The impact of outpatient supportive oncology on cancer care cost and utilization.

BACKGROUND: Supportive oncology (SO) care reduces symptom severity, admissions, and costs in patients with advanced cancer. This study examines the impact of SO care on utilization and costs. METHODS: Retrospective analysis of utilization and costs comparing patients enrolled in SO versus three comparison cohorts who did not receive SO. Using claims, the authors estimated differences in health care utilization and cost between the treatment group and comparison cohorts. The treatment group consisting of patients treated for cancer at an National Cancer Institute-designated cancer center who received SO between January 2018 and December 2019 were compared to an asynchronous cohort that received cancer care before January 2018 (n = 60), a contemporaneous cohort with palliative care receiving SO care from other providers in the Southeastern Pennsylvania region during the program period (n = 86), and a contemporaneous cohort without palliative care consisting of patients at other cancer centers who were eligible for but did not receive SO care (n = 393). RESULTS: At 30, 60, and 90 days post-enrollment into SO, the treatment group had between 27% and 70% fewer inpatient admissions and between 16% and 54% fewer emergency department visits (p < .05) compared to non-SO cohorts. At 90 days following enrollment in SO care, total medical costs were between 4.4% and 24.5% lower for the treatment group across all comparisons (p < .05). CONCLUSIONS: SO is associated with reduced admissions, emergency department visits, and total costs in advanced cancer patients. Developing innovative reimbursement models could be a cost-effective approach to improve care of patients with advanced cancer.

Prospective Implementation of a Consultative Store-and-Forward Teledermatology Model at a Single Urban Academic Health System with Real Cost Data Subanalysis.

Background: Teledermatology may increase access to care but has not been widely implemented due, in part, to lack of insurance coverage and reimbursement. We assessed the impact of implementing a consultative store-and-forward teledermatology model on access to care, medical cost, and utilization. Materials and Methods: Prospective implementation of teledermatology occurred at five University of Pennsylvania Health System primary care practices from June 27, 2016, to May 25, 2017. Primary outcomes included time to case completion, proportion of patients completing in-person dermatology visits, and total outpatient costs. Medical and pharmacy claims data were used for utilization and cost subanalysis. Results: The study included 167 patients and 1,962 controls with a 6-month follow-up. Median time to definitive dermatologist response was 0.19 days (interquartile range [IQR]: 0.03-2.92) for intervention and 83.60 days (IQR: 19.74-159.73) for controls. In medical claims subanalysis, no significant differences in mean outpatient costs ($3,366 vs. $2,232, p = 0.1356) or total medical costs ($3,535 vs. $2,654, p = 0.2899) were detected. Conclusions: Implementation of teledermatology improved access to care, and within this small sample, remained comparable in terms of cost and utilization. Thus, these data suggest teledermatology may improve access without increasing utilization or cost.

Assessing Cancer Patient Experience of Care in Outpatient Oncology Practices in the United States.

INTRODUCTION: Cancer patients' experience of care is an important component of quality that has not previously been used for comparing performance. We administered a new patient experience survey to cancer patients receiving outpatient chemotherapy treatment. We examined its measures for sensitivity to adjustment for case-mix and response tendency (level of general optimism/pessimism) and reliability for making performance comparisons between practices. METHODS: We surveyed 2304 cancer patients who received chemotherapy at 23 medical oncology practices in Southeastern Pennsylvania, receiving 715 responses (response rate 31%; 14 practices had 10 or more responses). We aggregated patient responses to calculate practice-level scores on 5 predefined composites: Affective Communication, Shared Decision-Making, Patient Self-Management, Exchanging Information, and Access. We then ranked the practices on each composite before and after adjustment for standard case-mix variables and supplemental adjustment for response tendency (measured via the Life Orientation Test-Revised). We calculated the reliability of practice scores on each composite using hierarchical linear models and calculated minimum sample sizes necessary to achieve reliabilities exceeding 0.7. RESULTS: After adjusting responses for case-mix and converting to a 0-100 scale, composite scores ranged from 77 for the Patient Self-Management composite to 92 for the Access composite. Adjustment for response tendency had an impact on practice rankings only for the Shared Decision-Making composite. The number of responses necessary to create reliable practice-level measurements ranged from 17 (Access composite) to 96 (Affective Communication composite). CONCLUSIONS: Patient experiences at oncology practices can be measured reliably using reasonable sample sizes. Standard case-mix adjustment is adequate for making comparisons on most composites.

