Integration of Electronic Patient-Reported Outcomes Into Routine Cancer Care: An Analysis of Factors Affecting Data Completeness

2017 ◽  
pp. 1-10 ◽  
Author(s):  
Nicholas G. Wysham ◽  
Steven P. Wolf ◽  
Gregory Samsa ◽  
Amy P. Abernethy ◽  
Thomas W. LeBlanc

Purpose Routinely collected patient-reported outcomes (PROs) could provide invaluable data to a patient-centered learning health system but are often highly missing in clinical trials. We analyzed our experience with PROs to understand patterns of missing data using electronic collection as part of routine clinical care. Methods This is an analysis of a prospectively collected observational database of electronic PROs captured as part of routine clinical care in four different outpatient oncology clinics at an academic referral center. Results More than 24,000 clinical encounters from 7,655 unique patients are included. Data were collected via an electronic tablet–based survey instrument (Patient Care Monitor, version 2.0), at the time of clinical care, as part of routine care processes. Missing instruments (ie, no items completed) were submitted for 6.8% of clinical encounters, and 15.8% of encounters had missing items. Nearly 90% of all encounters involved < 10% missing items. In multivariable analyses, younger age, private health insurance, being seen in the breast oncology clinic, less time spent on the instrument, and longitudinal care were significantly associated with less missingness. Conclusion Embedding collection of electronic PRO data into routine clinical care yielded low rates of missing data in this real-world, prospectively collected database. In contrast to clinical trial experience, missingness improve with longitudinal care. This approach may be a solution to minimizing missingness of PROs in research or clinical care settings in support of learning health care systems.

2019 ◽  
Vol 37 (31_suppl) ◽  
pp. 99-99
Author(s):  
Heather A Rosett ◽  
Susan C. Locke ◽  
Steven Paul Wolf ◽  
Kris Herring ◽  
Greg P. Samsa ◽  
...  

99 Background: Electronic patient-reported outcomes (ePROs) can improve quality of life and prolong survival in patients with cancer by enhancing the detection and tracking of unmet supportive care needs. However, there remain unanswered questions about how to handle missing ePRO data. We hypothesized that patients may skip specific questions they feel do not apply to them. We aimed to examine the relationship between patient demographics and missing items in a real-world ePRO dataset. Methods: We utilized a prospectively collected database of ePROs from oncology clinics administering Patient Care Monitor 2.0 (PCM), a validated symptom survey of 78 items for men and 86 for women. We tabulated the frequency of missing items by item and domain (emotional, functional and symptom-related), and examined these by age, gender, education, race and marital status. Results: In 20,986 encounters there were responses to at least 1 PCM item from 6,933 patients. On average just 1% of items were skipped per encounter. By domain, 12.3% of functional, 8.4% of symptom-related, and 1.6% of emotional constructs contained at least one missing item. The highest frequency of item non-response was seen in older patients (>60yo) and those with high school education or less. The most frequently skipped items included: “attend a paid job” (10.7%), “reduced sexual enjoyment” (3.8%), and “running” (3.7%). These questions may be less relevant to older individuals, who, for example, quit running years earlier or are retired. Men had less missingness overall, except for “cooks for self” and “house work”, which may reflect traditional gender roles in the Southeast US. Conclusions: In a real-world ePRO implementation, items pertaining to more universal issues for cancer patients, like emotional well-being, have much lower rates of missingness, especially compared to functional items like “attend a paid job.” These results suggest that patients differentially complete ePROs based on perceived question relevance to them. The underlying driver behind individual item non-response may itself be an important data point in clinical care, warranting further study and discussion during clinic visits.


2018 ◽  
Vol 44 (8) ◽  
pp. 441-453 ◽  
Author(s):  
Wendy E. Gerhardt ◽  
Constance A. Mara ◽  
Ian Kudel ◽  
Esi M. Morgan ◽  
Pamela J. Schoettker ◽  
...  

2020 ◽  
Vol 28 (11) ◽  
pp. 5099-5107
Author(s):  
Heather A. Rosett ◽  
Susan C. Locke ◽  
Steven P. Wolf ◽  
Kris W. Herring ◽  
Gregory P. Samsa ◽  
...  

2016 ◽  
Vol 3 (3) ◽  
pp. 168 ◽  
Author(s):  
Heather Tabano ◽  
Thomas Gill ◽  
Kathryn Anzuoni ◽  
Heather Allore ◽  
Ann Gruber-Baldini ◽  
...  

2017 ◽  
Author(s):  
Junetae Kim ◽  
Byungtae Lee ◽  
Sae Byul Lee ◽  
Il Yong Chung ◽  
Sei Hyun Ahn ◽  
...  

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.


EP Europace ◽  
2019 ◽  
Vol 22 (3) ◽  
pp. 368-374 ◽  
Author(s):  
Benjamin A Steinberg ◽  
Jeffrey Turner ◽  
Ann Lyons ◽  
Joshua Biber ◽  
Mihail G Chelu ◽  
...  

Abstract Aims Incorporating patient-reported outcomes (PROs) into routine care of atrial fibrillation (AF) enables direct integration of symptoms, function, and health-related quality of life (HRQoL) into practice. We report our initial experience with a system-wide PRO initiative among AF patients. Methods and results All patients with AF in our practice undergo PRO assessment with the Toronto AF Severity Scale (AFSS), and generic PROs, prior to electrophysiology clinic visits. We describe the implementation, feasibility, and results of clinic-based, electronic AF PRO collection, and compare AF-specific and generic HRQoL assessments. From October 2016 to February 2019, 1586 unique AF patients initiated 2379 PRO assessments, 2145 of which had all PRO measures completed (90%). The median completion time for all PRO measures per visit was 7.3 min (1st, 3rd quartiles: 6, 10). Overall, 38% of patients were female (n = 589), mean age was 68 (SD 12) years, and mean CHA2DS2-VASc score was 3.8 (SD 2.0). The mean AFSS symptom score was 8.6 (SD 6.6, 1st, 3rd quartiles: 3, 13), and the full range of values was observed (0, 35). Generic PROs of physical function, general health, and depression were impacted at the most severe quartiles of AF symptom score (P &lt; 0.0001 for each vs. AFSS quartile). Conclusion Routine clinic-based, PRO collection for AF is feasible in clinical practice and patient time investment was acceptable. Disease-specific AF PROs add value to generic HRQoL instruments. Further research into the relationship between PROs, heart rhythm, and AF burden, as well as PRO-guided management, is necessary to optimize PRO utilization.


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