A Systematic Review of Clinical Decision Support Systems for Clinical Oncology Practice

A qualitative analysis of algorithm-based decision support usability testing for symptom management across the trajectory of cancer care: one size does not fit all

BACKGROUND

Adults with cancer experience symptoms that change across the disease trajectory. Due to the distress and cost associated with uncontrolled symptoms, improving symptom management is an important component of quality cancer care. Clinical decision support (CDS) is a promising strategy to integrate clinical practice guideline (CPG)-based symptom management recommendations at the point of care.

METHODS

The objectives of this project were to develop and evaluate the usability of two symptom management algorithms (constipation and fatigue) across the trajectory of cancer care in patients with active disease treated in comprehensive or community cancer care settings to surveillance of cancer survivors in primary care practices. A modified ADAPTE process was used to develop algorithms based on national CPGs. Usability testing involved semi-structured interviews with clinicians from varied care settings, including comprehensive and community cancer centers, and primary care. The transcripts were analyzed with MAXQDA using Braun and Clarke's thematic analysis method. A cross tabs analysis was also performed to assess the prevalence of themes and subthemes by cancer care setting.

RESULTS

A total of 17 clinicians (physicians, nurse practitioners, and physician assistants) were interviewed for usability testing. Three main themes emerged: (1) Algorithms as useful, (2) Symptom management differences, and (3) Different target end-users. The cross-tabs analysis demonstrated differences among care trajectories and settings that originated in the Symptom management differences theme. The sub-themes of "Differences between diseases" and "Differences between care trajectories" originated from participants working in a comprehensive cancer center, which tends to be disease-specific locations for patients on active treatment. Meanwhile, participants from primary care identified the sub-theme of "Differences in settings," indicating that symptom management strategies are care setting specific.

CONCLUSIONS

While CDS can help promote evidence-based symptom management, systems providing care recommendations need to be specifically developed to fit patient characteristics and clinical context. Findings suggest that one set of algorithms will not be applicable throughout the entire cancer trajectory. Unique CDS for symptom management will be needed for patients who are cancer survivors being followed in primary care settings.

Clinical decision support systems for multidisciplinary team decision-making in patients with solid cancer: Composition of an implementation model based on a scoping review

Generating guideline-based recommendations during multidisciplinary team (MDT) meetings in solid cancers is getting more complex due to increasing amount of information needed to follow the guidelines. Usage of clinical decision support systems (CDSSs) can simplify and optimize decision-making. However, CDSS implementation is lagging behind. Therefore, we aim to compose a CDSS implementation model. By performing a scoping review of the currently reported CDSSs for MDT decision-making we determined 102 barriers and 86 facilitators for CDSS implementation out of 44 papers describing 20 different CDSSs. The most frequently reported barriers and facilitators for CDSS implementation supporting MDT decision-making concerned CDSS maintenance (e.g. incorporating guideline updates), validity of recommendations and interoperability with electronic health records. Based on the identified barriers and facilitators, we composed a CDSS implementation model describing clinical utility, analytic validity and clinical validity to guide CDSS integration more successfully in the clinical workflow to support MDTs in the future.

Evaluating clinical decision support software (CDSS): challenges for robust evidence generation

OBJECTIVES

Computerized clinical decision support software (CDSS) are digital health technologies that have been traditionally categorized as medical devices. However, the evaluation frameworks for traditional medical devices are not well adapted to assess the value and safety of CDSS. In this study, we identified a range of challenges associated with CDSS evaluation as a medical device and investigated whether and how CDSS are evaluated in Australia.

METHODS

Using a qualitative approach, we interviewed 11 professionals involved in the implementation and evaluation of digital health technologies at national and regional levels. Data were thematically analyzed using both data-driven (inductive) and theory-based (deductive) approaches.

RESULTS

Our results suggest that current CDSS evaluations have an overly narrow perspective on the risks and benefits of CDSS due to an inability to capture the impact of the technology on the sociotechnical environment. By adopting a static view of the CDSS, these evaluation frameworks are unable to discern how rapidly evolving technologies and a dynamic clinical environment can impact CDSS performance. After software upgrades, CDSS can transition from providing information to specifying diagnoses and treatments. Therefore, it is not clear how CDSS can be monitored continuously when changes in the software can directly affect patient safety.

CONCLUSION

Our findings emphasize the importance of taking a living health technology assessment approach to the evaluation of digital health technologies that evolve rapidly. There is a role for observational (real-world) evidence to understand the impact of changes to the technology and the sociotechnical environment on CDSS performance.

Timing, Indicators, and Approaches to Digital Patient Experience Evaluation: Umbrella Systematic Review

BACKGROUND

The increasing prevalence of DH applications has outpaced research and practice in digital health (DH) evaluations. Patient experience (PEx) was reported as one of the challenges facing the health system by the World Health Organization. To generate evidence on DH and promote the appropriate integration and use of technologies, a standard evaluation of PEx in DH is required.

OBJECTIVE

This study aims to systematically identify evaluation timing considerations (ie, when to measure), evaluation indicators (ie, what to measure), and evaluation approaches (ie, how to measure) with regard to digital PEx. The overall aim of this study is to generate an evaluation guide for further improving digital PEx evaluation.

METHODS

This is a 2-phase study parallel to our previous study. In phase 1, literature reviews related to PEx in DH were systematically searched from Scopus, PubMed, and Web of Science databases. Two independent raters conducted 2 rounds of paper screening, including title and abstract screening and full-text screening, and assessed the interrater reliability for 20% (round 1: 23/115 and round 2: 12/58) random samples using the Fleiss-Cohen coefficient (round 1: k1=0.88 and round 2: k2=0.80). When reaching interrater reliability (k>0.60), TW conducted the rest of the screening process, leaving any uncertainties for group discussions. Overall, 38% (45/119) of the articles were considered eligible for further thematic analysis. In phase 2, to check if there were any meaningful novel insights that would change our conclusions, we performed an updated literature search in which we collected 294 newly published reviews, of which 102 (34.7%) were identified as eligible articles. We considered them to have no important changes to our original results on the research objectives. Therefore, they were not integrated into the synthesis of this review and were used as supplementary materials.

RESULTS

Our review highlights 5 typical evaluation objectives that serve 5 stakeholder groups separately. We identified a set of key evaluation timing considerations and classified them into 3 categories: intervention maturity stages, timing of the evaluation, and timing of data collection. Information on evaluation indicators of digital PEx was identified and summarized into 3 categories (intervention outputs, patient outcomes, and health care system impact), 9 themes, and 22 subthemes. A set of evaluation theories, common study designs, data collection methods and instruments, and data analysis approaches was captured, which can be used or adapted to evaluate digital PEx.

CONCLUSIONS

Our findings enabled us to generate an evaluation guide to help DH intervention researchers, designers, developers, and program evaluators evaluate digital PEx. Finally, we propose 6 directions for encouraging further digital PEx evaluation research and practice to address the challenge of poor PEx.

The Facilitators and Barriers of the Implementation of a Clinical Decision Support System for Breast Cancer Multidisciplinary Team Meetings-An Interview Study

BACKGROUND

AI-driven clinical decision support systems (CDSSs) hold promise for multidisciplinary team meetings (MDTMs). This study aimed to uncover the hurdles and aids in implementing CDSSs during breast cancer MDTMs.

METHODS

Twenty-four core team members from three hospitals engaged in semi-structured interviews, revealing a collective interest in experiencing CDSS workflows in clinical practice. All interviews were audio recorded, transcribed verbatim and analyzed anonymously. A standardized approach, 'the framework method', was used to create an analytical framework for data analysis, which was performed by two independent researchers.

RESULTS

Positive aspects included improved data visualization, time-saving features, automated trial matching, and enhanced documentation transparency. However, challenges emerged, primarily concerning data connectivity, guideline updates, the accuracy of AI-driven suggestions, and the risk of losing human involvement in decision making. Despite the complexities involved in CDSS development and integration, clinicians demonstrated enthusiasm to explore its potential benefits.

CONCLUSIONS

Acknowledging the multifaceted nature of this challenge, insights into the barriers and facilitators identified in this study offer a potential roadmap for smoother future implementations. Understanding these factors could pave the way for more effective utilization of CDSSs in breast cancer MDTMs, enhancing patient care through informed decision making.

Evaluation of clinical decision support systems in oncology: An updated systematic review

With increasing reliance on technology in oncology, the impact of digital clinical decision support (CDS) tools needs to be examined. A systematic review update was conducted and peer-reviewed literature from 2016 to 2022 were included if CDS tools were used for live decision making and comparatively assessed quantitative outcomes. 3369 studies were screened and 19 were included in this updated review. Combined with a previous review of 24 studies, a total of 43 studies were analyzed. Improvements in outcomes were observed in 42 studies, and 34 of these were of statistical significance. Computerized physician order entry and clinical practice guideline systems comprise the greatest number of evaluated CDS tools (13 and 10 respectively), followed by those that utilize patient-reported outcomes (8), clinical pathway systems (8) and prescriber alerts for best-practice advisories (4). Our review indicates that CDS can improve guideline adherence, patient-centered care, and care delivery processes in oncology.

