Electronic Patient-Reported Outcome Measures Evaluating Cancer Symptoms: A Systematic Review

APPROACH e-PROM system: a user-centered development and evaluation of an electronic patient-reported outcomes measurement system for management of coronary artery disease

BACKGROUND

Coronary artery disease (CAD) confers increased risks of premature mortality, non-fatal morbidity, and significant impairment in functional status and health-related quality of life. Routine administration of electronic patient-reported outcome measures (PROMs) and its real time delivery to care providers is known to have the potential to inform routine cardiac care and to improve quality of care and patient outcomes. This study describes a user-centered development and evaluation of the Alberta Provincial Project for Outcomes Assessment (APPROACH) electronic Patient Reported Outcomes Measurement (e-PROM) system. This e-PROM system is an electronic system for the administration of PROMs to patients with CAD and the delivery of the summarized information to their care providers to facilitate patient-physician communication and shared decision-making. This electronic platform was designed to be accessible via web-based and hand-held devices. Heuristic and user acceptance evaluation were conducted with patients and attending care providers.

RESULTS

The APPROACH e-PROM system was co-developed with patients and care providers, research investigators, informaticians and information technology experts. Five PROMs were selected for inclusion in the online platform after consultations with patient partners, care providers, and PROMs experts: the Seattle Angina Questionnaire, Patient Health Questionnaire, EuroQOL, and Medical Outcomes Study Social Support Survey, and Self-Care of Coronary Heart Disease Inventory. The heuristic evaluation was completed by four design experts who examined the usability of the prototype interfaces. User acceptance testing was completed with 13 patients and 10 cardiologists who evaluated prototype user interfaces of the e-PROM system.

CONCLUSION

Both patients and physicians found the APPROACH e-PROM system to be easy to use, understandable, and acceptable. The APPROACH e-PROM system provides a user-informed electronic platform designed to incorporate PROMs into the delivery of individualized cardiac care for persons with CAD.

Longitudinal assessment of real-world patient adherence: a 12-month electronic patient-reported outcomes follow-up of women with early breast cancer undergoing treatment

BACKGROUND

Electronic patient-reported outcomes (ePROs) assess patients' health status and quality of life, improving patient care and treatment effects, yet little is known about their use and adherence in routine patient care.

AIMS

We evaluated the adherence of invasive breast cancer and ductal carcinoma in situ (DCIS) patients to ePROs follow-up and whether specific patient characteristics are related to longitudinal non-adherence.

METHODS

Since November 2016, the Breast Center at Charité - Universitätsmedizin Berlin has implemented an ongoing prospective PRO routine program, requiring patients to complete ePROs assessments and consent to email-based follow-up in the first 12 months after therapy starts. Frequencies and summary statistics are presented. Multiple logistic regression models were performed to determine an association between patient characteristics and non-adherence.

RESULTS

Out of 578 patients, 239 patients (41.3%, 95%CI: 37.3-45.5%) completed baseline assessment and all five ePROs follow-up during the first 12 months after therapy. On average, above 70% of those patients responded to the ePROs follow-up assessment. Adherence to the ePROs follow-up was higher during the COVID-19 pandemic than in the time periods before (47.4% (111/234) vs. 33.6% (71/211)). Factors associated with longitudinal non-adherence were younger age, a higher number of comorbidities, no chemotherapy, and a low physical functioning score in the EORTC QLQ-C30 at baseline.

CONCLUSIONS

The study reveals moderate adherence to 12-month ePROs follow-up assessments in invasive early breast cancer and DCIS patients, with response rates ranging from 60 to 80%. Emphasizing the benefits for young patients and those with high disease burdens might further increase adherence.

Adverse Event Signal Detection Using Patients' Concerns in Pharmaceutical Care Records: Evaluation of Deep Learning Models

BACKGROUND

Early detection of adverse events and their management are crucial to improving anticancer treatment outcomes, and listening to patients' subjective opinions (patients' voices) can make a major contribution to improving safety management. Recent progress in deep learning technologies has enabled various new approaches for the evaluation of safety-related events based on patient-generated text data, but few studies have focused on the improvement of real-time safety monitoring for individual patients. In addition, no study has yet been performed to validate deep learning models for screening patients' narratives for clinically important adverse event signals that require medical intervention. In our previous work, novel deep learning models have been developed to detect adverse event signals for hand-foot syndrome or adverse events limiting patients' daily lives from the authored narratives of patients with cancer, aiming ultimately to use them as safety monitoring support tools for individual patients.