Do payor-based outreach programs reduce medical cost and utilization?

There is growing interest in using predictive analytics to drive interventions that reduce avoidable healthcare utilization. This study evaluates the impact of such an intervention utilizing claims from 2013 to 2017 for high-risk Medicare Advantage patients with congestive heart failure. A predictive algorithm using clinical and nonclinical information produced a risk score ranking for health plan members in 10 separate waves between July 2013 and May 2015. Each wave was followed by an outreach intervention. The varying capacity for outreach across waves created a set of arbitrary intervention treatment cutoff points, separating treated and untreated members with very similar predicted risk scores. We estimate a difference-in-differences model to identify the effects of the intervention program among patients with a high score on care utilization. We find that enrollment in the intervention decreased the probability and number of hospitalizations (by 43% and 50%, respectively) and emergency room visits (10% and 14%, respectively), reduced the time until a primary care visit (8.2 days), and reduced total medical cost by $716 per month in the first 6 months following outreach.

The impact of patient-centered medical homes on medication adherence?

Accreditation of providers helps resolve the pervasive information asymmetries in health care markets. However, meeting accreditation standards typically involves flexibility in implementation, leading to heterogeneity in performance. For example, the patient-centered medical home (PCMH) is a leading model for recognizing high-performing primary care practices. Flexibility in PCMH implementation allows for varying degrees of emphasis on processes designed to enhance medication adherence. To assess the impact of the PCMH on adherence, we combine 6 years of detailed patient claims data with a novel dataset containing detailed practice-level PCMH attributes. We study the effects of the number and configuration of adherence-relevant capabilities, using variation in the timing of PCMH adoption to estimate its impact. While PCMH adoption improved overall medication adherence, when combining claims data with the unique recognition data detailing what PCMH capabilities were adopted, we find that these gains are concentrated among patients in practices that adopted more adherence-relevant capabilities. Despite mixed evidence in the literature concerning costs and utilization, our results indicate that PCMH recognition improves medication adherence.

Validation of Molecular Pathology Codes for the Identification of Mutational Testing in Lung and Colon Cancer.

BACKGROUND: Targeted therapy for patients with lung and colon cancer based on tumor molecular profiles is an important cancer treatment strategy, but the impact of gene mutation tests on cancer treatment and outcomes in large populations is not clear. In this study, we assessed the accuracy of an algorithm to identify tumor mutation testing in administrative claims data during a period before test-specific Current Procedural Terminology codes were available. MATERIALS AND METHODS: We used Pennsylvania Cancer Registry data to select patients with lung or colon cancer diagnosed between 2007 and 2011 who were treated at the University of Pennsylvania Health System, and we obtained their administrative claims. A combination of Current Procedural Terminology laboratory codes (stacking codes) was used to identify potential tumor mutation testing in the claims data. Patients' electronic medical records were then searched to determine whether tumor mutation testing actually had been performed. The sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV) were calculated. RESULTS: An algorithm using stacking codes had moderate sensitivity (86% for lung cancer and 81% for colon cancer) and high specificity (98% for lung cancer and 96% for colon cancer). Sensitivity and specificity did not vary significantly during 2007-2011. In patients with lung cancer, PPV was 98% and NPV was 92%. In patients with colon cancer, PPV was 96% and NPV was 83%. CONCLUSIONS: An algorithm using stacking codes can identify tumor mutation testing in administrative claims data among patients with lung and colon cancer with a high degree of accuracy.

Access to Mental Health and Substance Use Treatment in Comprehensive Primary Care Plus.