Identification of barriers at the primary care provider level to improve inflammatory breast cancer diagnosis and management

The purpose of this study, based in the United States, was to evaluate knowledge gaps and barriers related to diagnosis and care of inflammatory breast cancer (IBC), a rare but lethal breast cancer subtype, amongst Primary Care Providers (PCP) as they are often the first point of contact when patients notice initial symptoms. PCP participants in the Duke University Health System, federally qualified health center, corporate employee health and community practices, nearby academic medical center, Duke physician assistant and advanced practice nurse leadership program alumni were first selected in a convenience sample and for semi-structured interviews (n = 11). Based on these data, an online survey tool was developed and disseminated (n = 78) to assess salient measures of IBC diagnosis, health disparity factors, referral and care coordination practices, COVID-19 impact, and continuing medical education (CME). PCP reported access to care and knowledge gaps in symptom recognition (mean = 3.3, range 1-7) as major barriers. Only 31 % reported ever suspecting IBC in a patient. PCP (n = 49) responded being challenged with referral delays in diagnostic imaging. Additionally, since the COVID-19 pandemic started, 63 % reported breast cancer referral delays, and 33 % reported diagnosing less breast cancer. PCP stated interest in CME in their practice for improved diagnosis and patient care, which included online (53 %), lunch time or other in-service training (33 %), patient and provider-facing websites (32 %). Challenges communicating rare cancer information, gaps in confidence in diagnosing IBC, and timely follow-up with patients and specialists underscores the need for developing PCP educational modules to improve guideline-concordant care.

An Analysis of Clinical and Systemic Factors Associated with Palliative Radiotherapy Delivery and Completion at the End of Life in Alberta, Canada

Radiotherapy (RT) is often utilized for symptom control at the end of life. Palliative RT (pRT) may not be taken to completion by patients, thus decreasing clinical benefits and adversely impacting resource allocation. We determined rates of incomplete pRT and examined predictors of non-completion using an electronic questionnaire. Methods: A questionnaire was embedded within the RT electronic prescribing system for all five cancer centers of Alberta, Canada, between 2017 and 2020. Prescribing radiation oncologists (ROs) were tasked with completing the questionnaire. Treatment variables were collected for 2040 patients prescribed pRT. Details on pRT courses delivered and completed were used to determine rates of incomplete RT. Electronic medical records of a subset of 367 patients randomly selected from the 2040 patients were then analyzed to examine for association of non-completion of RT with patient, disease, and therapy-related factors. Results: Overall, 10% of patients did not complete pRT. The rate of single fractions prescribed as a proportion of all RT fractions increased from 18% (pre-2017: pre-study era) to 29% (2017-2020: study era) (p < 0.0001). After conducting multivariate analysis on the overall group, multiple lifetime malignancies (OR:0.64) or increasing the number of pRT fractions (OR:0.08-0.17) were associated with non-completion. Being selected for stereotactic RT (OR:3.75) or survival > 30 days post-RT prescription (OR:2.20-5.02) were associated with greater rates of RT completion. The ROs' estimates of life expectancy at the time of RT prescription were not predictive of RT completion. In the multivariate analysis of the 367-patient subset, the presence of hepatic metastases (OR 2.59), survival 30-59 days (OR 6.61) and survival 90+ days (OR 8.18) post-RT prescription were associated with pRT completion. Only increasing pRT fractionation (OR:0.05-0.2) was associated with non-completion. Conclusion: One in ten patients prescribed pRT did not complete their treatment course. Decreasing pRT fractionation and improving prognostication in patients near the end of life may decrease rates of incomplete RT courses.

Evaluation of a Decision Support System Developed with Deep Learning Approach for Detecting Dental Caries with Cone-Beam Computed Tomography Imaging

This study aims to investigate the effect of using an artificial intelligence (AI) system (Diagnocat, Inc., San Francisco, CA, USA) for caries detection by comparing cone-beam computed tomography (CBCT) evaluation results with and without the software. 500 CBCT volumes are scored by three dentomaxillofacial radiologists for the presence of caries separately on a five-point confidence scale without and with the aid of the AI system. After visual evaluation, the deep convolutional neural network (CNN) model generated a radiological report and observers scored again using AI interface. The ground truth was determined by a hybrid approach. Intra- and inter-observer agreements are evaluated with sensitivity, specificity, accuracy, and kappa statistics. A total of 6008 surfaces are determined as 'presence of caries' and 13,928 surfaces are determined as 'absence of caries' for ground truth. The area under the ROC curve of observer 1, 2, and 3 are found to be 0.855/0.920, 0.863/0.917, and 0.747/0.903, respectively (unaided/aided). Fleiss Kappa coefficients are changed from 0.325 to 0.468, and the best accuracy (0.939) is achieved with the aided results. The radiographic evaluations performed with aid of the AI system are found to be more compatible and accurate than unaided evaluations in the detection of dental caries with CBCT images.

The effects of cancer clinical decision support systems on patient-reported outcomes: A systematic review

PURPOSE

The implementation of high-quality decision-making support are integral to ensuring the delivery of quality cancer care and subsequently achieving positive patient outcomes. Decision Support Systems (DSS) are increasingly used, however it is not known what the effects are beyond supporting the decision-making process. We aimed to identify and synthesize the available literature regarding the effects of DSS on patient-reported outcomes both during and after cancer treatment.

METHODS

A systematic review was conducted using dual processes to identify empirical literature that reported an evaluation of DSS interventions and patient-reported outcomes. We appraised study quality using the Mixed Methods Appraisal Tool (MMAT). Data were narratively synthesized.

RESULTS

We included 15 studies, categorized as symptom assessment interventions or interactive educational interventions. Findings were mixed regarding the effectiveness of DSS interventions in improving total symptom distress and severity, whereas the majority were effective in reducing mean scores for worst and usual pain. Interventions were not effective in improving other health-related patient-reported outcomes including quality of life, global distress, depression, or self-efficacy and there were mixed effects for reducing decisional conflict. There was moderate to high patient adherence to the interventions and generally high satisfaction and acceptability, yet minimal evidence for the effect of DSS interventions in clinician adherence to intervention recommendations.

CONCLUSIONS

Including patient-reported outcomes in the evaluation of DSS is critical to understand their impact. Inconsistencies in reporting of interventions may, however, be a contributing factor to heterogeneous effects of clinical DSS regarding a broad range of patient-reported outcomes.

Barcode and radio frequency identification utilization varied across Korean hospitals

Barcodes and radio frequency identification (RFID) are increasingly used in health care to improve patient safety. However, studies on their utilization in clinical settings are limited. This study aimed to comprehensively examine the utilization status of barcodes and RFID in Korean hospitals, recognize the effects and obstacles associated with utilization, and explore the measures to expand the applications of barcodes and RFID. A self-reported online survey was conducted in tertiary hospitals, general hospitals, hospitals, and nursing hospitals in the Republic of Korea. The survey questionnaire comprised questions on barcodes and RFID utilization status, the effect of barcodes and RFID utilization, measures to expand the utilization of barcodes and RFID, and information on respondents' demographics and hospitals. A representative from each of 23 tertiary hospitals, 101 general hospitals, 232 hospitals, and 214 nursing hospitals completed the survey (total response rate 17%). The data were analysed using the chi-square test or Fisher's exact test to determine the differences in responses based on the type and characteristics of hospitals. The tertiary hospitals had the highest utilizations of both RFID and barcodes (n = 10, 43.5%), whereas the nursing hospitals had the lowest (n = 96, 55.1%). Barcodes and RFID were most commonly used in the visits and security management domains. However, the use of barcodes and RFID in medication dispensing and administration safety was low, despite its value in improving patient safety. The hospitals recognized the positive effect of utilization of barcodes and RFID, reporting the highest frequency for the prevention of patient safety incidents (n = 79, 85.9%). Nevertheless, the cost of barcodes and RFID facility investments (n = 128, 90.3%) appeared to be the greatest obstacle to the introduction of barcodes and RFID. Hence, barcodes and RFID facility investment support (n = 133, 95.5%) were given the highest priority among the measures to expand barcode and RFID utilization in health care. The utilization of barcodes and RFID varied across the type and domain of hospitals in the Republic of Korea. Hospitals recognized the positive effects of barcode and RFID utilization. Nonetheless, all hospitals were concerned about the cost of investment and maintenance of barcode and RFID facilities as the main obstacles to utilization. Therefore, a support plan must be developed for the cost of barcodes and RFID facility investments to expand barcode and RFID utilization in health care.