OBJECTIVE

This study was designed to evaluate whether our deep learning models can screen clinically important adverse event signals that require intervention by health care professionals. The applicability of our deep learning models to data on patients' concerns at pharmacies was also assessed.

METHODS

Pharmaceutical care records at community pharmacies were used for the evaluation of our deep learning models. The records followed the SOAP format, consisting of subjective (S), objective (O), assessment (A), and plan (P) columns. Because of the unique combination of patients' concerns in the S column and the professional records of the pharmacists, this was considered a suitable data for the present purpose. Our deep learning models were applied to the S records of patients with cancer, and the extracted adverse event signals were assessed in relation to medical actions and prescribed drugs.

RESULTS

From 30,784 S records of 2479 patients with at least 1 prescription of anticancer drugs, our deep learning models extracted true adverse event signals with more than 80% accuracy for both hand-foot syndrome (n=152, 91%) and adverse events limiting patients' daily lives (n=157, 80.1%). The deep learning models were also able to screen adverse event signals that require medical intervention by health care providers. The extracted adverse event signals could reflect the side effects of anticancer drugs used by the patients based on analysis of prescribed anticancer drugs. "Pain or numbness" (n=57, 36.3%), "fever" (n=46, 29.3%), and "nausea" (n=40, 25.5%) were common symptoms out of the true adverse event signals identified by the model for adverse events limiting patients' daily lives.

CONCLUSIONS

Our deep learning models were able to screen clinically important adverse event signals that require intervention for symptoms. It was also confirmed that these deep learning models could be applied to patients' subjective information recorded in pharmaceutical care records accumulated during pharmacists' daily work.

Development and validation of the Convalescence Symptom Assessment Scale for EsophageCtomy patients

AIM

A specific, valid and reliable measure is much needed to dynamically assess the recovery of symptoms in oesophagectomy patients. This study describes developing and validating the Convalescent Symptom Assessment Scale for oesophagectomy patients (CSAS_EC).

DESIGN

An instrument development and cross-sectional validation study was conducted.

METHODS

This study consists of two components: instrument development and psychometric tests. In instrument development, the literature review, qualitative interviews, Delphi method expert consultation and face validation were used to develop and refine scale content. In psychometric tests, the clinical test version scale was used to conduct a cross-sectional in the thoracic surgery department from 17 June to 20 November 2022. The Classical Test Theory and Multidimensional Item Response Theory (MIRT) analyses examined psychometric properties.

RESULTS

In instrument development, literature review (n = 20), qualitative interviews (n = 21), expert consultation (n = 12) and pre-survey (n = 15) led to the development of the clinical test version scale. In psychometric tests, a total of 331 participants were enrolled. Confirmatory factor analysis and MIRT analysis verified that a model with 28 items in four dimensions was good. The four dimensions were early recovery symptoms, late recovery symptoms, persistent present symptoms and psychosocial symptoms. The Cronbach's α is 0.827. The validity and reliability were demonstrated to be acceptable.

CONCLUSIONS

The CSAS_EC scale can be used as a tool to evaluate the recovery status of oesophagectomy patients.

Selecting patient-reported outcome measures for a patient-facing technology

Objective

This article provides insight into our process and considerations for selecting patient-reported outcome measures (PROMs) designed for self-reporting symptoms and quality-of-life among breast cancer (BCA) patients undergoing oral anticancer agent treatment via a patient-facing technology (PFT) platform.

Methods

Following established guidelines, we conducted a thorough assessment of a specific set of PROMs, comparing their content to identify the most suitable options for studying BCA patients.

Results

We recommend utilizing the combination of EORTC QLQ-C30 + EORTC QLQ-BR45 as the preferred instrument, especially when developing a dedicated "breast cancer-only" application.

Discussion

When developing and maintaining a dashboard for a PFT platform that includes multiple cancer types, it is important to consider the feasibility of interface design and workload. To achieve this, we recommend using PRO-CTCAE+PROMIS 10 GH for the PFT. Moreover, it is important to consider adding ad hoc items to complement the chosen PROM(s).

Conclusion

This article describes our efforts to identify PROMs for self-reported data while considering patient and developer burdens, providing guidance to PFT developers facing similar challenges in PROM selection.