Importance: To meet increasing demand for mental health and substance use services, the Centers for Medicare & Medicaid Services launched the 5-year Comprehensive Primary Care Plus (CPC+) demonstration in 2017, requiring primary care practices to integrate behavioral health services. Objective: To examine the association of CPC+ with access to mental health and substance use treatment before and during the COVID-19 pandemic. Design, Setting, and Participants: Using difference-in-differences analyses, this retrospective cohort study compared adults attributed to CPC+ and non-CPC+ practices, from January 1, 2018, to June 30, 2022. The study included adults aged 19 to 64 years who had depression, anxiety, or opioid use disorder (OUD) and were enrolled with a private health insurer in Pennsylvania. Data were analyzed from January to June 2023. Exposure: Receipt of care at a practice participating in CPC+. Main Outcomes and Measures: Total cost of care and the number of primary care visits for evaluation and management, community mental health center visits, psychiatric hospitalizations, substance use treatment visits (residential and nonresidential), and prescriptions filled for antidepressants, anxiolytics, buprenorphine, naltrexone, or methadone. Results: The 188 770 individuals in the sample included 102 733 adults (mean [SD] age, 49.5 [5.6] years; 57 531 women [56.4%]) attributed to 152 CPC+ practices and 86 037 adults (mean [SD] age, 51.6 [6.6] years; 47 321 women [54.9%]) attributed to 317 non-CPC+ practices. Among patients diagnosed with OUD, compared with patients attributed to non-CPC+ practices, attribution to a CPC+ practice was associated with filling more prescriptions for buprenorphine (0.117 [95% CI, 0.037 to 0.196] prescriptions per patient per quarter) and anxiolytics (0.162 [95% CI, 0.005 to 0.319] prescriptions per patient per quarter). Among patients diagnosed with depression or anxiety, attribution to a CPC+ practice was associated with more prescriptions for buprenorphine (0.024 [95% CI, 0.006 to 0.041] prescriptions per patient per quarter). Conclusions and Relevance: Findings of this cohort study suggest that individuals with an OUD who received care at a CPC+ practice filled more buprenorphine and anxiolytics prescriptions compared with patients who received care at a non-CPC+ practice. As the Centers for Medicare & Medicaid Innovation invests in advanced primary care demonstrations, it is critical to understand whether these models are associated with indicators of high-quality primary care.

Reassessing the management of uncomplicated urinary tract infection: A retrospective analysis using machine learning causal inference.

Background: Uncomplicated urinary tract infection (UTI) is a common indication for outpatient antimicrobial therapy. National guidelines for the management of uncomplicated UTI were published by the Infectious Diseases Society of America in 2011, however it is not fully known the extent to which they align with current practices, patient diversity, and pathogen biology, all of which have evolved significantly in the time since their publication. Objective: We aimed to re-evaluate efficacy and adverse events for first-line antibiotics (nitrofurantoin, and trimethoprim-sulfamethoxazole), versus second-line antibiotics (fluoroquinolones) and versus alternative agents (oral ß-lactams) for uncomplicated UTI in contemporary clinical practice by applying machine learning algorithms to a large claims database formatted into the Observational Medical Outcomes Partnership (OMOP) common data model. Outcomes: Our primary outcome was a composite endpoint for treatment failure, defined as outpatient or inpatient re-visit within 30 days for UTI, pyelonephritis or sepsis. Secondary outcomes were the risk of 4 common antibiotic-associated adverse events: gastrointestinal symptoms, rash, kidney injury and C. difficile infection. Statistical methods: We adjusted for covariate-dependent censoring and treatment indication using a broad set of domain-expert derived features. Sensitivity analyses were conducted using OMOP-learn, an automated feature engineering package for OMOP datasets. Results: Our study included 57,585 episodes of UTI from 49,037 patients. First-line antibiotics were prescribed in 35,018 (61%) episodes, second-line antibiotics were prescribed in 21,140 (37%) episodes and alternative antibiotics were prescribed in 1,427 (2%) episodes. After adjustment, patients receiving first-line therapies had an absolute risk difference of -2.1% [95% CI -2.9% to -1.6%] for having a revisit for UTI within 30 days of diagnosis relative to second-line antibiotics. First-line therapies had an absolute risk difference of -6.6% [95% CI -9.4% to -3.8%] for 30-day revisit compared to alternative ß-lactam antibiotics. Differences in adverse events were clinically similar between first and second line agents, but lower for first-line agents relative to alternative antibiotics (-3.5% [95% CI -5.9% to -1.2%]). Results were similar for models built with OMOPlearn. Conclusion: Our study provides support for the continued use of first-line antibiotics for the management of uncomplicated UTI. Our results also provide proof-of-principle that automated feature extraction methods for OMOP formatted data can emulate manually curated models, thereby promoting reproducibility and generalizability.

