Development and implementation of oncology care pathways in an integrated care network: the Via Oncology Pathways experience

Assessing Serious Spinal Pathology Using Bayesian Network Decision Support: Development and Validation Study

BACKGROUND

Identifying and managing serious spinal pathology (SSP) such as cauda equina syndrome or spinal infection in patients presenting with low back pain is challenging. Traditional red flag questioning is increasingly criticized, and previous studies show that many clinicians lack confidence in managing patients presenting with red flags. Improving decision-making and reducing the variability of care for these patients is a key priority for clinicians and researchers.

OBJECTIVE

We aimed to improve SSP identification by constructing and validating a decision support tool using a Bayesian network (BN), which is an artificial intelligence technique that combines current evidence and expert knowledge.

METHODS

A modified RAND appropriateness procedure was undertaken with 16 experts over 3 rounds, designed to elicit the variables, structure, and conditional probabilities necessary to build a causal BN. The BN predicts the likelihood of a patient with a particular presentation having an SSP. The second part of this study used an established framework to direct a 4-part validation that included comparison of the BN with consensus statements, practice guidelines, and recent research. Clinical cases were entered into the model and the results were compared with clinical judgment from spinal experts who were not involved in the elicitation. Receiver operating characteristic curves were plotted and area under the curve were calculated for accuracy statistics.

RESULTS

The RAND appropriateness procedure elicited a model including 38 variables in 3 domains: risk factors (10 variables), signs and symptoms (17 variables), and judgment factors (11 variables). Clear consensus was found in the risk factors and signs and symptoms for SSP conditions. The 4-part BN validation demonstrated good performance overall and identified areas for further development. Comparison with available clinical literature showed good overall agreement but suggested certain improvements required to, for example, 2 of the 11 judgment factors. Case analysis showed that cauda equina syndrome, space-occupying lesion/cancer, and inflammatory condition identification performed well across the validation domains. Fracture identification performed less well, but the reasons for the erroneous results are well understood. A review of the content by independent spinal experts backed up the issues with the fracture node, but the BN was otherwise deemed acceptable.

CONCLUSIONS

The RAND appropriateness procedure and validation framework were successfully implemented to develop the BN for SSP. In comparison with other expert-elicited BN studies, this work goes a step further in validating the output before attempting implementation. Using a framework for model validation, the BN showed encouraging validity and has provided avenues for further developing the outputs that demonstrated poor accuracy. This study provides the vital first step of improving our ability to predict outcomes in low back pain by first considering the problem of SSP.

INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID)

RR2-10.2196/21804.

Prospective Comparison of Medical Oncologists and a Machine Learning Model to Predict 3-Month Mortality in Patients With Metastatic Solid Tumors

Importance

To date, oncologist and model prognostic performance have been assessed independently and mostly retrospectively; however, how model prognostic performance compares with oncologist prognostic performance prospectively remains unknown.

Objective

To compare oncologist performance with a model in predicting 3-month mortality for patients with metastatic solid tumors in an outpatient setting.

Design, Setting, and Participants

This prognostic study evaluated prospective predictions for a cohort of patients with metastatic solid tumors seen in outpatient oncology clinics at a National Cancer Institute-designated cancer center and associated satellites between December 6, 2019, and August 6, 2021. Oncologists (57 physicians and 17 advanced practice clinicians) answered a 3-month surprise question (3MSQ) within clinical pathways. A model was trained with electronic health record data from January 1, 2013, to April 24, 2019, to identify patients at high risk of 3-month mortality and deployed silently in October 2019. Analysis was limited to oncologist prognostications with a model prediction within the preceding 30 days.

Exposures

Three-month surprise question and gradient-boosting binary classifier.

Main Outcomes and Measures

The primary outcome was performance comparison between oncologists and the model to predict 3-month mortality. The primary performance metric was the positive predictive value (PPV) at the sensitivity achieved by the medical oncologists with their 3MSQ answers.

Results

A total of 74 oncologists answered 3099 3MSQs for 2041 patients with advanced cancer (median age, 62.6 [range, 18-96] years; 1271 women [62.3%]). In this cohort with a 15% prevalence of 3-month mortality and 30% sensitivity for both oncologists and the model, the PPV of oncologists was 34.8% (95% CI, 30.1%-39.5%) and the PPV of the model was 60.0% (95% CI, 53.6%-66.3%). Area under the receiver operating characteristic curve for the model was 81.2% (95% CI, 79.1%-83.3%). The model significantly outperformed the oncologists in short-term mortality.

Conclusions and Relevance

In this prognostic study, the model outperformed oncologists overall and within the breast and gastrointestinal cancer cohorts in predicting 3-month mortality for patients with advanced cancer. These findings suggest that further studies may be useful to examine how model predictions could improve oncologists' prognostic confidence and patient-centered goal-concordant care at the end of life.