Development and Validation of Oral Health-Related Quality of Life Scale for Patients Undergoing Endodontic Treatment (OHQE) for Irreversible Pulpitis

An oral health-related quality of life measure specific to patients undergoing endodontic treatment has not been developed. This study aimed to validate the oral health-related quality of life scale for patients undergoing endodontic treatment (OHQE) for irreversible pulpitis, comprised of 42 questions. Sixty-two patients with irreversible pulpitis, comprising 23 (37.1%) males and 39 (62.9%) females, were enrolled between August 2022 and February 2023. Data were collected at three time points: pretreatment, post-treatment, and at the second week post-treatment. Factor analysis revealed physical, psychological, and expectations as subscales of OHQE. Cronbach's alpha coefficients ranged from 0.87 to 0.95 for each subscale. Each subscale of the General Oral Health Assessment Index (GOHAI) was moderately correlated with the OHQE subscales. Good-poor analysis revealed a significant difference between the high-scoring and low-scoring groups for each OHQE subscale. The intraclass correlation coefficients of the OHQE subscales ranged from 0.89 to 0.95. Multivariate linear regression analysis revealed a significant correlation between the pretreatment and post-treatment psychological factors (p < 0.05). Thus, OHQE will help researchers and policymakers understand the impact of oral health on the quality of life of patients with irreversible pulpitis undergoing endodontic treatment. OHQE could contribute to the appropriate planning, treatment decisions, and management of dental treatment.

Adverse event signal extraction from cancer patients' narratives focusing on impact on their daily-life activities

Adverse event (AE) management is important to improve anti-cancer treatment outcomes, but it is known that some AE signals can be missed during clinical visits. In particular, AEs that affect patients' activities of daily living (ADL) need careful monitoring as they may require immediate medical intervention. This study aimed to build deep-learning (DL) models for extracting signals of AEs limiting ADL from patients' narratives. The data source was blog posts written in Japanese by breast cancer patients. After pre-processing and annotation for AE signals, three DL models (BERT, ELECTRA, and T5) were trained and tested in three different approaches for AE signal identification. The performances of the trained models were evaluated in terms of precision, recall, and F1 scores. From 2,272 blog posts, 191 and 702 articles were identified as describing AEs limiting ADL or not limiting ADL, respectively. Among tested DL modes and approaches, T5 showed the best F1 scores to identify articles with AE limiting ADL or all AE: 0.557 and 0.811, respectively. The most frequent AE signals were "pain or numbness", "fatigue" and "nausea". Our results suggest that this AE monitoring scheme focusing on patients' ADL has potential to reinforce current AE management provided by medical staff.

Digital Transformation of Cancer Care in the Era of Big Data, Artificial Intelligence and Data-Driven Interventions: Navigating the Field

OBJECTIVES

To navigate the field of digital cancer care and define and discuss key aspects and applications of big data analytics, artificial intelligence (AI), and data-driven interventions.

DATA SOURCES

Peer-reviewed scientific publications and expert opinion.

CONCLUSION

The digital transformation of cancer care, enabled by big data analytics, AI, and data-driven interventions, presents a significant opportunity to revolutionize the field. An increased understanding of the lifecycle and ethics of data-driven interventions will enhance development of innovative and applicable products to advance digital cancer care services.

IMPLICATIONS FOR NURSING PRACTICE

As digital technologies become integrated into cancer care, nurse practitioners and scientists will be required to increase their knowledge and skills to effectively use these tools to the patient's benefit. An enhanced understanding of the core concepts of AI and big data, confident use of digital health platforms, and ability to interpret the outputs of data-driven interventions are key competencies. Nurses in oncology will play a crucial role in patient education around big data and AI, with a focus on addressing any arising questions, concerns, or misconceptions to foster trust in these technologies. Successful integration of data-driven innovations into oncology nursing practice will empower practitioners to deliver more personalized, effective, and evidence-based care.

Real time patient-reported outcome measures in patients with cancer: Early experience within an integrated health system

BACKGROUND

While patient-reported outcome measures (PROMs) have benefit in cancer clinical trials, real-world applications are lacking. This study describes the method of implementation of a cancer enterprise-wide PROMs platform.

