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

2020 ◽  
Author(s):  
Maike Richter ◽  
Michael Storck ◽  
Rogerio Blitz ◽  
Janik Goltermann ◽  
Juliana Seipp ◽  
...  
Keyword(s):  
Affective Disorders ◽  
Inpatient Treatment ◽  
Patient Reported Outcomes ◽  
Data Entry ◽  
Machine Learning Algorithms ◽  
Individual Risk ◽  
Depression Severity ◽  
Patient Reported ◽  
Longitudinal Data Collection ◽  
Digital Collection

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.

scholarly journals Repeated Digitized Assessment of Risk and Symptom Profiles During Inpatient Treatment of Affective Disorder: Observational Study

10.2196/24066 ◽  
2020 ◽  
Vol 7 (12) ◽  
pp. e24066
Author(s):  
Maike Frederike Richter ◽  
Michael Storck ◽  
Rogério Blitz ◽  
Janik Goltermann ◽  
Juliana Seipp ◽  
...  
Keyword(s):  
Affective Disorder ◽  
Affective Disorders ◽  
Inpatient Treatment ◽  
Data Entry ◽  
Self Report ◽  
Individual Risk ◽  
Depression Severity ◽  
Symptom Profiles ◽  
Longitudinal Data Collection ◽  
Digital Collection

Background Predictive models have revealed promising results for the individual prognosis of treatment response and relapse risk as well as for differential diagnosis in affective disorders. Yet, in order to translate personalized predictive modeling from research contexts to psychiatric clinical routine, standardized collection of information of sufficient detail and temporal resolution in day-to-day clinical care is needed. Digital collection of self-report measures by patients is a time- and cost-efficient approach to gain such data throughout treatment. Objective The objective of this study was to investigate whether patients with severe affective disorders were 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 assessments were of sufficient diagnostic validity. Methods We implemented a system for longitudinal digital collection of risk and symptom profiles based on repeated self-reports via tablet computers throughout inpatient treatment of affective disorders at the Department of Psychiatry at the University of Münster. Tablet-handling competency and the speed of data entry were assessed. Depression severity was additionally assessed by a clinical interviewer at baseline and before discharge. Results Of 364 affective disorder patients who were approached, 242 (66.5%) participated in the study; 88.8% of participants (215/242) were diagnosed with major depressive disorder, and 27 (11.2%) had bipolar disorder. During the duration of inpatient treatment, 79% of expected assessments were completed, with an average of 4 completed assessments per participant; 4 participants (4/242, 1.6%) dropped out of the study prematurely. During data entry, 89.3% of participants (216/242) did not require additional support. Needing support with tablet handling and slower data entry pace were predicted by older age, whereas depression severity at baseline did not influence these measures. Patient self-reporting of depression severity showed high agreement with standardized external assessments by a clinical interviewer. Conclusions Our results indicate that digital collection of self-report measures 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 toward personalized psychiatric care.

scholarly journals Repeated Digitized Assessment of Risk and Symptom Profiles During Inpatient Treatment of Affective Disorder: Observational Study (Preprint)

2020 ◽  
Author(s):  
Maike Frederike Richter ◽  
Michael Storck ◽  
Rogério Blitz ◽  
Janik Goltermann ◽  
Juliana Seipp ◽  
...  
Keyword(s):  
Affective Disorder ◽  
Affective Disorders ◽  
Inpatient Treatment ◽  
Data Entry ◽  
Self Report ◽  
Individual Risk ◽  
Depression Severity ◽  
Symptom Profiles ◽  
Longitudinal Data Collection ◽  
Digital Collection