Association of a Remote Blood Pressure Monitoring Program With Postpartum Adverse Outcomes.

OBJECTIVE: To use administrative claims data to evaluate the association of a remote blood pressure monitoring program with adverse postpartum clinical outcomes in patients with a hypertensive disorder of pregnancy. METHODS: This was a retrospective cohort study of Independence Blue Cross members with a hypertensive disorder of pregnancy diagnosis across three obstetric hospitals from 2017 to 2021. Patients who were enrolled in twice-daily text-based blood pressure monitoring for 10 days postpartum were compared with two propensity-score matched cohorts of patients who met the program criteria: an asynchronous cohort (cohort A), consisting of patients at any of the three participating hospitals before remote monitoring program implementation, and a contemporaneous cohort (cohort C), consisting of patients at other hospitals during the same time period as clinical use of the program. Patients with less than 16 months of continuous insurance enrollment before delivery were excluded. Claims for adverse clinical outcomes after delivery discharge were evaluated. Health care service utilization and total medical costs were evaluated. RESULTS: The 1,700 patients in remote blood pressure monitoring program were matched to 1,021 patients in cohort A and 1,276 in cohort C. Within the first 6 months after delivery, patients enrolled in remote monitoring were less likely to have the composite adverse outcome than those in cohort A (2.9% vs 4.7%; OR 0.61, 95% CI 0.40-0.98). There was no statistically significant difference relative to cohort C (3.2% vs 4.5%; OR 0.71, 95% CI 0.47-1.07). The remote monitoring group had more cardiology visits and fewer postnatal emergency department (ED) visits and readmissions compared with both comparison cohorts. Reductions in ED visits and readmissions drove overall lower total medical costs for the program cohort. CONCLUSION: Patients enrolled in a remote blood pressure monitoring program were less likely to experience an adverse outcome in the first 6 months after delivery. Reductions in ED visits and readmissions resulted in lower postpartum total medical costs compared with both control cohorts. Broad implementation of evidence-based remote monitoring programs may reduce postpartum adverse outcomes, thereby reducing morbidity and mortality in populations such as the one studied here.

Impact of the collaborative care model on medical spending.

OBJECTIVES: The collaborative care model integrates mental health care into primary care. In 2017, CMS created new billing codes to reimburse collaborative care. We measured the impact of a program supported by these codes on medical spending. STUDY DESIGN: Quasi-experimental. METHODS: We identified a commercially insured and managed Medicare sample of 825 patients who received collaborative care services in 8 primary care practices. We used propensity score matching to match treated patients to potential controls, resulting in 569 patients per group. We performed a difference-in-differences regression analysis to evaluate the impact of collaborative care on total medical spending, including medical, psychiatric, and pharmaceutical claims. RESULTS: Collaborative care patients' mean total medical cost began to fall after a patient's third month in the program and fell below the mean cost of control patients at month 7. Difference-in-differences regressions indicate a nonsignificant savings in total medical cost of $29.35 per member per month for patients in collaborative care compared with matched controls (95% CI, -$226.52 to $167.82). Treated members incurred $34.11 (95% CI, $31.95-$36.27) higher primary care costs that were directly attributed to collaborative care, $19.91 (95% CI, $4.84-$34.98) higher costs for other mental or behavioral health care, and a nonsignificant reduction of $91.34 (95% CI, -$319.32 to $136.63) in inpatient costs. CONCLUSIONS: Modest spending on collaborative care services to address the behavioral health needs of patients did not increase overall health care costs. This is the first economic study of a collaborative care program supported by the new billing codes.

On the impact of predictive analytics-driven disease management interventions.

