge.…”

“…99.3% of forms with scores (#Items 91,974 / #uncompleted items 645) were completed; 88% of forms without scores (#items 14,318 / #uncompleted items 1,701). Validity of the aquired data on depressive symptomatology was already analyzed in a feasibility study by Richter et al [24]. 8 standardized documentation forms with 39 items were created for the retrospective data export (Table 1).…”

“…The main objective of the presented study is the design and successful implementation of the informatics infrastructure required to train and validate predictive models in day-to-day clinical application in psychiatry as part of the SEED 11/19 study [24]. In detail, this includes the following steps -Implementation of standardized documentation forms in electronic health records -The set-up of an interface for direct data transfer between clinical documentation systems and a database for predictive analysis -The set-up of a unified databases that allows pooling of clinical data with further research data for predictive analysis -Visual presentation of relevant data entities and results of predictive analysis in electronic health records at the point-of-care…”

“…The main objective of the presented study is the design and successful implementation of the informatics infrastructure required to train and validate predictive models in day-to-day clinical application in psychiatry as part of the SEED 11/19 study [24]. In detail, this includes the following steps…”

Design and Implementation of an Informatics Infrastructure for Standardized Data Acquisition, Transfer, Storage, and Export in Psychiatric Clinical Routine: Feasibility Study

“…The main objective of this study was the design and successful implementation of the informatics infrastructure required to train and validate predictive models in day-to-day clinical application in psychiatry as part of the SEED 11/19 study [ 25 ]. Our study consisted of the following steps in detail:…”