BACKGROUND Predictive models have revealed promising results for the individual prognosis of treatment response and relapse risk as well as for differential diagnosis in affective disorders. Yet, in order to translate personalized predictive modeling from research contexts to psychiatric clinical routine, standardized collection of information of sufficient detail and temporal resolution in day-to-day clinical care is needed. Digital collection of self-report measures by patients is a time- and cost-efficient approach to gain such data throughout treatment. OBJECTIVE The objective of this study was to investigate whether patients with severe affective disorders were 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 assessments were of sufficient diagnostic validity. METHODS We implemented a system for longitudinal digital collection of risk and symptom profiles based on repeated self-reports via tablet computers throughout inpatient treatment of affective disorders at the Department of Psychiatry at the University of Münster. Tablet-handling competency and the speed of data entry were assessed. Depression severity was additionally assessed by a clinical interviewer at baseline and before discharge. RESULTS Of 364 affective disorder patients who were approached, 242 (66.5%) participated in the study; 88.8% of participants (215/242) were diagnosed with major depressive disorder, and 27 (11.2%) had bipolar disorder. During the duration of inpatient treatment, 79% of expected assessments were completed, with an average of 4 completed assessments per participant; 4 participants (4/242, 1.6%) dropped out of the study prematurely. During data entry, 89.3% of participants (216/242) did not require additional support. Needing support with tablet handling and slower data entry pace were predicted by older age, whereas depression severity at baseline did not influence these measures. Patient self-reporting of depression severity showed high agreement with standardized external assessments by a clinical interviewer. CONCLUSIONS Our results indicate that digital collection of self-report measures 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 toward personalized psychiatric care.

scholarly journals Application of Machine Learning Algorithms to Depression Screening and Attempt at Pattern Extraction of Patient-Reported Outcomes that Negatively Affect Classification Accuracy (Preprint)

2017 ◽  
Author(s):  
Junetae Kim ◽  
Byungtae Lee ◽  
Sae Byul Lee ◽  
Il Yong Chung ◽  
Sei Hyun Ahn ◽  
...  
Keyword(s):  
Deep Learning ◽  
Missing Data ◽  
Patient Reported Outcomes ◽  
Machine Learning Algorithms ◽  
Depression Screening ◽  
Smartphone Applications ◽  
Patient Reported ◽  
Negative Effect ◽  
Using Data ◽  
Screening Accuracy

BACKGROUND Smartphone applications have recently been used as a breakthrough technology for monitoring mental health conditions in cancer outpatient settings. However, the use of electronic patient-reported outcomes (ePROs) on mental conditions through smartphone applications raises new concerns, which includes the question of the accuracy of depression screening. Thus, research is essential for improving the depression-screening performance. OBJECTIVE This study aims to (1) test whether deep-learning-based algorithms can overcome the limitations of traditional statistical methods in terms of depression screening accuracy. In addition, the study aims to (2) explore ePRO patterns that adversely affect depression screening accuracy. METHODS As a deep learning-based algorithm, a feedforward neural network algorithm was used. As a traditional statistical method, a random intercept logistic regression was employed. To explore the ePRO patterns that negatively impact model accuracy, mental fluctuations, missing data, and compounding effects between mental fluctuations and missing data were tested. The performances of the algorithms and the effects of the ePRO patterns were measured through the receiver operating characteristic comparison test. RESULTS The results of the study show that the performance of the deep-learning-based models was superior to that of the traditional statistical approach. The study found that mental fluctuations statistically reduced the accuracy of depression-screening models. A weak association between ePRO omissions and screening accuracy was found. Moreover, the compounding effects that had a negative effect on the depression screening accuracy were statistically significant. CONCLUSIONS Although well-trained deep-learning-based models exhibit excellent performance, they still have some limitations. Thus, it is very important to focus on data quality to predict health outcomes when using data that is difficult to quantify, such as mental conditions.

The Development and Initial Findings of A Study of a Prospective Adult Research Cohort with Inflammatory Bowel Disease (SPARC IBD)

2021 ◽  
Author(s):  
Laura E Raffals ◽  
Sumona Saha ◽  
Meenakshi Bewtra ◽  
Cecile Norris ◽  
Angela Dobes ◽  
...  
Keyword(s):  
Inflammatory Bowel Disease ◽  
Clinical Data ◽  
Bowel Disease ◽  
Patient Reported Outcomes ◽  
Data Entry ◽  
The United States ◽  
Predictors Of Response ◽  
Patient Reported ◽  
Inflammatory Bowel ◽  
Reported Data