OBJECTIVES: To evaluate the effect of a predictive algorithm-driven disease management (DM) outreach program compared with non-predictive algorithm-driven DM program participation on health care spending and utilization. STUDY DESIGN: We used propensity score matching forMedicare Advantage members with chronic heart failure (CHF) to evaluate the impact of predictive algorithm-driven DM outreach using claims data from 2013 to 2018 from a large commercial health insurer. METHODS: The insurer ran a predictive algorithm to identify members with CHF with a high likelihood of hospitalization (LOH), and a DM outreach was initiated to those identified as being at high risk of hospitalization (high-LOH intervention group). The intervention group was matched to members with similar concurrent medical risk profiles, based on the DxCG/Verisk score, who received the same DM outreach through the insurer's standard process (low-LOH intervention group). This approach allowed an evaluation of the predictive algorithm in targeting individuals suitable for DM outreach. RESULTS: Regression models showed that high-LOH intervention members had a lower probability of hospitalization (0.032; P = .075) and emergency department (ED) visit (0.039; P = .043) in the year after the outreach compared with low-LOH intervention members, leading to lower total outpatient spending ($1517; P < .001). Analyses for no-intervention members showed that predictive outreach members would have been expected to have higher inpatient and ED utilization and higher medical spending compared with the traditional care members. CONCLUSIONS: A prediction-driven DM outreach program among patients with CHF was effective in reducing medical spending in the year after the outreach compared with traditional DM outreach programs.

The Potential For Bias In Machine Learning And Opportunities For Health Insurers To Address It.

As the use of machine learning algorithms in health care continues to expand, there are growing concerns about equity, fairness, and bias in the ways in which machine learning models are developed and used in clinical and business decisions. We present a guide to the data ecosystem used by health insurers to highlight where bias can arise along machine learning pipelines. We suggest mechanisms for identifying and dealing with bias and discuss challenges and opportunities to increase fairness through analytics in the health insurance industry.

Telemedicine catches on: changes in the utilization of telemedicine services during the COVID-19 pandemic.

OBJECTIVES: To determine the degree of telemedicine expansion overall and across patient subpopulations and diagnoses. We hypothesized that telemedicine visits would increase substantially due to the need for continuity of care despite the disruptive effects of COVID-19. STUDY DESIGN: A retrospective study of health insurance claims for telemedicine visits from January 1, 2018, through March 10, 2020 (prepandemic period), and March 11, 2020, through October 31, 2020 (pandemic period). METHODS: We analyzed claims from 1,589,777 telemedicine visits that were submitted to Independence Blue Cross (Independence) from telemedicine-only providers and providers who traditionally deliver care in person. The primary exposure was the combination of individual behavior changes, state stay-at-home orders, and the Independence expansion of billing policies for telemedicine. The comparison population consisted of telemedicine visits in the prepandemic period. RESULTS: Telemedicine increased rapidly from a mean (SD) of 773 (155) weekly visits in prepandemic 2020 to 45,632 (19,937) weekly visits in the pandemic period. During the pandemic period, a greater proportion of telemedicine users were older, had Medicare Advantage insurance plans, had existing chronic conditions, or resided in predominantly non-Hispanic Black or African American Census tracts compared with during the prepandemic period. A significant increase in telemedicine claims containing a mental health-related diagnosis was observed. CONCLUSIONS: Telemedicine expanded rapidly during the COVID-19 pandemic across a broad range of clinical conditions and demographics. Although levels declined later in 2020, telemedicine utilization remained markedly higher than 2019 and 2018 levels. Trends suggest that telemedicine will likely play a key role in postpandemic care delivery.

Psychosocial determinants of cancer-related information seeking among cancer patients.

This study explores the utility of using the Integrative Model of Behavioral Prediction as a framework for predicting cancer patients' intentions to seek information about their cancer from sources other than a physician, and to examine the relation between patients' baseline intentions to seek information and their actual seeking behavior at follow-up. Within 1 year of their diagnosis with colon, breast, or prostate cancer, 1,641 patients responded to a mailed questionnaire assessing intentions to seek cancer-related information from a source other than their doctor, as well as their attitudes, perceived normative pressure, and perceived behavioral control with respect to this behavior. In addition, the survey assessed their cancer-related information seeking. One year later, 1,049 of these patients responded to a follow-up survey assessing cancer-related information seeking during the previous year. Attitudes, perceived normative pressure, and perceived behavioral control were predictive of information-seeking intentions, although attitudes emerged as the primary predictor. Intentions to seek information, perceived normative pressure regarding information seeking, baseline information-seeking behavior, and being diagnosed with stage 4 cancer were predictive of actual information-seeking behavior at follow-up. Practical implications are discussed.