The Constrained-Disorder Principle Assists in Overcoming Significant Challenges in Digital Health: Moving from "Nice to Have" to Mandatory Systems

The success of artificial intelligence depends on whether it can penetrate the boundaries of evidence-based medicine, the lack of policies, and the resistance of medical professionals to its use. The failure of digital health to meet expectations requires rethinking some of the challenges faced. We discuss some of the most significant challenges faced by patients, physicians, payers, pharmaceutical companies, and health systems in the digital world. The goal of healthcare systems is to improve outcomes. Assisting in diagnosing, collecting data, and simplifying processes is a "nice to have" tool, but it is not essential. Many of these systems have yet to be shown to improve outcomes. Current outcome-based expectations and economic constraints make "nice to have," "assists," and "ease processes" insufficient. Complex biological systems are defined by their inherent disorder, bounded by dynamic boundaries, as described by the constrained disorder principle (CDP). It provides a platform for correcting systems' malfunctions by regulating their degree of variability. A CDP-based second-generation artificial intelligence system provides solutions to some challenges digital health faces. Therapeutic interventions are held to improve outcomes with these systems. In addition to improving clinically meaningful endpoints, CDP-based second-generation algorithms ensure patient and physician engagement and reduce the health system's costs.

The global effect of digital health technologies on health workers' competencies and health workplace: an umbrella review of systematic reviews and lexical-based and sentence-based meta-analysis

Systematic reviews have quantified the effectiveness, feasibility, acceptability, and cost-effectiveness of digital health technologies (DHTs) used by health-care workers. We aimed to collate available evidence on technologies' effect on health-care workers' competencies and performance. We searched the Cochrane Database of Systematic Reviews, Embase, MEDLINE, Epistemonikos, and Scopus for reviews published from database inception to March 1, 2023. Studies assessing the effects of DHTs on the organisational, socioeconomic, clinical, and epidemiological levels within the workplace, and on health-care workers' performance parameters, were included. Data were extracted and clustered into 25 domains using vote counting based on the direction of effect. The relative frequency of occurrence (RFO) of each domain was estimated using R software. AMSTAR-2 tool was used to appraise the quality of reporting, and the Confidence in the Evidence from Reviews of Qualitative research approach developed by Grading of Recommendations Assessment, Development and Evaluation was used to analyse the certainty of evidence among included studies. The 12 794 screened reviews generated 132 eligible records for assessment. Top-ranked RFO identifiers showed associations of DHT with the enhancement of health-care workers' performance (10·9% [95% CI 5·3-22·5]), improvement of clinical practice and management (9·8% [3·9-24·2]), and improvement of care delivery and access to care (9·2% [4·1-20·9]). Our overview found that DHTs positively influence the daily practice of health-care workers in various medical specialties. However, poor reporting in crucial domains is widely prevalent in reviews of DHT, hindering our findings' generalisability and interpretation. Likewise, most of the included reviews reported substantially more data from high-income countries. Improving the reporting of future studies and focusing on low-income and middle-income countries might elucidate and answer current knowledge gaps.

Survival After Implementation of a Decision Support Tool to Facilitate Evidence-Based Cancer Treatment

PURPOSE

Decision support tools (DSTs) to facilitate evidence-based cancer treatment are increasingly common in care delivery organizations. Implementation of these tools may improve process outcomes, but little is known about effects on patient outcomes such as survival. We aimed to evaluate the effect of implementing a DST for cancer treatment on overall survival (OS) among patients with breast, colorectal, and lung cancer.

METHODS

We used institutional cancer registry data to identify adults treated for first primary breast, colorectal, or lung cancer between December 2013 and December 2017. Our intervention of interest was implementation of a commercial DST for cancer treatment, and outcome of interest was OS. We emulated a single-arm trial with historical comparison and used a flexible parametric model to estimate standardized 3-year restricted mean survival time (RMST) difference and mortality risk ratio (RR) with 95% confidence limits (CLs).

RESULTS

Our study population comprised 1,059 patients with cancer (323 breast, 318 colorectal, and 418 lung). Depending on cancer type, median age was 55-60 years, 45%-67% were racial/ethnic minorities, and 49%-69% were uninsured. DST implementation had little effect on survival at 3 years. The largest effect was observed among patients with lung cancer (RMST difference, 1.7 months; 95% CL, -0.26 to 3.7; mortality RR, 0.95; 95% CL, 0.88 to 1.0). Adherence with tool-based treatment recommendations was >70% before and >90% across cancers.

CONCLUSION

Our results suggest that implementation of a DST for cancer treatment has nominal effect on OS, which may be partially attributable to high adherence with evidence-based treatment recommendations before tool implementation in our setting. Our results raise awareness that improved process outcomes may not translate to improved patient outcomes in some care delivery settings.

A comparison between clinical decision support system and clinicians in breast cancer

Objective

We are building a clinical decision support system (CSCO AI) for breast cancer patients to improve the efficiency of clinical decision-making. We aimed to assess cancer treatment regimens given by CSCO AI and different levels of clinicians.

Methods

400 breast cancer patients were screened from the CSCO database. Clinicians with similar levels were randomly assigned one of the volumes (200 cases). CSCO AI was asked to assess all cases. Three reviewers were independently asked to evaluate the regimens from clinicians and CSCO AI. Regimens were masked before evaluation. The primary outcome was the proportion of high-level conformity (HLC).

Results

The overall concordance between clinicians and CSCO AI was 73.9% (3621/4900). It was 78.8% (2757/3500) in the early-stage, higher than that in the metastatic stage (61.7% [864/1400], p < 0.001). The concordance was 90.7% (635/700) and 56.4% (395/700) in adjuvant radiotherapy and second-line therapy respectively. HLC in CSCO AI was 95.8% (95%CI:94.0%-97.6%), significantly higher than that in clinicians (90.8%, 95%CI:89.8%-91.8%). Considering professions, the HLC of surgeons was 85.9%, lower than that of CSCO AI (OR = 0.25,95%CI: 0.16-0.41). The most significant difference in HLC was in first-line therapy (OR = 0.06, 95%CI:0.01-0.41). When clinicians were divided according to their levels, there was no statistical significance between CSCO AI and higher level clinicians.

Conclusions

Decision from CSCO AI for breast cancer was superior than most clinicians did except in second-line therapy. The improvements in process outcomes suggest that CSCO AI can be widely used in clinical practice.

Development and validation of the global assessment of the evidence implementation environment [GENIE] tool

BACKGROUND

Overcoming the various barriers to evidence implementation is critical to delivering evidence-based health care. Identifying and managing these obstacles is somewhat challenging however, due to interprofessional and interjurisdictional variations in reported barriers. An efficient, systematic, comprehensive and innovative approach to isolating the barriers to evidence implementation is therefore needed.

MATERIALS AND METHODS

Using a mixed methods design, the study aimed to develop, refine and validate a tool to assess the evidence implementation environment for complementary medicine (CM) professions. The tool was developed using a five-stage process, and refined and validated using a two-round e-Delphi technique.

RESULTS

Informed by reviews examining the barriers and enablers to evidence implementation in CM, and shaped by the Behaviour Change Wheel Framework, a preliminary 33-item tool was created (i.e. the Global Assessment of the Evidence Implementation Environment [GENIE] tool). A two-round Delphi technique was used to refine the criteria, with a panel of 23 experts agreeing to the removal of two criteria, and the addition of two items. In the end, the Delphi panel reached consensus on 33 criteria, which were sorted into nine stakeholder groups.

CONCLUSION

This study has for the first time, created an innovative tool to assess the capacity and capability of CM professions to engage in evidence-based practice at an optimal level. By assessing the evidence implementation environment of CM professions, the GENIE tool is able to determine where resources, infrastructure and personnel should be directed in order to optimise the uptake of evidence-based practices within CM professions.

Healthcare Professionals’ Perceptions, Barriers, and Facilitators towards Adopting Computerised Clinical Decision Support Systems in Antimicrobial Stewardship in Jordanian Hospitals

Understanding healthcare professionals’ perceptions towards a computerised decision support system (CDSS) may provide a platform for the determinants of the successful adoption and implementation of CDSS. This cross-sectional study examined healthcare professionals’ perceptions, barriers, and facilitators to adopting a CDSS for antibiotic prescribing in Jordanian hospitals. This study was conducted among healthcare professionals in Jordan’s two tertiary and teaching hospitals over four weeks (June–July 2021). Data were collected in a paper-based format from senior and junior prescribers and non-prescribers (n = 254) who agreed to complete a questionnaire. The majority (n = 184, 72.4%) were aware that electronic prescribing and electronic health record systems could be used specifically to facilitate antibiotic use and prescribing. The essential facilitator made CDSS available in a portable format (n = 224, 88.2%). While insufficient training to use CDSS was the most significant barrier (n = 175, 68.9%). The female providers showed significantly lower awareness (p = 0.006), and the nurses showed significantly higher awareness (p = 0.041) about using electronic prescribing and electronic health record systems. This study examined healthcare professionals’ perceptions of adopting CDSS in antimicrobial stewardship (AMS) and shed light on the perceived barriers and facilitators to adopting CDSS in AMS, reducing antibiotic resistance, and improving patient safety. Furthermore, results would provide a framework for other hospital settings concerned with implementing CDSS in AMS and inform policy decision-makers to react by implementing the CDSS system in Jordan and globally. Future studies should concentrate on establishing policies and guidelines and a framework to examine the adoption of the CDSS for AMS.