Abstract Background Clinical and molecular subcategories of inflammatory bowel disease (IBD) are needed to discover mechanisms of disease and predictors of response and disease relapse. We aimed to develop a study of a prospective adult research cohort with IBD (SPARC IBD) including longitudinal clinical and patient-reported data and biosamples. Methods We established a cohort of adults with IBD from a geographically diverse sample of patients across the United States with standardized data and biosample collection methods and sample processing techniques. At enrollment and at time of lower endoscopy, patient-reported outcomes (PRO), clinical data, and endoscopy scoring indices are captured. Patient-reported outcomes are collected quarterly. The quality of clinical data entry after the first year of the study was assessed. Results Through January 2020, 3029 patients were enrolled in SPARC, of whom 66.1% have Crohn’s disease (CD), 32.2% have ulcerative colitis (UC), and 1.7% have IBD-unclassified. Among patients enrolled, 990 underwent colonoscopy. Remission rates were 63.9% in the CD group and 80.6% in the UC group. In the quality study of the cohort, there was 96% agreement on year of diagnosis and 97% agreement on IBD subtype. There was 91% overall agreement describing UC extent as left-sided vs extensive or pancolitis. The overall agreement for CD behavior was 83%. Conclusion The SPARC IBD is an ongoing large prospective cohort with longitudinal standardized collection of clinical data, biosamples, and PROs representing a unique resource aimed to drive discovery of clinical and molecular markers that will meet the needs of precision medicine in IBD.

Feasibility of a healthcare system-based tetralogy of Fallot patient registry

2017 ◽  
Vol 28 (2) ◽  
pp. 269-275
Author(s):  
Audrey L. Khoury ◽  
Eric G. Jernigan ◽  
Muntasir H. Chowdhury ◽  
Laura R. Loehr ◽  
Jennifer S. Nelson
Keyword(s):  
Tetralogy Of Fallot ◽  
Patient Reported Outcomes ◽  
Online Survey ◽  
Data Entry ◽  
Epidemiological Studies ◽  
Adult Patients ◽  
Birth Mother ◽  
Information Privacy Concerns ◽  
Recruitment Challenges ◽  
Patient Reported

AbstractBackgroundPatient-reported outcomes and epidemiological studies in adults with tetralogy of Fallot are lacking. Recruitment and longitudinal follow-up investigation across institutions is particularly challenging. Objectives of this study were to assess the feasibility of recruiting adult patients with tetralogy of Fallot for a patient-reported outcomes study, describe challenges for recruitment, and create an interactive, online tetralogy of Fallot registry.MethodsAdult patients living with tetralogy of Fallot, aged 18–58 years, at the University of North Carolina were identified using diagnosis code query. A survey was designed to collect demographics, symptoms, history, and birth mother information. Recruitment was attempted by phone (Part I, n=20) or by email (Part II, n=20). Data analysis included thematic grouping of recruitment challenges and descriptive statistics. Feasibility threshold was 75% for recruitment and for data fields completed per patient.ResultsIn Part I, 60% (12/20) were successfully contacted and eight (40%) were enrolled. Demographics and birth mother information were obtained for all enrolled patients. In Part II, 70% (14/20) were successfully contacted; 30% (6/20) enrolled and completed all data fields linked to REDCap database; the median time for survey completion was 8 minutes. Half of the patients had cardiac operations/procedures performed at more than one hospital. Automatic electronic data entry from the online survey was uncomplicated.ConclusionsAlthough recruitment (54%) fell below our feasibility threshold, enrolled individuals were willing to complete phone or online surveys. Incorrect contact information, privacy concerns, and patient-reported time constraints were challenges for recruitment. Creating an online survey and linked database is technically feasible and efficient for patient-reported outcomes research.

Using mobile app technology for patient data capture and implementation.

2012 ◽  
Vol 30 (34_suppl) ◽  
pp. 317-317
Author(s):  
Steven J. Nurkin ◽  
Stephen B. Edge ◽  
Venkata R. Kakarla ◽  
Nikhil I. Khushalani
Keyword(s):  
Cancer Registry ◽  
Patient Reported Outcomes ◽  
Breast Surgery ◽  
Point Of Care ◽  
Data Entry ◽  
Patient Data ◽  
Care Plan ◽  
Patient Treatment ◽  
Patient Reported ◽  
Treatment Summary