METHODS

After establishing a multispecialty stakeholder group within a large integrated health system, domain-specific instruments were selected from the National Institutes of Health's Patient-Reported Outcomes Measurement Information System (PROMIS) instruments (pain interference, fatigue, physical function, and depression) and were administered at varying frequencies throughout each patient's cancer journey. All cancer patients with an oncologic visit were eligible to complete the PROMs prior to the visit using a patient portal, or at the time of the visit using a tablet. PROMs were integrated into clinical workflow. Clinical partnerships were essential for successful implementation. Descriptive preliminary data were compared using multivariable logistic regression to determine the factors associated with method of PROMs completion.

RESULTS

From September 16, 2020 to July 23, 2021, 23 of 38 clinical units (60.5%) implemented PROMs over 2392 encounters and 1666 patients. Approximately one third of patients (n = 629, 37.8%) used the patient portal. Black patients (aOR 0.70; 95% CI: 0.51-0.97) and patients residing in zip codes with higher percentage of unemployment (aOR: 0.07, 95% CI: 0.01-0.41) were among the least likely to complete PROMs using the patient portal.

CONCLUSIONS

Successful system-wide implementation of PROMs among cancer patients requires engagement from multispecialty stakeholders and investment from clinical partners. Attention to the method of PROMs collection is required in order to reduce the potential for disparities, such as Black populations and those residing in areas with high levels of unemployment.

Supportive care needs and psychosocial outcomes of rural versus urban women with breast cancer

OBJECTIVE

To identify whether supportive care needs vary according to remoteness and area-level socio-economic status (SES) and to identify the combinations of socio-demographic, area-level and health factors that are associated with poorer quality of life, psychological distress and severity of unmet supportive care needs METHODS: Cross sectional data was collected from women with a breast cancer diagnosis (n=2,635) in Queensland, Australia, through a telephone survey including socio-demographic, health, psychosocial and supportive care needs measures. Hierarchical regression and cluster analyses were applied to assess the predictors of unmet need and psychosocial outcomes and to identify socio-demographic and health status profiles of women, comparing their level of unmet needs and psychosocial outcomes.

RESULTS

Women living in outer regional areas reported the highest severity of unmet need in the patient care domain. Greater unmet need for health systems and information and patient care was also evident for those in moderately and most disadvantaged areas. Three clusters were identified reflecting (1) older women with poorer health and lower education (19%); (2) younger educated women with better health and private insurance (61%); and (3) physically active women with localised cancer who had completed treatment (20%). Poorer outcomes were evident in the first two of these clusters.

CONCLUSIONS

This better understanding of the combinations of characteristics associated with poorer psychosocial outcomes and higher unmet need can be used to identify women with higher supportive care needs early and to target interventions. This article is protected by copyright. All rights reserved.

Affective Concept-Based Encoding of Patient Narratives via Sentic Computing and Neural Networks

The automatic generation of features without human intervention is the most critical task for biomedical sentiment analysis. Regarding the high dynamicity of shared patient narrative data, the lack of formal medical language sentiment dictionaries prevents retrieval of the appropriate sentiment, which is unapproachable and can be prone to annotator bias. We propose a novel affective biomedical concept-based encoding via sentic computing and neural networks. The main contributions include four aspects. First, a biomedical embedding, in which a medical entity is defined, normalized, and synthesized from a text, is built using online patient narratives after being combined with label propagation from a widely used comprehensive biomedical vocabulary. Second, considering the dependence on biomedical definitions, drug reaction sample selection based on general matching is suggested. These feature settings are then used to build and recognize affective semantics and sentics based on an extreme learning machine. Finally, a semisupervised LSTM-BiLSTM model for biomedical sentiment analysis is constructed. There was a massive influx of patient self-reports related to the COVID-19 pandemic. A study was conducted in this direction, and we tested the validity, medical language familiarity, and transferability of our approach by analyzing millions of COVID-19 tweets. Comparisons to affective lexicons also indicate that integrating extreme learning machine cognitive capabilities has advantages over biomedical sentiment analysis. By considering sentics vectors on top of the formed embeddings, our semisupervised LSTM-BiLSTM achieved an accuracy of 87.5%. The evaluations of unsupervised learning approximated the results of the previous model when dealing with a serious loss of biomedical data. In this paper, we demonstrate the effectiveness of integrating deep-learning-based cognitive capabilities for both enhancing distributed biomedical definitions and inferring sentiment compositions from many patient self-reports on social networks. The relevant encoding of affective information conveyed regarding medication subjects clearly reveals defined roles and expectations that can have a positive impact on public health.