Clinician interest in clinical decision support for PSA-based prostate cancer screening

OBJECTIVE

To evaluate the interest of primary care clinicians in utilizing CDS for PSA screening. Evidence suggests that electronic clinical decision support (CDS) may decrease low-value prostate-specific antigen (PSA) testing. However, physician attitudes towards CDS for PSA screening are largely unknown.

METHODS

A survey was sent to 201 primary care clinicians, including both physicians and Advanced Practice Providers (APP), within a large academic health system. Eligible clinicians cared for male patients aged 40 to 80 years and ordered ≥5 PSA tests in the past year. Respondents were stratified into 3 groups, appropriate screeners, low-value screeners, or rare-screeners, based on responses to survey questions assessing PSA screening practices. The degree of interest in electronic CDS was determined via a composite Likert score comprising relevant survey items.

RESULTS

Survey response rate was 29% (59/201) consisting of 85% MD/DO and 15% APP respondents. All clinicians surveyed were interested in CDS (P < 0.001) without significant difference between screener groups. Clinicians agreed most uniformly that CDS be evidence-based. Clinicians disagreed on whether CDS would decrease professional discretion over patient decisions.

CONCLUSIONS

Primary care clinicians are interested in CDS for PSA screening regardless of their current screening practices. Prioritizing CDS features that clinicians value, such as ensuring CDS recommendations are evidence-based, may increase the likelihood of successful implementation, whereas perceived threat to autonomy may be a hinderance to utilization.

Decision Support System and Web-Application Using Supervised Machine Learning Algorithms for Easy Cancer Classifications

Using a decision support system (DSS) that classifies various cancers provides support to the clinicians/researchers to make better decisions that can aid in early cancer diagnosis, thereby reducing chances of incorrect disease diagnosis. Thus, this work aimed at designing a classification model that can predict accurately for 5 different cancer types comprising of 20 cancer exomes, using the mutations identified from whole exome cancer analysis. Initially, a basic model was designed using supervised machine learning classification algorithms such as K-nearest neighbor (KNN), support vector machine (SVM), decision tree, naïve bayes and random forest (RF), among which decision tree and random forest performed better in terms of preliminary model accuracy. However, output predictions were incorrect due to less training scores. Thus, 16 essential features were then selected for model improvement using 2 approaches. All imbalanced datasets were balanced using SMOTE. In the first approach, all features from 20 cancer exome datasets were trained and models were designed using decision tree and random forest. Balanced datasets for decision tree model showed an accuracy of 77%, while with the RF model, the accuracy improved to 82% where all 5 cancer types were predicted correctly. Area under the curve for RF model was closer to 1, than decision tree model. In the second approach, all 15 datasets were trained, while 5 were tested. However, only 2 cancer types were predicted correctly. To cross validate RF model, Matthew's correlation co-efficient (MCC) test was performed. For method 1, the MCC test and MCC cross validation was found to be 0.7796 and 0.9356 respectively. Likewise, for second approach, MCC was observed to be 0.9365, corroborating the accuracy of the designed model. The model was successfully deployed using Streamlit as a web application for easy use. This study presents insights for allowing easy cancer classifications.

[Development, Evaluation and Implementation of Digital Health Interventions (Part 1) - Discussion Paper of the Digital Health Working Group of the German Network for Health Services Research (DNVF)]

The development and application of digital interventions in health-related topics are gaining momentum in health service research. Digital interventions are often complex and need to be evaluated and implemented in complex settings. Due to their characteristics, this poses methodological challenges for health services research that have to be identified and addressed. Hence, the Working Group on Digital Health of the German Network for Health Services Research (DNVF) has prepared a discussion paper. This paper discusses methodological, practical and theoretical challenges associated with the development and evaluation of digital interventions from the perspective of health services research. Possible solutions are suggested and future research needs to address these methodological challenges are identified.

Improved guideline adherence in oncology through clinical decision-support systems: still hindered by current health IT infrastructures?

PURPOSE OF REVIEW

Despite several efforts to enhance guideline adherence in cancer management, the rate of adherence remains often dissatisfactory in clinical routine. Clinical decision-support systems (CDSS) have been developed to support the management of cancer patients by providing evidence-based recommendations. In this review, we focus on both current evidence supporting the beneficial effects of CDSS on guideline adherence as well as technical and structural requirements for CDSS implementation in clinical routine.

RECENT FINDINGS

Some studies have demonstrated a significant improvement of guideline adherence by CDSSs in oncologic diseases such as breast cancer, colon cancer, cervical cancer, prostate cancer, and hepatocellular carcinoma as well as in the management of cancer pain. However, most of these studies were rather small and designs rather simple. One reason for this limited evidence might be that CDSSs are only occasionally implemented in clinical routine. The main limitations for a broader implementation might lie in the currently existing clinical data infrastructures that do not sufficiently allow CDSS interoperability as well as in some CDSS tools themselves, if handling is hampered by poor usability.

SUMMARY

In principle, CDSSs improve guideline adherence in clinical cancer management. However, there are some technical und structural obstacles to overcome to fully implement CDSSs in clinical routine.

Computerized clinical decision support systems for prescribing in primary care: main characteristics and implementation impact-protocol of an evidence and gap map

BACKGROUND

Computerized clinical decision support systems are used by clinicians at the point of care to improve quality of healthcare processes (prescribing error prevention, adherence to clinical guidelines, etc.) and clinical outcomes (preventive, therapeutic, and diagnostics). Attempts to summarize results of computerized clinical decision support systems to support prescription in primary care have been challenging, and most systematic reviews and meta-analyses failed due to an extremely high degree of heterogeneity present among the included primary studies. The aim of our study will be to synthesize the evidence, considering all methodological factors that could explain these differences, and build an evidence and gap map to identify important remaining research questions.

METHODS

A literature search will be conducted from January 2010 onwards in MEDLINE, Embase, the Cochrane Library, and Web of Science databases. Two reviewers will independently screen all citations, full text, and abstract data. The study methodological quality and risk of bias will be appraised using appropriate tools if applicable. A flow diagram with the screened studies will be presented, and all included studies will be displayed using interactive evidence and gap maps. Results will be reported in accordance with recommendations from the Campbell Collaboration on the development of evidence and gap maps.

DISCUSSION

Evidence behind computerized clinical decision support systems to support prescription use in primary care has so far been difficult to be synthesized. Evidence and gap maps represent an innovative approach that has emerged and is increasingly being used to address a broader research question, where multiple types of intervention and outcomes reported may be evaluated. Broad inclusion criteria have been chosen with regard to study designs, in order to collect all available information. Regarding the limitations, we will only include English and Spanish language studies from the last 10 years, we will not perform a grey literature search, and we will not carry out a meta-analysis due to the predictable heterogeneity of available studies.

SYSTEMATIC REVIEW REGISTRATION

This study is registered in Open Science Framework https://bit.ly/2RqKrWp.

Assessing Knowledge and Barriers at the Primary Care Provider Level that Contribute to Disparities in Inflammatory Breast Cancer Diagnosis and Treatment

Purpose

The purpose of this study was to evaluate knowledge gaps and barriers related to diagnosis and care of inflammatory breast cancer (IBC), a rare but most lethal breast cancer subtype, amongst Primary Care Providers (PCP) as they are often the first point of contact when patients notice initial symptoms.

Methods

PCP participants within Duke University Health System, federally qualified health center, corporate employee health and community practices, nearby academic medical center, Duke physician assistant, and nurse leadership program alumni were first selected in a convenience sample (n=11) for semi-structured interviews (n=11). Based on these data, an online survey tool was developed and disseminated (n=78) to assess salient measures of IBC diagnosis, health disparity factors, referral and care coordination practices, COVID impact, and continued medical education (CME).

Results

PCP reported access to care and knowledge gaps in symptom recognition (mean = 3.3, range 1-7) as major barriers. Only 31% reported ever suspecting IBC in a patient. PCP (n=49) responded being challenged with referral delays in diagnostic imaging. Additionally, since the COVID-19 pandemic started, 63% reported breast cancer referral delays, and 33% reported diagnosing less breast cancer. PCP stated interest in CME in their practice for improved diagnosis and patient care, which included online (53%), lunch time or other in-service training (33%), patient and provider-facing websites (32%).

Conclusions

Challenges communicating rare cancer information, gaps in confidence in diagnosing IBC, and timely follow-up with patients and specialists underscores the need for developing PCP educational modules to improve guideline-concordant care.

Need for numbers: assessing cancer survivors' needs for personalized and generic statistical information

Background

Statistical information (e.g., on long-term survival or side effects) may be valuable for healthcare providers to share with their patients to facilitate shared decision making on treatment options. In this pre-registered study, we assessed cancer survivors’ need for generic (population-based) versus personalized (tailored towards patient/tumor characteristics) statistical information after their diagnosis. We examined how information coping style, subjective numeracy, and anxiety levels of survivors relate to these needs and identified statistical need profiles. Additionally, we qualitatively explored survivors’ considerations for (not) wanting statistical information.

