Electronic Medical Record-Based Nudge Intervention to Increase Comprehensive Molecular Genotyping in Patients With Metastatic Non-Small Cell Lung Cancer: Results From a Prospective Clinical Trial.

PURPOSE: Less than half of the patients with newly diagnosed metastatic non-small cell lung cancer (NSCLC) undergo comprehensive molecular testing. We designed an electronic medical record (EMR)-based "nudge intervention" to prompt plasma-based molecular testing at the time of initial medical oncology consultation. METHODS: A nonrandomized prospective trial was conducted at the University of Pennsylvania's academic practice and two affiliated community practices. Molecular genotyping was performed by tissue- and/or plasma-based next generation sequencing methods. Comprehensive testing was defined as testing for EGFR, ALK, BRAF, ROS1, MET, RET, KRAS, and NTRK. Guideline-concordant treatment was defined as the use of the appropriate first-line (1L) therapy as per the National Comprehensive Cancer Network (NCCN) guidelines. Proportion of patients with comprehensive molecular genotyping results available at any time, molecular results available before 1L therapy, and guideline-concordant 1L treatment were compared between the preintervention and postintervention cohorts using Fisher's exact test or Pearson's chi-squared test. RESULTS: Five hundred and thirty-three patients were included, 376 in the preintervention cohort and 157 in the postintervention cohort. After implementation of the EMR-based nudge, a higher proportion of patients underwent comprehensive molecular testing in the postintervention versus the preintervention cohort (100% v 88%, P = <.001), had results of comprehensive molecular testing available before initiating 1L treatment (97.3% v 91.6%, P = .026), and received NCCN guideline-concordant care (89.8% v 78.2%, P = .035). CONCLUSION: Across three practice sites in a large health system, implementation of a provider team-focused EMR-based nudge intervention was feasible, and led to a higher number of patients with NSCLC undergoing comprehensive molecular genotyping. These findings demonstrate that behavioral nudges can promote molecular testing and should be studied further as a tool to improve guideline-concordant care in both community and academic sites.

Detection of Medication Taking Using a Wrist-Worn Commercially Available Wearable Device.

PURPOSE: Medication nonadherence is a persistent and costly problem across health care. Measures of medication adherence are ineffective. Methods such as self-report, prescription claims data, or smart pill bottles have been used to monitor medication adherence, but these are subject to recall bias, lack real-time feedback, and are often expensive. METHODS: We proposed a method for monitoring medication adherence using a commercially available wearable device. Passively collected motion data were analyzed on the basis of the Movelet algorithm, a dictionary learning framework that builds person-specific chapters of movements from short frames of elemental activities within the movements. We adapted and extended the Movelet method to construct a within-patient prediction model that identifies medication-taking behaviors. RESULTS: Using 15 activity features recorded from wrist-worn wearable devices of 10 patients with breast cancer on endocrine therapy, we demonstrated that medication-taking behavior can be predicted in a controlled clinical environment with a median accuracy of 85%. CONCLUSION: These results in a patient-specific population are exemplar of the potential to measure real-time medication adherence using a wrist-worn commercially available wearable device.

Association Between Availability of Molecular Genotyping Results and Overall Survival in Patients With Advanced Nonsquamous Non-Small-Cell Lung Cancer.

PURPOSE: Current guidelines recommend molecular genotyping for patients newly diagnosed with metastatic nonsquamous (mNSq) non-small-cell lung cancer (NSCLC). The association between availability of molecular genotyping before first line (1L) therapy and overall survival (OS) is not known. METHODS: We conducted a real-world cohort study using electronic health records in patients newly diagnosed with mNSq NSCLC. Cox proportional-hazards multivariable regression models were constructed to examine the association between OS and test result availability before 1L therapy, adjusting for covariates. Additional analyses were conducted to assess the consistency and strength of the relationship. Multivariable logistic regression models were used to examine the association between concurrent tissue and plasma testing (v tissue alone) and result availability. RESULTS: Three hundred twenty-six patients were included, 80% (261/326) with results available before 1L (available testing group), and 20% (65/326) without results available (unavailable testing group). With 14.2-month median follow-up, patients in the available testing group had significantly longer OS relative to the unavailable testing group (adjusted hazard ratio, 0.43; 95% CI, 0.30 to 0.62; P < .0001). The adjusted odds of availability of results before 1L therapy was higher with concurrent tissue and plasma testing (v tissue testing alone; adjusted odds ratio, 2.06; 95% CI, 1.09 to 3.90; P = .026). CONCLUSION: Among patients with mNSq NSCLC in a real-world cohort, availability of molecular genotyping results before 1L therapy was associated with significantly better OS. Concurrent tissue and plasma testing was associated with a higher odds of availability of results before 1L therapy. These findings warrant renewed attention to the completion of molecular genotyping before 1L therapy.