Incorporating Value-Based Care Into Oncology

Value-based care within insurance design utilizes evidence-based medicine as a means of defining high-value versus low-value diagnostics and treatments. The goals of value-based care are to shift spending and coverage toward high-value care and reduce the use of low-value practices. Within oncology, several value-based methods have been proposed and implemented. We review value-based care being used within oncology, including defining the value of oncology drugs through frameworks, clinical care pathways, alternative payment models including the Oncology Care Model, value-based insurance design, and reducing low-value care including the Choosing Wisely initiatives.

Application of Information Technology to Clinical Trial Evaluation and Enrollment: A Review

Importance

As cancer treatment has become more individualized, oncologic clinical trials have become more complex. Increasingly numerous and stringent eligibility criteria frequently include tumor molecular or genomic characteristics that may not be readily identified in medical records, rendering it difficult to best match clinical trials with clinical sites and to identify potentially eligible patients once a clinical trial has been selected and activated. Partly because of these factors, enrollment rates for cancer clinical trials remain low, creating delays and increased costs for drug development. Information technology (IT) platforms have been applied to the implementation and conduct of clinical trials to improve efficiencies in several medical fields, and these platforms have recently been introduced to oncologic studies.

Observations

This review summarizes cancer and noncancer studies that used IT platforms for assistance with clinical trial site selection, patient recruitment, and patient screening. The review does not address the use of IT in other aspects of clinical research, such as wearable physical activity monitors or telehealth visits. A large number of IT platforms (which may be patient facing, site or investigator facing, or sponsor facing) are now commercially available. These applications use artificial intelligence and/or natural language processing to identify and summarize protocol eligibility criteria, institutional patient populations, and individual electronic health records. Although there is an expanding body of literature examining the role of this technology, relatively few studies to date have been performed in oncologic settings.

Conclusions and Relevance

This review found that an increasing number and variety of IT platforms were available to assist in the planning and conduct of clinical trials. Because oncologic clinical care and clinical trial protocols are particularly complex, nuanced, and individualized, published experience with this technology in other fields may not be fully applicable to cancer settings. The extent to which these services will overcome ongoing and increasing challenges in cancer clinical research remains unclear.

Integrating Academic and Community Cancer Care and Research through Multidisciplinary Oncology Pathways for Value-Based Care: A Review and the City of Hope Experience

As the US transitions from volume- to value-based cancer care, many cancer centers and community groups have joined to share resources to deliver measurable, high-quality cancer care and clinical research with the associated high patient satisfaction, provider satisfaction, and practice health at optimal costs that are the hallmarks of value-based care. Multidisciplinary oncology care pathways are essential components of value-based care and their payment metrics. Oncology pathways are evidence-based, standardized but personalizable care plans to guide cancer care. Pathways have been developed and studied for the major medical, surgical, radiation, and supportive oncology disciplines to support decision-making, streamline care, and optimize outcomes. Implementing multidisciplinary oncology pathways can facilitate comprehensive care plans for each cancer patient throughout their cancer journey and across large multisite delivery systems. Outcomes from the delivered pathway-based care can then be evaluated against individual and population benchmarks. The complexity of adoption, implementation, and assessment of multidisciplinary oncology pathways, however, presents many challenges. We review the development and components of value-based cancer care and detail City of Hope’s (COH) academic and community-team-based approaches for implementing multidisciplinary pathways. We also describe supportive components with available results towards enterprise-wide value-based care delivery.

A scoping review of clinical decision support tools that generate new knowledge to support decision making in real time

OBJECTIVE

A growing body of observational data enabled its secondary use to facilitate clinical care for complex cases not covered by the existing evidence. We conducted a scoping review to characterize clinical decision support systems (CDSSs) that generate new knowledge to provide guidance for such cases in real time.

MATERIALS AND METHODS

PubMed, Embase, ProQuest, and IEEE Xplore were searched up to May 2020. The abstracts were screened by 2 reviewers. Full texts of the relevant articles were reviewed by the first author and approved by the second reviewer, accompanied by the screening of articles' references. The details of design, implementation and evaluation of included CDSSs were extracted.

RESULTS

Our search returned 3427 articles, 53 of which describing 25 CDSSs were selected. We identified 8 expert-based and 17 data-driven tools. Sixteen (64%) tools were developed in the United States, with the others mostly in Europe. Most of the tools (n = 16, 64%) were implemented in 1 site, with only 5 being actively used in clinical practice. Patient or quality outcomes were assessed for 3 (18%) CDSSs, 4 (16%) underwent user acceptance or usage testing and 7 (28%) functional testing.

CONCLUSIONS

We found a number of CDSSs that generate new knowledge, although only 1 addressed confounding and bias. Overall, the tools lacked demonstration of their utility. Improvement in clinical and quality outcomes were shown only for a few CDSSs, while the benefits of the others remain unclear. This review suggests a need for a further testing of such CDSSs and, if appropriate, their dissemination.