Methods

Cancer survivors’ need for statistics regarding incidence, survival, recurrence, side effects and quality of life were assessed with an online questionnaire. For each of these topics, survivors were asked to think back to their first cancer diagnosis and to indicate their need for generic and personalized statistics on a 4-point scale (‘not at all’- ‘very much’). Associations between information coping style, subjective numeracy, and anxiety with need for generic and personalized statistics were examined with Pearson’s correlations. Statistical need profiles were identified using latent class analysis. Considerations for (not) wanting statistics were analyzed qualitatively.

Results

Overall, cancer survivors (n = 174) had a higher need for personalized than for generic statistics (p < .001, d = 0.74). Need for personalized statistics was associated with higher subjective numeracy (r = .29) and an information-seeking coping style (r = .41). Three statistical need profiles were identified (1) a strong need for both generic and personalized statistics (34%), (2) a stronger need for personalized than for generic statistics (55%), and (3) a little need for both generic and personalized statistics (11%). Considerations for wanting personalized cancer statistics ranged from feelings of being in control to making better informed decisions about treatment. Considerations for not wanting statistics related to negative experience with statistics and to the unpredictability of future events for individual patients.

Conclusions

In light of the increased possibilities for using personalized statistics in clinical practice and decision aids, it appears that most cancer survivors want personalized statistical information during treatment decision-making. Subjective numeracy and information coping style seem important factors influencing this need. We encourage further development and implementation of data-driven personalized decision support technologies in oncological care to support patients in treatment decision making.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12911-022-02005-2.

Effects of a Guideline-Informed Clinical Decision Support System Intervention to Improve Colony-Stimulating Factor Prescribing: A Cluster Randomized Clinical Trial

IMPORTANCE

Colony-stimulating factors are prescribed to patients undergoing chemotherapy to reduce the risk of febrile neutropenia. Research suggests that 55% to 95% of colony-stimulating factor prescribing is inconsistent with national guidelines.

OBJECTIVE

To examine whether a guideline-based standing order for primary prophylactic colony-stimulating factors improves use and reduces the incidence of febrile neutropenia.

DESIGN, SETTING, AND PARTICIPANTS

This cluster randomized clinical trial, the Trial Assessing CSF Prescribing Effectiveness and Risk (TrACER), involved 32 community oncology clinics in the US. Participants were adult patients with breast, colorectal, or non-small cell lung cancer initiating cancer therapy and enrolled between January 2016 and April 2020. Data analysis was performed from July to October 2021.

INTERVENTIONS

Sites were randomized 3:1 to implementation of a guideline-based primary prophylactic colony-stimulating factor standing order system or usual care. Automated orders were added for high-risk regimens, and an alert not to prescribe was included for low-risk regimens. Risk was based on National Comprehensive Cancer Network guidelines.

MAIN OUTCOMES AND MEASURES

The primary outcome was to find an increase in colony-stimulating factor use among high-risk patients from 40% to 75%, a reduction in use among low-risk patients from 17% to 7%, and a 50% reduction in febrile neutropenia rates in the intervention group. Mixed model logistic regression adjusted for correlation of outcomes within a clinic.

RESULTS

A total of 2946 patients (median [IQR] age, 59.0 [50.0-67.0] years; 2233 women [77.0%]; 2292 White [79.1%]) were enrolled; 2287 were randomized to the intervention, and 659 were randomized to usual care. Colony-stimulating factor use for patients receiving high-risk regimens was high and not significantly different between groups (847 of 950 patients [89.2%] in the intervention group vs 296 of 309 patients [95.8%] in the usual care group). Among high-risk patients, febrile neutropenia rates for the intervention (58 of 947 patients [6.1%]) and usual care (13 of 308 patients [4.2%]) groups were not significantly different. The febrile neutropenia rate for patients receiving high-risk regimens not receiving colony-stimulating factors was 14.9% (17 of 114 patients). Among the 585 patients receiving low-risk regimens, colony-stimulating factor use was low and did not differ between groups (29 of 457 patients [6.3%] in the intervention group vs 7 of 128 patients [5.5%] in the usual care group). Febrile neutropenia rates did not differ between usual care (1 of 127 patients [0.8%]) and the intervention (7 of 452 patients [1.5%]) groups.

CONCLUSIONS AND RELEVANCE

In this cluster randomized clinical trial, implementation of a guideline-informed standing order did not affect colony-stimulating factor use or febrile neutropenia rates in high-risk and low-risk patients. Overall, use was generally appropriate for the level of risk. Standing order interventions do not appear to be necessary or effective in the setting of prophylactic colony-stimulating factor prescribing.

TRIAL REGISTRATION

ClinicalTrials.gov Identifier: NCT02728596.

How are ontologies implemented to represent clinical practice guidelines in clinical decision support systems: protocol for a systematic review

BACKGROUND

Clinical practice guidelines are statements which are based on the best available evidence, and their goal is to improve the quality of patient care. Integrating clinical practice guidelines into computer systems can help physicians reduce medical errors and help them to have the best possible practice. Guideline-based clinical decision support systems play a significant role in supporting physicians in their decisions. Meantime, system errors are the most critical concerns in designing decision support systems that can affect their performance and efficacy. A well-developed ontology can be helpful in this matter. The proposed systematic review will specify the methods, components, language of rules, and evaluation methods of current ontology-driven guideline-based clinical decision support systems.

METHODS

This review will identify literature through searching MEDLINE (via Ovid), PubMed, EMBASE, Cochrane Library, CINAHL, ScienceDirect, IEEEXplore, and ACM Digital Library. Gray literature, reference lists, and citing articles of the included studies will be searched. The quality of the included studies will be assessed by the mixed methods appraisal tool (MMAT-version 2018). At least two independent reviewers will perform the screening, quality assessment, and data extraction. A third reviewer will resolve any disagreements. Proper data analysis will be performed based on the type of system and ontology engineering evaluation data.

DISCUSSION

The study will provide evidence regarding applying ontologies in guideline-based clinical decision support systems. The findings of this systematic review will be a guide for decision support system designers and developers, technologists, system providers, policymakers, and stakeholders. Ontology builders can use the information in this review to build well-structured ontologies for personalized medicine.

SYSTEMATIC REVIEW REGISTRATION

PROSPERO CRD42018106501.

CancerCellTracker: a brightfield time-lapse microscopy framework for cancer drug sensitivity estimation

MOTIVATION

Time-lapse microscopy is a powerful technique that relies on images of live cells cultured ex vivo that are captured at regular intervals of time to describe and quantify their behavior under certain experimental conditions. This imaging method has great potential in advancing the field of precision oncology by quantifying the response of cancer cells to various therapies and identifying the most efficacious treatment for a given patient. Digital image processing algorithms developed so far require high-resolution images involving very few cells originating from homogeneous cell line populations. We propose a novel framework that tracks cancer cells to capture their behavior and quantify cell viability to inform clinical decisions in a high-throughput manner.

RESULTS

The brightfield microscopy images a large number of patient-derived cells in an ex vivo reconstruction of the tumor microenvironment treated with 31 drugs for up to 6 days. We developed a robust and user-friendly pipeline CancerCellTracker that detects cells in co-culture, tracks these cells across time and identifies cell death events using changes in cell attributes. We validated our computational pipeline by comparing the timing of cell death estimates by CancerCellTracker from brightfield images and a fluorescent channel featuring ethidium homodimer. We benchmarked our results using a state-of-the-art algorithm implemented in ImageJ and previously published in the literature. We highlighted CancerCellTracker's efficiency in estimating the percentage of live cells in the presence of bone marrow stromal cells.

AVAILABILITY AND IMPLEMENTATION

https://github.com/compbiolabucf/CancerCellTracker.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

Interventions to reduce the incidence of medical error and its financial burden in health care systems: A systematic review of systematic reviews

Background and aim

Improving health care quality and ensuring patient safety is impossible without addressing medical errors that adversely affect patient outcomes. Therefore, it is essential to correctly estimate the incidence rates and implement the most appropriate solutions to control and reduce medical errors. We identified such interventions.

Methods

We conducted a systematic review of systematic reviews by searching four databases (PubMed, Scopus, Ovid Medline, and Embase) until January 2021 to elicit interventions that have the potential to decrease medical errors. Two reviewers independently conducted data extraction and analyses.

Results

Seventysix systematic review papers were included in the study. We identified eight types of interventions based on medical error type classification: overall medical error, medication error, diagnostic error, patients fall, healthcare-associated infections, transfusion and testing errors, surgical error, and patient suicide. Most studies focused on medication error (66%) and were conducted in hospital settings (74%).

Conclusions

Despite a plethora of suggested interventions, patient safety has not significantly improved. Therefore, policymakers need to focus more on the implementation considerations of selected interventions.