Development of Machine Learning Algorithms Incorporating Electronic Health Record Data, Patient-Reported Outcomes, or Both to Predict Mortality for Outpatients With Cancer.

PURPOSE: Machine learning (ML) algorithms that incorporate routinely collected patient-reported outcomes (PROs) alongside electronic health record (EHR) variables may improve prediction of short-term mortality and facilitate earlier supportive and palliative care for patients with cancer. METHODS: We trained and validated two-phase ML algorithms that incorporated standard PRO assessments alongside approximately 200 routinely collected EHR variables, among patients with medical oncology encounters at a tertiary academic oncology and a community oncology practice. RESULTS: Among 12,350 patients, 5,870 (47.5%) completed PRO assessments. Compared with EHR- and PRO-only algorithms, the EHR + PRO model improved predictive performance in both tertiary oncology (EHR + PRO v EHR v PRO: area under the curve [AUC] 0.86 [0.85-0.87] v 0.82 [0.81-0.83] v 0.74 [0.74-0.74]) and community oncology (area under the curve 0.89 [0.88-0.90] v 0.86 [0.85-0.88] v 0.77 [0.76-0.79]) practices. CONCLUSION: Routinely collected PROs contain added prognostic information not captured by an EHR-based ML mortality risk algorithm. Augmenting an EHR-based algorithm with PROs resulted in a more accurate and clinically relevant model, which can facilitate earlier and targeted supportive care for patients with cancer.

Willingness to Travel for Cellular Therapy: The Influence of Follow-Up Care Location, Oncologist Continuity, and Race.

PURPOSE: Patients weigh competing priorities when deciding whether to travel to a cellular therapy center for treatment. We conducted a choice-based conjoint analysis to determine the relative value they place on clinical factors, oncologist continuity, and travel time under different post-treatment follow-up arrangements. We also evaluated for differences in preferences by sociodemographic factors. METHODS: We administered a survey in which patients with diffuse large B-cell lymphoma selected treatment plans between pairs of hypothetical options that varied in travel time, follow-up arrangement, oncologist continuity, 2-year overall survival, and intensive care unit admission rate. We determined importance weights (which represent attributes' value to participants) using generalized estimating equations. RESULTS: Three hundred and two patients (62%) responded. When all follow-up care was at the center providing treatment, plans requiring longer travel times were less attractive (v 30 minutes, importance weights [95% CI] of -0.54 [-0.80 to -0.27], -0.57 [-0.84 to -0.29], and -0.17 [-0.49 to 0.14] for 60, 90, and 120 minutes). However, the negative impact of travel on treatment plan choice was mitigated by offering shared follow-up (importance weights [95% CI] of 0.63 [0.33 to 0.93], 0.32 [0.08 to 0.57], and 0.26 [0.04 to 0.47] at 60, 90, and 120 minutes). Black participants were less likely to choose plans requiring longer travel, regardless of follow-up arrangement, as indicated by lower value importance weights for longer travel times. CONCLUSION: Reducing travel burden through shared follow-up may increase patients' willingness to travel to receive cellular therapies, but additional measures are required to facilitate equitable access.

Association Between a National Insurer's Pay-for-Performance Program for Oncology and Changes in Prescribing of Evidence-Based Cancer Drugs and Spending.

