A systematic review and meta-analysis of digital application use in clinical research in pain medicine

IMPORTANCE

Pain is a silent global epidemic impacting approximately a third of the population. Pharmacological and surgical interventions are primary modes of treatment. Cognitive/behavioural management approaches and interventional pain management strategies are approaches that have been used to assist with the management of chronic pain. Accurate data collection and reporting treatment outcomes are vital to addressing the challenges faced. In light of this, we conducted a systematic evaluation of the current digital application landscape within chronic pain medicine.

OBJECTIVE

The primary objective was to consider the prevalence of digital application usage for chronic pain management. These digital applications included mobile apps, web apps, and chatbots.

DATA SOURCES

We conducted searches on PubMed and ScienceDirect for studies that were published between 1st January 1990 and 1st January 2021.

STUDY SELECTION

Our review included studies that involved the use of digital applications for chronic pain conditions. There were no restrictions on the country in which the study was conducted. Only studies that were peer-reviewed and published in English were included. Four reviewers had assessed the eligibility of each study against the inclusion/exclusion criteria. Out of the 84 studies that were initially identified, 38 were included in the systematic review.

DATA EXTRACTION AND SYNTHESIS

The AMSTAR guidelines were used to assess data quality. This assessment was carried out by 3 reviewers. The data were pooled using a random-effects model.

MAIN OUTCOMES AND MEASURES

Before data collection began, the primary outcome was to report on the standard mean difference of digital application usage for chronic pain conditions. We also recorded the type of digital application studied (e.g., mobile application, web application) and, where the data was available, the standard mean difference of pain intensity, pain inferences, depression, anxiety, and fatigue.

RESULTS

38 studies were included in the systematic review and 22 studies were included in the meta-analysis. The digital interventions were categorised to web and mobile applications and chatbots, with pooled standard mean difference of 0.22 (95% CI: -0.16, 0.60), 0.30 (95% CI: 0.00, 0.60) and -0.02 (95% CI: -0.47, 0.42) respectively. Pooled standard mean differences for symptomatologies of pain intensity, depression, and anxiety symptoms were 0.25 (95% CI: 0.03, 0.46), 0.30 (95% CI: 0.17, 0.43) and 0.37 (95% CI: 0.05, 0.69), respectively. A sub-group analysis was conducted on pain intensity due to the heterogeneity of the results (I ² = 82.86%; p = 0.02). After stratifying by country, we found that digital applications were more likely to be effective in some countries (e.g., United States, China) than others (e.g., Ireland, Norway).

CONCLUSIONS AND RELEVANCE

The use of digital applications in improving pain-related symptoms shows promise, but further clinical studies would be needed to develop more robust applications.

SYSTEMATIC REVIEW REGISTRATION

https://www.crd.york.ac.uk/prospero/, identifier: CRD42021228343.

Randomized Controlled Trials of Artificial Intelligence in Clinical Practice: Systematic Review

BACKGROUND

The number of artificial intelligence (AI) studies in medicine has exponentially increased recently. However, there is no clear quantification of the clinical benefits of implementing AI-assisted tools in patient care.

OBJECTIVE

This study aims to systematically review all published randomized controlled trials (RCTs) of AI-assisted tools to characterize their performance in clinical practice.

METHODS

CINAHL, Cochrane Central, Embase, MEDLINE, and PubMed were searched to identify relevant RCTs published up to July 2021 and comparing the performance of AI-assisted tools with conventional clinical management without AI assistance. We evaluated the primary end points of each study to determine their clinical relevance. This systematic review was conducted following the updated PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) 2020 guidelines.

RESULTS

Among the 11,839 articles retrieved, only 39 (0.33%) RCTs were included. These RCTs were conducted in an approximately equal distribution from North America, Europe, and Asia. AI-assisted tools were implemented in 13 different clinical specialties. Most RCTs were published in the field of gastroenterology, with 15 studies on AI-assisted endoscopy. Most RCTs studied biosignal-based AI-assisted tools, and a minority of RCTs studied AI-assisted tools drawn from clinical data. In 77% (30/39) of the RCTs, AI-assisted interventions outperformed usual clinical care, and clinically relevant outcomes improved with AI-assisted intervention in 70% (21/30) of the studies. Small sample size and single-center design limited the generalizability of these studies.

CONCLUSIONS

There is growing evidence supporting the implementation of AI-assisted tools in daily clinical practice; however, the number of available RCTs is limited and heterogeneous. More RCTs of AI-assisted tools integrated into clinical practice are needed to advance the role of AI in medicine.

TRIAL REGISTRATION

PROSPERO CRD42021286539; https://www.crd.york.ac.uk/prospero/display_record.php?RecordID=286539.