Usability, acceptability, and implementation strategies for the Exercise in Cancer Evaluation and Decision Support (EXCEEDS) algorithm: a Delphi study

INTRODUCTION

Oncology guidelines recommend participation in cancer rehabilitation or exercise services (CR/ES) to optimize survivorship. Yet, connecting the right survivor, with the right CR/ES, at the right time remains a challenge. The Exercise in Cancer Evaluation and Decision Support (EXCEEDS) algorithm was developed to enhance CR/ES clinical decision-making and facilitate access to CR/ES. We used Delphi methodology to evaluate usability, acceptability, and determine pragmatic implementation priorities.

METHODS

Participants completed three online questionnaires including (1) simulated case vignettes, (2) 4-item acceptability questionnaire (0-5 pts), and (3) series of items to rank algorithm implementation priorities (potential users, platforms, strategies). To evaluate usability, we used Chi-squared test to compare frequency of accurate pre-exercise medical clearance and CR/ES triage recommendations for case vignettes when using EXCEEDS vs. without. We calculated mean acceptability and inter-rater agreement overall and in 4 domains. We used the Eisenhower Prioritization Method to evaluate implementation priorities.

RESULTS

Participants (N = 133) mostly represented the fields of rehabilitation (69%), oncology (25%), or exercise science (17%). When using EXCEEDS (vs. without), their recommendations were more likely to be guideline concordant for medical clearance (83.4% vs. 66.5%, X² = 26.61, p < .0001) and CR/ES triage (60.9% vs. 51.1%, X² = 73.79, p < .0001). Mean acceptability was M = 3.90 ± 0.47; inter-rater agreement was high for 3 of 4 domains. Implementation priorities include 1 potential user group, 2 platform types, and 9 implementation strategies.

CONCLUSION

This study demonstrates the EXCEEDS algorithm can be a pragmatic and acceptable clinical decision support tool for CR/ES recommendations. Future research is needed to evaluate algorithm usability and acceptability in real-world clinical pathways.

Patient reported outcomes in oncology: changing perspectives-a systematic review

In public health context, oncology is associated with severe negative impact on patients and on their relatives’ quality of life. Over the last decades, survival has remained at 50% worldwide for some tumor locations. Patient reported outcomes (PROs) assessment and, the corresponding use in clinical practice, help establishing patient individualized profiling involving caregivers. The purpose of this systematic review was to examine critical success factors for PROs assessment in daily clinical oncology practice. Additionally, we investigated how PROs collection can change oncology perspectives for patients and caregivers. According to PRISMA guidelines, 83 studies were included in this systematic review, whether related with implementation in daily clinical practice or associated with its use in oncology. PROs assessment gathers multi-professional teams, biomedical and clinical expertise, patients, families and caregivers. Institutional involvement, first line for caregiver’s adherence, team continuous formation, encompassing training and support, design of clear workflows, continuous monitoring, and data analysis are crucial for implementation. PROs measures are decisive in oncology. Several items were improved, including caregiver–patient–physician communication, patient risk groups identification, unmet problems and needs detection, disease course and treatment tracking, prognostic markers, cost-effectiveness measurement and comfort/support provision for both patients and caregivers. Routine assessment and implementation of PROs in clinical practice are a major challenge and a paradigm transformation for future.

12th Korea Healthcare Congress 2021; 김치국부터 마시지 말라; The Time for Digital Health is Almost Here

We are now on the cusp of massive adoption of digital health technologies. Medicine is becoming an information science intertwined with technology and data science. This talk aims to describe the current state of digital transformation in healthcare, to identify reasons for enthusiasm and caution, and to provide a framework for thinking about what is necessary for hospitals and health systems to be confident about incorporating these innovations into practice. I have three key recommendations. First, we should buy results, not claims. Those in positions that influence decisions about endorsing or purchasing digital products designed to improve care or outcomes ought to buy results, not claims or intermediate results. Moreover, although analytic validity and clinical validity are important, they sometimes do not reflect the impact of a product in its entirety. Ultimately, we need to know whether patients benefit. Second, we should insist on transparency. The performance of a product cannot be a secret. The basis on which developers make claims about their products should be open to all, including patients. Better yet, data on which experts reach a conclusion should be shared, just as many companies share research data on drugs and devices. Third, we should be aware of unintended adverse consequences. We should evaluate every intervention for unintended adverse consequences. Changes to systems, with all good intentions, can always go awry. In conclusion, insistence on good and evolving evidence is the best way to arrive at our destination: the use of innovations to improve outcomes.

Can artificial intelligence improve cancer treatments?

Artificial intelligence (AI) powered by the accumulating clinical and molecular data about cancer has fueled the expectation that a transformation in cancer treatments towards significant improvement of patient outcomes is at hand. However, such transformation has been so far elusive. The opacity of AI algorithms and the lack of quality annotated data being available at population scale are among the challenges to the application of AI in oncology. Fundamentally however, the heterogeneity of cancer and its evolutionary dynamics make every tumor response to therapy sufficiently different from the population, machine-learned statistical models, challenging hence the capacity of these models to yield reliable inferences about treatment recommendations that can improve patient outcomes. This article reviews the nominal elements of clinical decision-making for precision oncology and frames the utility of AI to cancer treatment improvements in light of cancer unique challenges.

Advancing pharmacy and healthcare with virtual digital technologies

Digitalisation of the healthcare sector promises to revolutionise patient healthcare globally. From the different technologies, virtual tools including artificial intelligence, blockchain, virtual, and augmented reality, to name but a few, are providing significant benefits to patients and the pharmaceutical sector alike, ranging from improving access to clinicians and medicines, as well as improving real-time diagnoses and treatments. Indeed, it is envisioned that such technologies will communicate together in real-time, as well as with their physical counterparts, to create a large-scale, cyber healthcare system. Despite the significant benefits that virtual-based digital health technologies can bring to patient care, a number of challenges still remain, ranging from data security to acceptance within the healthcare sector. This review provides a timely account of the benefits and challenges of virtual health interventions, as well an outlook on how such technologies can be transitioned from research-focused towards real-world healthcare and pharmaceutical applications to transform treatment pathways for patients worldwide.

The effect of decision support systems on pain in patients with cancer: A systematic review and meta-analysis of randomized controlled trials

PURPOSE

This study was conducted to systematically examine the effect of decision support systems (DSSs) applied to patients with cancer on pain severity.

REVIEW METHODS

Systematic review and meta-analysis. A search was done on Web of Science, Science Direct, PubMed, ProQuest, EBSCOhost/CINAHL Complete, Scopus, Springer Link, Cochrane Library, and Ovid databases, which covered a period until September 2021. Meta-analysis of the data was conducted on the CMA 3 software package. Comprehensive reviews were conducted by two independent researchers in line with the PICOS criteria. The study was conducted according to the PRISMA checklist.

FINDINGS

Five randomized controlled trials with 1.880 participants were included in this systematic review and meta-analysis. In the study, visits, consultations, simulation of patient outcomes, telephone support, and email applications were employed for periods ranging from 6 weeks to 6 months. The evaluation of the meta-analysis results indicated that DSSs had positive effects on pain severity in patients with cancer (Hedge's g = 0.22; p < 0.001).

CONCLUSION

The findings of this systematic review and meta-analysis show that DSSs can be used as an effective and comfortable technological application in reducing the severity of pain in patients with cancer.

CLINICAL RELEVANCE

The use of DSSs for pain severity in patients with cancer is an effective method. In line with the findings of this systematic review and meta-analysis, awareness and knowledge levels of all health disciplines about DSSs will increase. It is believed that the use of DSSs to improve patient-centered care will be guiding.

Implementing clinical decision support for reducing women Veterans' cardiovascular risk in VA: A mixed-method, longitudinal study of context, adaptation, and uptake

Evaluations of clinical decision support (CDS) implementation often struggle to measure and explain heterogeneity in uptake over time and across settings, and to account for the impact of context and adaptation on implementation success. In 2017-2020, the EMPOWER QUERI implemented a cardiovascular toolkit using a computerized template aimed at reducing women Veterans' cardiovascular risk across five Veterans Healthcare Administration (VA) sites, using an enhanced Replicating Effective Programs (REP) implementation approach. In this study, we used longitudinal joint displays of qualitative and quantitative findings to explore (1) how contextual factors emerged across sites, (2) how the template and implementation strategies were adapted in response to contextual factors, and (3) how contextual factors and adaptations coincided with template uptake across sites and over time. We identified site structure, staffing changes, relational authority of champions, and external leadership as important contextual factors. These factors gave rise to adaptations such as splitting the template into multiple parts, pairing the template with a computerized reminder, conducting academic detailing, creating cheat sheets, and using small-scale pilot testing. All five sites exhibited variability in utilization over the months of implementation, though later sites exhibited higher template utilization immediately post-launch, possibly reflecting a "preloading" of adaptations from previous sites. These findings underscore the importance of adaptive approaches to implementation, with intentional shifts in intervention and strategy to meet the needs of individual sites, as well as the value of integrating mixed-method data sources in conducting longitudinal evaluation of implementation efforts.