PURPOSE: Cancer drug prescribing by medical oncologists accounts for the greatest variation in practice and the largest portion of spending on cancer care. We evaluated the association between a national commercial insurer's ongoing pay-for-performance (P4P) program for oncology and changes in the prescribing of evidence-based cancer drugs and spending. METHODS: We conducted an observational difference-in-differences study using administrative claims data covering 6.7% of US adults. We leveraged the geographically staggered, time-varying rollout of the P4P program to simulate a stepped-wedge study design. We included patients age 18 years or older with breast, colon, or lung cancer who were prescribed cancer drug regimens by 1,867 participating oncologists between 2013 and 2017. The exposure was a time-varying dichotomous variable equal to 1 for patients who were prescribed a cancer drug regimen after the P4P program was offered. The primary outcome was whether a patient's drug regimen was a program-endorsed, evidence-based regimen. We also evaluated spending over a 6-month episode period. RESULTS: The P4P program was associated with an increase in evidence-based regimen prescribing from 57.1% of patients in the preintervention period to 62.2% in the intervention period, for a difference of +5.1 percentage point (95% CI, 3.0 percentage points to 7.2 percentage points; P < .001). The P4P program was also associated with a differential $3,339 (95% CI, $1,121 to $5,557; P = .003) increase in cancer drug spending and a differential $253 (95% CI, $100 to $406; P = .001) increase in patient out-of-pocket spending, but no significant changes in total health care spending ($2,772; 95% CI, -$181 to $5,725; P = .07) over the 6-month episode period. CONCLUSION: P4P programs may be effective in increasing evidence-based cancer drug prescribing, but may not yield cost savings.

Views From Patients With Cancer in the Setting of Unplanned Acute Care: Informing Approaches to Reduce Health Care Utilization.

PURPOSE: New oncology care delivery models that avoid preventable acute care are needed, yet it is unclear which interventions best meet the needs of patients and caregivers. Perspectives from patients who experienced unplanned acute care events may inform the successful development and implementation of care delivery models. METHODS: We performed a qualitative interview study of patients with solid tumors on active treatment who experienced the following 3 types of unplanned acute care events: emergency department visits, first hospitalizations, and multiple hospitalizations. Patients were prospectively recruited within a large academic health system from August 2018 to January 2019. Interviews followed a semi-structured guide developed from the Consolidated Framework for Implementation Research. The constant comparative approach was used to identify themes. RESULTS: Forty-nine patients were interviewed; 51% were men, 75% were non-Hispanic White, and the mean age was 57.4 years (standard deviation, 1.9 years). Fifty-five percent of patients had metastatic disease, and 33% had an Eastern Cooperative Oncology Group performance status of 3-4. We identified the following key themes: drivers of the decision to seek acute care, patients' emotional concerns that influence interactions with the oncology team, and strategies used to avoid acute care. Patients' recommendations for interventions included anticipatory guidance, peer support, improved triage methods, and enhanced symptom management. Patients preferred options for virtual and home-based outpatient care. CONCLUSION: Patient-centered care models should focus on early delivery of supportive interventions that help patients and caregivers navigate the unexpected issues that come with cancer treatment. Patients advocate for proactive, multidisciplinary supportive interventions that enable home-based care and are led by the primary oncology team.

Characteristics of Physicians Participating in Medicare's Oncology Care Model Bundled Payment Program.

PURPOSE: The Oncology Care Model (OCM) is Medicare's first bundled payment program for patients with cancer. We examined baseline characteristics of OCM physician participants and markets with high OCM physician participation to inform generalizability and complement the ongoing practice-level evaluation of the OCM. METHODS: In this cross-sectional study, we identified characteristics of US medical oncologists practicing in 2016, using a national telephone-verified physician database. We linked these data with Dartmouth Atlas and Medicare claims data from 2011 through 2016 to identify characteristics of markets with high OCM participation. We used logistic regression to examine relationships between market characteristics and OCM participation. RESULTS: Of 10,428 US medical oncologists, 2,605 (24.9%) were listed in an OCM practice. There were no differences in sex or medical training between OCM participants and nonparticipants, although OCM participants were slightly younger. OCM participants practiced in larger (median daily patient volume, 80 v 55 patients) and urban practices (95.2% v 90.7%) and were less likely to be part of a health system (41.0% v 60.4%) or solo practice (45.5% v 67.4%; all P < .001). Participation was higher in southern and mid-Atlantic markets. Markets with high OCM physician participation had higher specialist density, hospital care intensity, and acute care use at the end of life (all P < .001). Market-level penetration of Accountable Care Organizations (adjusted odds ratio, 4.65; 95% CI 3.31 to 6.56; P < .001) and Medicare Advantage (adjusted odds ratio 2.82; 95% CI, 1.97 to 4.06; P < .001) were associated with higher OCM participation. CONCLUSION: In the first description of oncologists participating in the OCM, we found differences in practice demographics, care intensity, and exposure to nontraditional payment models between OCM-participating and nonparticipating physicians. Such provider-level differences may not be captured in Medicare's practice-level analysis.