Co-created Mobile Apps for Palliative Care Using Community-Partnered Participatory Research: Development and Usability Study

Background

Open design formats for mobile apps help clinicians and stakeholders bring their needs to direct, co-creative solutions. Palliative care for patients with advanced cancers requires intensive monitoring and support and remains an area in high need for innovation.

Objective

This study aims to use community-partnered participatory research to co-design and pretest a mobile app that focuses on palliative care priorities of clinicians and patients with advanced cancer.

Methods

In-person and teleconference workshops were held with patient and family stakeholders, researchers, and clinicians in palliative care and oncology. Question prompts, written feedback, semistructured interviews, and facilitated group discussions identified the core palliative care needs. Using Chorus, a no-code app-building platform, a mobile app was co-designed with the stakeholders. A pretest with 11 patients was conducted, with semistructured interviews of clinician and patient users for feedback.

Results

Key themes identified from the focus groups included needs for patient advocacy and encouragement, access to vetted information, patient-clinician communication support, and symptom management. The initial prototype, My Wellness App, contained a weekly wellness journal to track patient-reported symptoms, goals, and medication use; information on self-management of symptoms; community resources; and patient and caregiver testimonial videos. Initial pretesting identified value in app-based communication for clinicians, patients, and caregivers, with suggestions for improving user interface, feedback and presentation of symptom reports, and gamification and staff coordinators to support patient app engagement.

Conclusions

The development of a mobile app using community-partnered participatory research is a low-technology and feasible intervention for palliative care. Iterative redesign and user interface expertise may improve implementation.

Active surveillance of chemotherapy-related symptom burden in ambulatory cancer patients via the implementation of electronic patient-reported outcomes and sensor-enabled vital signs capture: protocol for a decentralised feasibility pilot study

INTRODUCTION

Remote patient monitoring (RPM) has emerged as a potential avenue for optimising the management of symptoms in patients undergoing chemotherapy. However, RPM is a complex, multilevel intervention with technology, workflow, contextual and patient experience components. The purpose of this pilot study is to determine the feasibility of RPM protocol implementation with respect to decentralised recruitment, patient retention, adherence to reporting recommendations, RPM platform usability and patient experience in ambulatory cancer patients at high risk for chemotherapy-related symptoms.

METHODS AND ANALYSIS

This protocol describes a single-arm decentralised feasibility pilot study of technology-enhanced outpatient symptom management system in patients with gastrointestinal and thoracic cancer receiving chemotherapy and cancer care at a single site (MD Anderson Cancer Center, Houston Texas). An anticipated total of 25 patients will be recruited prior to the initiation of chemotherapy and provided with a set of validated questionnaires at enrollment and after our 1-month feasibility pilot trial period. Our intervention entails the self-reporting of symptoms and vital signs via a HIPAA-compliant, secure tablet interface that also enables (1) the provision of self-care materials to patients, (2) generation of threshold alerts to a dedicated call-centre and (3) videoconferencing. Vital sign information (heart rate, blood pressure, pulse, oxygen saturation, weight and temperature) will be captured via Bluetooth-enabled biometric monitoring devices which are integrated with the tablet interface. Protocolised triage and management of symptoms will occur in response to the alerts. Feasibility and acceptability metrics will characterise our recruitment process, protocol adherence, patient retention and usability of the RPM platform. We will also document the perceived effectiveness of our intervention by patients.

ETHICS AND DISSEMINATION

This study has been granted approval by the institutional review board of MD Anderson Cancer Center. We anticipate dissemination of our pilot and subsequent effectiveness trial results via presentations at national conferences and peer-reviewed publications in the relevant medical journals. Our results will also be made available to cancer survivors, their caregivers and hospital administration.

TRIAL REGISTRATION NUMBER

NCI202107464.

Artificial intelligence in oncology: Path to implementation

In recent years, the field of artificial intelligence (AI) in oncology has grown exponentially. AI solutions have been developed to tackle a variety of cancer‐related challenges. Medical institutions, hospital systems, and technology companies are developing AI tools aimed at supporting clinical decision making, increasing access to cancer care, and improving clinical efficiency while delivering safe, high‐value oncology care. AI in oncology has demonstrated accurate technical performance in image analysis, predictive analytics, and precision oncology delivery. Yet, adoption of AI tools is not widespread, and the impact of AI on patient outcomes remains uncertain. Major barriers for AI implementation in oncology include biased and heterogeneous data, data management and collection burdens, a lack of standardized research reporting, insufficient clinical validation, workflow and user‐design challenges, outdated regulatory and legal frameworks, and dynamic knowledge and data. Concrete actions that major stakeholders can take to overcome barriers to AI implementation in oncology include training and educating the oncology workforce in AI; standardizing data, model validation methods, and legal and safety regulations; funding and conducting future research; and developing, studying, and deploying AI tools through multidisciplinary collaboration.