Digital health in oncology in Africa: A scoping review and cross-sectional survey

BACKGROUND

Low- and middle-income countries, especially in Africa, face a growing cancer burden. Adoption of digital health solutions has the potential to improve cancer care delivery and research in these countries. However, the extent of implementation and the impact of digital health interventions across the cancer continuum in Africa have not been studied.

AIMS

To describe the current landscape of digital health interventions in oncology in Africa.

METHODS

We conducted a scoping literature review and supplemented this with a survey. Following the PRISMA for Scoping Reviews guidelines, we searched literature in PubMed and Embase for keywords and synonyms for cancer, digital health, and African countries, and abstracted data using a structured form. For the survey, participants were delegates of the 2019 conference of the African Organization for Research and Training in Cancer.

RESULTS

The literature review identified 57 articles describing 40 digital health interventions or solutions from 17 African countries, while the survey included 111 respondents from 18 African countries, and these reported 25 different digital health systems. Six articles (10.5%) reported randomized controlled trials. The other 51 articles (89.5%) were descriptive or quasi-experimental studies. The interventions mostly targeted cancer prevention (28 articles, 49.1%) or diagnosis and treatment (23 articles, 40.4%). Four articles (7.0%) targeted survivorship and end of life, and the rest were cross-cutting. Cervical cancer was the most targeted cancer (25 articles, 43.9%). Regarding WHO classification of digital interventions, most were for providers (35 articles, 61.4%) or clients (13, 22.8%), while the others were for data services or cut across these categories. The interventions were mostly isolated pilots using basic technologies such as SMS and telephone calls for notifying patients of their appointments or results, or for cancer awareness; image capture apps for cervical cancer screening, and tele-conferencing for tele-pathology and mentorship. Generally positive results were reported, but evaluation focused on structure and process measures such as ease of use, infrastructure requirements, and acceptability of intervention; or general benefits e.g. supporting training and mentorship of providers, communication among providers and clients, and improving data collection and management. No studies evaluated individualized clinical outcomes, and there were no interventions in literature for health system managers although the systems identified in the survey had such functionality, e.g. inventory management. The survey also indicated that none of the digital health systems had all the functionalities for a comprehensive EHR, and major barriers for digital health were initial and ongoing costs, resistance from clinical staff, and lack of fit between the EHR and the clinical workflows.

CONCLUSION

Digital health interventions in oncology in Africa are at early maturity stages but promising. Barriers such as funding, fit between digital health tools and clinical workflows, and inertia towards technology, shall need to be addressed to allow for advancement of digital health solutions to support all parts of the cancer continuum. Future research should investigate the impact of digital health solutions on long-term cancer outcomes such as cancer mortality, morbidity and quality of life.

Clinical Decision Support Systems

BACKGROUND

By combining up-to-date medical knowledge and steadily increasing patient data, a new level of medical care can emerge.

SUMMARY AND KEY MESSAGES

Clinical decision support systems (CDSSs) are an arising solution to handling rich data and providing them to health care providers in order to improve diagnosis and treatment. However, despite promising examples in many areas, substantial evidence for a thorough benefit of these support solutions is lacking. This may be due to a lack of general frameworks and diverse health systems around the globe. We therefore summarize the current status of CDSSs in medicine but also discuss potential limitations that need to be overcome in order to further foster future development and acceptance.

Computer-Based Decision Tools for Shared Therapeutic Decision-making in Oncology: Systematic Review

BACKGROUND

Therapeutic decision-making in oncology is a complex process because physicians must consider many forms of medical data and protocols. Another challenge for physicians is to clearly communicate their decision-making process to patients to ensure informed consent. Computer-based decision tools have the potential to play a valuable role in supporting this process.

OBJECTIVE

This systematic review aims to investigate the extent to which computer-based decision tools have been successfully adopted in oncology consultations to improve patient-physician joint therapeutic decision-making.

METHODS

This review was conducted in accordance with the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) 2020 checklist and guidelines. A literature search was conducted on February 4, 2021, across the Cochrane Database of Systematic Reviews (from 2005 to January 28, 2021), the Cochrane Central Register of Controlled Trials (December 2020), MEDLINE (from 1946 to February 4, 2021), Embase (from 1947 to February 4, 2021), Web of Science (from 1900 to 2021), Scopus (from 1969 to 2021), and PubMed (from 1991 to 2021). We used a snowball approach to identify additional studies by searching the reference lists of the studies included for full-text review. Additional supplementary searches of relevant journals and gray literature websites were conducted. The reviewers screened the articles eligible for review for quality and inclusion before data extraction.

RESULTS

There are relatively few studies looking at the use of computer-based decision tools in oncology consultations. Of the 4431 unique articles obtained from the searches, only 10 (0.22%) satisfied the selection criteria. From the 10 selected studies, 8 computer-based decision tools were identified. Of the 10 studies, 6 (60%) were conducted in the United States. Communication and information-sharing were improved between physicians and patients. However, physicians did not change their habits to take advantage of computer-assisted decision-making tools or the information they provide. On average, the use of these computer-based decision tools added approximately 5 minutes to the total length of consultations. In addition, some physicians felt that the technology increased patients' anxiety.

CONCLUSIONS

Of the 10 selected studies, 6 (60%) demonstrated positive outcomes, 1 (10%) showed negative results, and 3 (30%) were neutral. Adoption of computer-based decision tools during oncology consultations continues to be low. This review shows that information-sharing and communication between physicians and patients can be improved with the assistance of technology. However, the lack of integration with electronic health records is a barrier. This review provides key requirements for enhancing the chance of success of future computer-based decision tools. However, it does not show the effects of health care policies, regulations, or business administration on physicians' propensity to adopt the technology. Nevertheless, it is important that future research address the influence of these higher-level factors as well.

TRIAL REGISTRATION

PROSPERO International Prospective Register of Systematic Reviews CRD42021226087; https://www.crd.york.ac.uk/prospero/display_record.php?ID=CRD42021226087.

Surgery for brain metastases - real-world prognostic factors' association with survival

BACKGROUND

Surgical resection of brain metastases (BM) improves overall survival (OS) in selected patients. Selecting those patients likely to benefit from surgery is challenging. The Graded Prognostic Assessment (GPA) and the diagnosis-specific Graded Prognostic Assessment (ds-GPA) were developed to predict survival in patients with BM, but not specifically to guide patient selection for surgery. Our aim was to evaluate the feasibility of preoperative GPA/ds-GPA scores and assess variables associated with OS.

METHODS

We retrospectively reviewed first-time surgical resection of BM from solid tumors at a Norwegian regional referral center from 2011 to 2018.

RESULTS

Of 590 patients, 51% were female and median age was 63 years. Median OS was 10.3 months and 74 patients (13%) died within three months after surgery. Preoperatively tumor origin was unknown in 20% of patients. A GPA score could be calculated for 92% of the patients preoperatively, but could not correctly predict survival. A ds-GPA score could be calculated for 46% of patients. Multivariable regression analysis revealed shorter OS in patients with higher age, worse functioning status, colorectal primary cancer compared to lung cancer, presence of extracranial metastases, and more than four BM. Patients with preoperative progressive extracranial disease or synchronous BM had shorter OS compared to patients with stable extracranial disease.

CONCLUSION

Ds-GPA could be calculated in less than half of patients preoperatively and GPA poorly identified patients which had minimal benefit of surgery. Including status of extracranial disease improve prognostication and therefore selection to surgery for brain metastases.

Creation of clinical algorithms for decision-making in oncology: an example with dose prescription in radiation oncology

In oncology, decision-making in individual situations is often very complex. To deal with such complexity, people tend to reduce it by relying on their initial intuition. The downside of this intuitive, subjective way of decision-making is that it is prone to cognitive and emotional biases such as overestimating the quality of its judgements or being influenced by one’s current mood. Hence, clinical predictions based on intuition often turn out to be wrong and to be outperformed by statistical predictions. Structuring and objectivizing oncological decision-making may thus overcome some of these issues and have advantages such as avoidance of unwarranted clinical practice variance or error-prevention. Even for uncertain situations with limited medical evidence available or controversies about the best treatment option, structured decision-making approaches like clinical algorithms could outperform intuitive decision-making. However, the idea of such algorithms is not to prescribe the clinician which decision to make nor to abolish medical judgement, but to support physicians in making decisions in a systematic and structured manner. An example for a use-case scenario where such an approach may be feasible is the selection of treatment dose in radiation oncology. In this paper, we will describe how a clinical algorithm for selection of a fractionation scheme for palliative irradiation of bone metastases can be created. We explain which steps in the creation process of a clinical algorithm for supporting decision-making need to be  performed and which challenges and limitations have to be considered.

Redesign of computerized decision support system to improve Non Vitamin K oral anticoagulant prescribing-A pre and post qualitative and quantitative study

BACKGROUND

Inappropriate prescribing of non-vitamin K agents (NOAC) contributes to significant economic and personal burden to our society. Studies have shown that when well designed and targeted, computerized alerts can be effective in improving prescribing without contributing to alert fatigue.