Using Big Data and Predictive Analytics to Determine Patient Risk in Oncology.

Big data and predictive analytics have immense potential to improve risk stratification, particularly in data-rich fields like oncology. This article reviews the literature published on use cases and challenges in applying predictive analytics to improve risk stratification in oncology. We characterized evidence-based use cases of predictive analytics in oncology into three distinct fields: (1) population health management, (2) radiomics, and (3) pathology. We then highlight promising future use cases of predictive analytics in clinical decision support and genomic risk stratification. We conclude by describing challenges in the future applications of big data in oncology, namely (1) difficulties in acquisition of comprehensive data and endpoints, (2) the lack of prospective validation of predictive tools, and (3) the risk of automating bias in observational datasets. If such challenges can be overcome, computational techniques for clinical risk stratification will in short order improve clinical risk stratification for patients with cancer.

A Younger Man With Localized Prostate Cancer Asks, "Which Type of Radiation Is Right for Me?"

The Oncology Grand Rounds series is designed to place original reports published in the Journal into clinical context. A case presentation is followed by a description of diagnostic and management challenges, a review of the relevant literature, and a summary of the authors' suggested management approaches. The goal of this series is to help readers better understand how to apply the results of key studies, including those published in Journal of Clinical Oncology, to patients seen in their own clinical practice. A 61-year-old man presents with stage II prostate cancer after a period of active surveillance. Work-up reveals T1cN0M0 carcinoma, a prostate-specific antigen (PSA) level of 4.8 ng/mL, and Grade Group II (highest Gleason 3+4) in three cores of 12 taken, at the right mid-gland and right apex. The patient has been on active surveillance for the past 16 months. He was originally diagnosed after biopsy for an elevated PSA with stage I prostate cancer, T1cN0M0; PSA, 4.5 ng/mL; Grade Group 1 (Gleason 3+3) in one core of 12 taken, also at the right mid-gland. A multiparametric magnetic resonance imaging scan showed a heterogeneous peripheral zone without a dominant lesion and a calculated prostate volume of 28 mL. His medical history includes hypercholesterolemia, for which he takes atorvastatin. He is otherwise healthy and has no other significant medical or surgical history. His father had prostate cancer in his 70s and died of other causes at 89 years of age. The patient reports 2- to 3-hour urinary frequency and 0 to 1 nocturia, and has no difficulty obtaining or maintaining an erection. After meeting with his urologist, he sees a radiation oncologist.

Best Practices for Reducing Unplanned Acute Care for Patients With Cancer.

Variation and cost in oncology care represent a large and growing burden for the US health care system, and acute hospital care is one of the single largest drivers. Reduction of unplanned acute care is a major priority for clinical transformation in oncology; proposed changes to Medicare reimbursement for patients with cancer who suffer unplanned admissions while receiving chemotherapy heighten the need. We conducted a review of best practices to reduce unplanned acute care for patients with cancer. We searched PubMed for articles published between 2000 and 2017 and reviewed guidelines published by professional organizations. We identified five strategies to reduce unplanned acute care for patients with cancer: (1) identify patients at high risk for unplanned acute care; (2) enhance access and care coordination; (3) standardize clinical pathways for symptom management; (4) develop new loci for urgent cancer care; and (5) use early palliative care. We assessed each strategy on the basis of specific outcomes: reduction in emergency department visits, reduction in hospitalizations, and reduction in rehospitalizations within 30 days. For each, we define gaps in knowledge and identify areas for future effort. These five strategies can be implemented separately or, with possibly more success, as an integrated program to reduce unplanned acute care for patients with cancer. Because of the large investment required and the limited data on effectiveness, there should be further research and evaluation to identify the optimal strategies to reduce emergency department visits, hospitalizations, and rehospitalizations. Proposed reimbursement changes amplify the need for cancer programs to focus on this issue.