Optimal Care Pathways for People with Lung Cancer- a Scoping Review of the Literature

INTRODUCTION

Much of the existing work around implementation of cancer optimal care pathways (OCP) has either focused exclusively on the clinical elements of care or has targeted individual stages in the cancer trajectory, rather than using a patient-centred or service delivery lens to inform the integration of care across the continuum. This review aimed to identify and summarise the available literature on lung cancer OCP.

METHODS

A scoping review was conducted, with literature across multiple databases and grey literature searched. Articles were included if the OCP was being used to manage adult patients with lung cancer and reported on either the development process and outcomes and/or barriers and facilitators associated with optimal care pathway development and/or uptake.

RESULTS

Of the 381 references screened, 32 articles were included. The lung cancer pathways reviewed varied significantly. A number of themes were identified including the development and implementation of the OCP; the use of quality indicators to audit the OCP; and studies on outcomes of the OCP incorporating timeliness of care delivery, patient experiences and health care utilisation and costs.

CONCLUSIONS

The limited number of relevant articles found in this review may suggest that an OCP for lung cancer is still in its preliminary stages across the broader health systems.

The Community Oncology and Academic Medical Center Alliance in the Age of Precision Medicine: Cancer Genetics and Genomics Considerations

Recent public policy, governmental regulatory and economic trends have motivated the establishment and deepening of community health and academic medical center alliances. Accordingly, community oncology practices now deliver a significant portion of their oncology care in association with academic cancer centers. In the age of precision medicine, this alliance has acquired critical importance; novel advances in nucleic acid sequencing, the generation and analysis of immense data sets, the changing clinical landscape of hereditary cancer predisposition and ongoing discovery of novel, targeted therapies challenge community-based oncologists to deliver molecularly-informed health care. The active engagement of community oncology practices with academic partners helps with meeting these challenges; community/academic alliances result in improved cancer patient care and provider efficacy. Here, we review the community oncology and academic medical center alliance. We examine how practitioners may leverage academic center precision medicine-based cancer genetics and genomics programs to advance their patients' needs. We highlight a number of project initiatives at the City of Hope Comprehensive Cancer Center that seek to optimize community oncology and academic cancer center precision medicine interactions.

System-Level Factors Associated With Use of Outpatient Specialty Palliative Care Among Patients With Advanced Cancer

PURPOSE

The proportion of patients with advanced cancer who receive outpatient specialty palliative care (OSPC) is as low as 2.0%. Improved understanding of the system-level factors influencing use of OSPC could inform adaptations to the delivery of palliative care to maximize access. We examined associations between OSPC use among patients with advanced solid tumors and oncology-OSPC clinic colocation and patient travel time to an OSPC clinic.

PATIENTS AND METHODS

We conducted a retrospective cohort study of patients with advanced solid tumors receiving oncologic treatment between January 1 and December 31, 2016, within a comprehensive cancer center network with well-established, oncology-specific OSPC clinics. Multivariable logistic regression analysis was used to evaluate the associations of clinic colocation and geographic access with OSPC use.

RESULTS

Of 9,485 patients with advanced solid tumors, 478 (5.0%) received OSPC services in 2016. After controlling for age, sex, marital status, cancer type, insurance, treatment intent, and illness severity, patients whose oncologist practices were colocated with OSPC clinics were more likely to use OSPC (odds ratio [OR], 19.2; 95% CI, 14.1 to 26.2). Compared with patients who lived > 90 minutes from an OSPC clinic, patients with travel times of < 30 minutes (OR, 3.2; 95% CI, 2.2 to 4.6) and 31 to 60 minutes (OR, 2.4; 95% CI, 1.6 to 3.6) were also more likely to use OSPC.

CONCLUSION

Among patients with advanced solid tumors, colocation of oncology and OSPC clinics and shorter patient travel time were associated with greater odds of using OSPC. Future efforts to increase OSPC use in this population should consider clinic colocation and travel burden.

Health challenges in Kazakhstan and Central Asia

The Central Asian region, which encompasses Kazakhstan, Uzbekistan, Tajikistan, Turkmenistan and Kyrgyzstan, is an interesting geographic region with a rich history dating back to the Silk Road, Mongol conquests and expansion of the Russian Empire. However, from a public health viewpoint, the Central Asian region is under-investigated, and many public health challenges exist, as countries of Central Asia inherited the centralised medical systems practiced in the Soviet Union, and are currently undergoing rapid transitions. A large number of low and middle-income countries around the world, including countries of Central Asia, face a double burden of chronic and infectious disease. This essay focuses on the exploration of the most important public health challenges in the Central Asian region, including limited scientific productivity, the double burden of chronic and infectious disease, the need for healthcare reform and the reduction in care variation. Central Asia has a large number of medical schools, medical centres, and emerging research institutes that can be used to foster a change in medical and public health practice in the region.