317 Background: Mobile application technology has quickly become an integral part of clinical cancer care. While this technology is most commonly used as a point of care reference or educational tool, it may also be an effective method to capture patient data. The purpose of this project was to create a mobile solution for fast, point-of-care data capture and implementation, patient reported outcomes, to generate a patient treatment summary and provide data for cancer registry and clinical trials. Methods: Using the “MedDB app” (Bitwise Analytics) on an iPad2 (Apple) device, an application was developed to collect patient data following breast surgery for cancer. Collected data included elements required for pathologic staging using the American Joint Committee on Cancer TNM system (7th Edition) with the application deriving pathologic TNM and Stage Group. In addition, the type of breast surgery, lymph node surgery, and the expected next steps in treatment are coded for collection. Data are entered into the App in the clinic and transferred using a blinded code number to a web-based database. This database is then used to generate a patient surgical treatment summary and care plan, and is available for uploading into a research database. We then developed an individual patient App for their own personal mobile device. The purpose was to collect patient reported outcomes through their adjuvant care and surviorship. Results: The initial pilot included data entry on 20 breast cancer patients who had surgery prior to adjuvant therapy. The time to complete data entry was less than 60 seconds per case. Conclusions: Mobile communication devices are increasingly becoming key tools for clinicians. They are primarily used to search for medical resource information and the review of medical records. This pilot demonstrates the potential for use of mobile computer devices for collecting key data for clinical trials, the cancer registry, generating a patient treatment summary and care plan at the point of service as well as patient reported outcomes through personalized patient apps. Future development will include studies of integration with the electronic health record, the cancer registry systems, and expansion to a complete survivorship care plan system.

Augmenting machine learning algorithms to predict mortality using patient-reported outcomes in oncology.

2021 ◽  
Vol 39 (15_suppl) ◽  
pp. 1510-1510
Author(s):  
Ravi Bharat Parikh ◽  
Jill Schnall ◽  
Manqing Liu ◽  
Peter Edward Gabriel ◽  
Corey Chivers ◽  
...  
Keyword(s):  
Quality Of Life ◽  
Machine Learning ◽  
Mortality Risk ◽  
Patient Reported Outcomes ◽  
Machine Learning Algorithms ◽  
Measurement Information ◽  
Patients With Cancer ◽  
Patient Reported ◽  
Mortality Table

1510 Background: Machine learning (ML) algorithms based on electronic health record (EHR) data have been shown to accurately predict mortality risk among patients with cancer, with areas under the curve (AUC) generally greater than 0.80. While patient-reported outcomes (PROs) may also predict mortality among patients with cancer, it is unclear whether routinely-collected PROs improve the predictive performance of EHR-based ML algorithms. Methods: This cohort study included 8600 patients with cancer who had an outpatient encounter at one of 18 medical oncology practices in a large academic health system between July 1st, 2019 and January 1st, 2020. 4692 (54.9%) patients completed assessments of symptoms, performance status, and quality of life from the PRO version of the Common Terminology Criteria for Adverse Events and the Patient-Reported Outcomes Measurement Information System Global v.1.2 scales. We hypothesized that ML models predicting 180-day all-cause mortality based on EHR + PRO data would improve AUC compared to ML models based on EHR data alone. We assessed univariate and adjusted associations between each PRO and 180-day mortality. To train the EHR-only model, we fit a Least Absolute Shrinkage and Selection Operator (LASSO) regression using 192 EHR demographic, comorbidity, and laboratory variables. To train the EHR + PRO model, we used a two-phase approach to fit a model using EHR data for all patients and PRO data for those who completed assessments. To test our hypothesis, we compared the bootstrapped AUC, area under the precision-recall curve (AUPRC), and sensitivity at a 20% risk threshold for both models. Results: 464 (5.4%) patients died within 180 days of the encounter. Decreased quality of life, functional status, and appetite were associated with greater 180-day mortality (Table). Compared to the EHR-only model, the EHR + PRO model significantly improved AUC (0.86 [95% CI 0.85-0.86] vs. 0.80 [95% CI 0.80-0.81]), AUPRC (0.40 [95% CI 0.37-0.42] vs. 0.30 [95% CI 0.28-0.32]), and sensitivity (0.45 [95% CI 0.42-0.48] vs. 0.33 [95% CI 0.30-0.35]). Conclusions: Routinely collected PROs augment EHR-based ML mortality risk algorithms. ML algorithms based on EHR and PRO data may facilitate earlier supportive care for patients with cancer. Association of PROs with 180-day mortality.[Table: see text]

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