METHOD

A collaborative multidisciplinary review group was set up to review and endorse an upgrade and modification to the hospital electronic medication management system (EMS). The intervention focused on implementing tailored electronic patient specific physiological alerts (such as age, renal function weight and drug interactions) built in EMS to improve the appropriateness of NOAC prescribing at this multisite teaching Australian hospital. To assess the qualitative and quantitative impact of the intervention, a pre and post retrospective study of NOAC prescribing of 100 patients' pre and post the implementation stage was conducted in a multisite Australian 650 bed hospital. Appropriateness of NOAC prescribing was assessed by an experienced pharmacist using approved prescribing product information recommendations. Prescriber satisfaction and experience survey was assessed in both stages of the study using a standard satisfaction survey. Associated hospital acquired complications (HAC) with potential inappropriate NOAC prescribing were evaluated as well as related admission cost and average length of stay.

RESULTS

Redesign of computerised decision support in EMS improved appropriateness of NOAC prescribing from 48 % to 91 %, P < 0.05. A total of 67 prescribers accepted the invitation to participate in the qualitative satisfaction study. Half the respondents (n = 33, 50 %) answered positively to a question assessing the usefulness of implementing NOAC alerts in the EMS in improving their practice and patient safety. This rate has increased to 72 % (n = 48) in the post intervention phase. P < 0.05. Additionally, the total number of reported HAC that are likely to be associated with inappropriate NOAC prescribing was reduced by 36 % in the post intervention phase (from 29 to 22 (RR = 0.7454 95 %CI (0.4283-1.2972), P = 0.2986). The cost of associated HAC has also reduced by 29 % (from $1,282,748 to $911,117) as well as the mean length stay by 11 % (from 18 days to 16 days) post the intervention phase.

CONCLUSION

This study highlights that well-designed electronic prescribing alerts that provide context-relevant information to prescribers are likely to result in benefits to clinicians and patients as well reduction in economic burden. Moreover, they could also contribute to reducing hospital acquired complications and lessen the economic burden on our society.

Clinical Decision Support Systems in Otolaryngology-Head and Neck Surgery: A State of the Art Review

OBJECTIVE

To offer practical, evidence-informed knowledge on clinical decision support systems (CDSSs) and their utility in improving care and reducing costs in otolaryngology-head and neck surgery. This primer on CDSSs introduces clinicians to both the capabilities and the limitations of this technology, reviews the literature on current state, and seeks to spur further progress in this area.

DATA SOURCES

PubMed/MEDLINE, Embase, and Web of Science.

REVIEW METHODS

Scoping review of CDSS literature applicable to otolaryngology clinical practice. Investigators identified articles that incorporated knowledge-based computerized CDSSs to aid clinicians in decision making and workflow. Data extraction included level of evidence, Osheroff classification of CDSS intervention type, otolaryngology subspecialty or domain, and impact on provider performance or patient outcomes.

CONCLUSIONS

Of 3191 studies retrieved, 11 articles met formal inclusion criteria. CDSS interventions included guideline or protocols support (n = 8), forms and templates (n = 5), data presentation aids (n = 2), and reactive alerts, reference information, or order sets (all n = 1); 4 studies had multiple interventions. CDSS studies demonstrated effectiveness across diverse domains, including antibiotic stewardship, cancer survivorship, guideline adherence, data capture, cost reduction, and workflow. Implementing CDSSs often involved collaboration with health information technologists.

IMPLICATIONS FOR PRACTICE

While the published literature on CDSSs in otolaryngology is finite, CDSS interventions are proliferating in clinical practice, with roles in preventing medical errors, streamlining workflows, and improving adherence to best practices for head and neck disorders. Clinicians may collaborate with information technologists and health systems scientists to develop, implement, and investigate the impact of CDSSs in otolaryngology.

Barriers and Facilitators for Implementation of a Computerized Clinical Decision Support System in Lung Cancer Multidisciplinary Team Meetings-A Qualitative Assessment

Simple Summary

Oncological computerized clinical decision support systems (CCDSSs) are currently being developed to facilitate workflows of multidisciplinary team meetings (MDTMs). To successfully implement these systems in MDTMs, the aim of this qualitative assessment was to identify barriers and facilitators for implementation and to provide actionable findings for an implementation strategy. The main facilitators for implementation of the CCDSS were considered to be easy access to well-structured data, and reducing time spent by clinicians on MDTM preparation and duration of the MDTMs. Main barriers for adoption were seen in incomplete or non-trustworthy output generated by the system and insufficient adaptability of the system to local and contextual needs. Actionable findings for an implementation strategy were a usability test and validation study involving key users in the organization’s real-life setting. Given the growing interest in CCDSSs in oncology care, insight in barriers and facilitators for successful implementation seems highly relevant.

Abstract

Background: Oncological computerized clinical decision support systems (CCDSSs) to facilitate workflows of multidisciplinary team meetings (MDTMs) are currently being developed. To successfully implement these CCDSSs in MDTMs, this study aims to: (a) identify barriers and facilitators for implementation for the use case of lung cancer; and (b) provide actionable findings for an implementation strategy. Methods: The Consolidated Framework for Implementation Science was used to create an interview protocol and to analyze the results. Semi-structured interviews were conducted among various health care professionals involved in MDTMs. The transcripts were analyzed using a thematic analysis following a deductive approach. Results: Twenty-six professionals participated in the interviews. The main facilitators for implementation of the CCDSS were considered to be easy access to well-structured patient data, and the resulting reduction of MDTM preparation time and of duration of MDTMs. Main barriers for adoption were seen in incomplete or non-trustworthy output generated by the system and insufficient adaptability of the system to local and contextual needs. Conclusion: Using a CCDSS in lung cancer MDTMs was expected to increase efficiency of workflows. Successful implementation was seen as dependent on the reliability and adaptability of the CCDSS and involvement of key users in the implementation process.

A Methodology Based on Expert Systems for the Early Detection and Prevention of Hypoxemic Clinical Cases

Respiratory diseases are currently considered to be amongst the most frequent causes of death and disability worldwide, and even more so during the year 2020 because of the COVID-19 global pandemic. Aiming to reduce the impact of these diseases, in this work a methodology is developed that allows the early detection and prevention of potential hypoxemic clinical cases in patients vulnerable to respiratory diseases. Starting from the methodology proposed by the authors in a previous work and grounded in the definition of a set of expert systems, the methodology can generate alerts about the patient's hypoxemic status by means of the interpretation and combination of data coming both from physical measurements and from the considerations of health professionals. A concurrent set of Mamdani-type fuzzy-logic inference systems allows the collecting and processing of information, thus determining a final alert associated with the measurement of the global hypoxemic risk. This new methodology has been tested experimentally, producing positive results so far from the viewpoint of time reduction in the detection of a blood oxygen saturation deficit condition, thus implicitly improving the consequent treatment options and reducing the potential adverse effects on the patient's health.

Challenges for the evaluation of digital health solutions-A call for innovative evidence generation approaches

The field of digital health, and its meaning, has evolved rapidly over the last 20 years. For this article we followed the most recent definition provided by FDA in 2020. Emerging solutions offers tremendous potential to positively transform the healthcare sector. Despite the growing number of applications, however, the evolution of methodologies to perform timely, cost-effective and robust evaluations have not kept pace. It remains an industry-wide challenge to provide credible evidence, therefore, hindering wider adoption. Conventional methodologies, such as clinical trials, have seldom been applied and more pragmatic approaches are needed. In response, several academic centers such as researchers from the Institute of Global Health Innovation at Imperial College London have initiated a digital health clinical simulation test bed to explore new approaches for evidence gathering relevant to solution type and maturity. The aim of this article is to: (1) Review current research approaches and discuss their limitations; (2) Discuss challenges faced by different stakeholders in undertaking evaluations; and (3) Call for new approaches to facilitate the safe and responsible growth of the digital health sector.

Drug Dosing Recommendations for All Patients: A Roadmap for Change

Most drug labels do not contain dosing recommendations for a significant portion of real‐world patients for whom the drug is prescribed. Current label recommendations predominately reflect the population studied in pivotal trials that typically exclude patients who are very young or old, emaciated or morbidly obese, pregnant, or have multiple characteristics likely to influence dosing. As a result, physicians may need to guess the correct dose and regimen for these patients. It is now feasible to provide dose and regimen recommendations for these patients by integrating available scientific knowledge and by utilizing or modifying current regulatory agency‐industry practices. The purpose of this commentary is to explore several factors that should be considered in creating a process that will provide more effective, safe, and timely drug dosing recommendations for most, if not all, patients. These factors include the availability of real‐world data, development of predictive models, experience with the US Food and Drug Administration (FDA)’s pediatric exclusivity program, development of clinical decision software, funding mechanisms like the Prescription Drug Users Fee Act (PDUFA), and harmonization of global regulatory policies. From an examination of these factors, we recommend a relatively simple, efficient expansion of current practices designed to predict, confirm, and continuously improve drug dosing for more patients. We believe implementing these recommendations will benefit patients, payers, industry, and regulatory agencies.