Relation Between Narrow Networks and Providers of Cancer Care.

Purpose Health insurers offer plans covering a narrow subset of providers in an attempt to lower premiums and compete for consumers. However, narrow networks may limit access to high-quality providers, particularly those caring for patients with cancer. Methods We examined provider networks offered on the 2014 individual health insurance exchanges, assessing oncologist supply and network participation in areas that do and do not contain one of 69 National Cancer Institute (NCI)-Designated Cancer Centers. We characterized a network's inclusion of oncologists affiliated with NCI-Designated Cancer Centers relative to oncologists excluded from the network within the same region and assessed the relationship between this relative inclusion and each network's breadth. We repeated these analyses among networks offered in the same regions as the subset of 27 NCI-Designated Cancer Centers identified as National Comprehensive Cancer Network (NCCN) Cancer Centers. Results In regions containing NCI-Designated Cancer Centers, there were 13.7 oncologists per 100,000 residents and 4.9 (standard deviation [SD], 2.8) networks covering a mean of 39.4% (SD, 26.2%) of those oncologists, compared with 8.8 oncologists per 100,000 residents and 3.2 (SD, 2.1) networks covering on average 49.9% (SD, 26.8%) of the area's oncologists ( P < .001 for all comparisons). There was a strongly significant correlation ( r = 0.4; P < .001) between a network's breadth and its relative inclusion of oncologists associated with NCI-Designated Cancer Centers; this relationship held when considering only affiliation with NCCN Cancer Centers. Conclusion Narrower provider networks are more likely to exclude oncologists affiliated with NCI-Designated or NCCN Cancer Centers. Health insurers, state regulators, and federal lawmakers should offer ways for consumers to learn whether providers of cancer care with particular affiliations are in or out of narrow provider networks.

Adjuvant radiation therapy for bladder cancer: a dosimetric comparison of techniques.

Trials of adjuvant radiation after cystectomy are under development. There are no studies comparing radiation techniques to inform trial design. This study assesses the effect on bowel and rectal dose of 3 different modalities treating 2 proposed alternative clinical target volumes (CTVs). Contours of the bowel, rectum, CTV-pelvic sidewall (common/internal/external iliac and obturator nodes), and CTV-comprehensive (CTV-pelvic sidewall plus cystectomy bed and presacral regions) were drawn on simulation images of 7 post-cystectomy patients. We optimized 3-dimensional conformal radiation (3-D), intensity-modulated radiation (IMRT), and single-field uniform dose (SFUD) scanning proton plans for each CTV. Mixed models regression was used to compare plans for bowel and rectal volumes exposed to 35% (V35%), 65% (V65%), and 95% (V95%) of the prescribed dose. For any given treatment modality, treating the larger CTV-comprehensive volume compared with treating only the CTV-pelvic sidewall nodes significantly increased rectal dose (V35% rectum, V65% rectum, and V95% rectum; p < 0.001 for all comparisons), but it did not produce significant differences in bowel dose (V95% bowel, V65% bowel, or V35% bowel). The 3-D plans, compared with both the IMRT and the SFUD plans, had a significantly greater V65% bowel and V95% bowel for each proposed CTV (p < 0.001 for all comparisons). The effect of treatment modality on rectal dosimetry differed by CTV, but it generally favored the IMRT and the SFUD plans over the 3-D plans. Comparison of the IMRT plan vs the SFUD plan yielded mixed results with no consistent advantage for the SFUD plan over the IMRT plan. Targeting a CTV that spares the cystectomy bed and presacral region may marginally improve rectal toxicity but would not be expected to improve the bowel toxicity associated with any given modality of adjuvant radiation. Using the IMRT or the SFUD plans instead of the 3-D conformal plan may improve both bowel and rectal toxicity.