The impact of the non-essential business closure policy on Covid-19 infection rates.

In response to the Covid-19 pandemic, many localities instituted non-essential business closure orders, keeping individuals categorized as essential workers at the frontlines while sending their non-essential counterparts home. We examine the extent to which being designated as an essential or non-essential worker impacts one's risk of being Covid-positive following the non-essential business closure order in Pennsylvania. We also assess the intrahousehold transmission risk experienced by their cohabiting family members and roommates. Using a difference-in-differences framework, we estimate that workers designated as essential have a 55% higher likelihood of being positive for Covid-19 than those classified as non-essential; in other words, non-essential workers experience a protective effect. While members of the health care and social assistance subsector contribute significantly to this overall effect, it is not completely driven by them. We also find evidence of intrahousehold transmission that differs in intensity by essential status. Dependents cohabiting with an essential worker have a 17% higher likelihood of being Covid-positive compared to those cohabiting with a non-essential worker. Roommates cohabiting with an essential worker experience a 38% increase in likelihood of being Covid-positive. Analysis of households with a Covid-positive member suggests that intrahousehold transmission is an important mechanism driving these effects.

Disruptions in preventive care: Mammograms during the COVID-19 pandemic.

OBJECTIVE: To measure the extent to which the provision of mammograms was impacted by the COVID-19 pandemic and surrounding guidelines. DATA SOURCES: De-identified summary data derived from medical claims and eligibility files were provided by Independence Blue Cross for women receiving mammograms. STUDY DESIGN: We used a difference-in-differences approach to characterize the change in mammograms performed over time and a queueing formula to estimate the time to clear the queue of missed mammograms. DATA COLLECTION: We used data from the first 30 weeks of each year from 2018 to 2020. PRINCIPAL FINDINGS: Over the 20 weeks following March 11, 2020, the volume of screening mammograms and diagnostic mammograms fell by 58% and 38% of expected levels, on average. Lowest volumes were observed in week 15 (April 8 to 14), when screening and diagnostic mammograms fell by 99% and 74%, respectively. Volumes began to rebound in week 19 (May), with diagnostic mammograms reaching levels to similar to previous years' and screening mammograms remaining 14% below expectations. We estimate it will take a minimum of 22 weeks to clear the queue of missed mammograms in our study sample. CONCLUSIONS: The provision of mammograms has been significantly disrupted due to the COVID-19 pandemic.

Cancer information scanning and seeking in the general population.

The amount of cancer-related information available in the media and other sources continues to increase each year. We wondered how people make use of such content in making specific health decisions. We studied both the information they actively seek ("seeking") and that which they encounter in a less purposive way ("scanning") through a nationally representative survey of adults aged 40-70 years (n = 2,489) focused on information use around three prevention behaviors (dieting, fruit and vegetable consumption, and exercising) and three screening test behaviors (prostate-specific antigen, colonoscopy, mammogram). Overall, respondents reported a great deal of scanning and somewhat less seeking (on average 62% versus 28% for each behavior), and they used a range of sources including mass media, interpersonal conversations, and the Internet, alongside physicians. Seeking was predicted by female gender, age of 55-64 vs. 40-44, higher education, Black race and Hispanic ethnicity, and being married. Scanning was predicted by older age, female gender, and education. Respondents were fairly consistent in their place on a typology of scanning and seeking across behaviors. Seeking was associated with all six behaviors, and scanning was associated with three of six behaviors.

Patient-centered oncology care: impact on utilization, patient experiences, and quality.

OBJECTIVES: To determine whether implementation of patient-centered oncology standards in 5 medical oncology practices improved patient experiences and quality and reduced emergency department (ED) and hospital use. STUDY DESIGN: Retrospective, pre-post study design with a concurrent nonrandomized control group. METHODS: We used insurance claims to calculate all-cause hospitalizations and ED visits and primary care and specialist office visits (n = 28,826 eligible patients during baseline and 30,843 during follow-up) and identify patients for a care experiences survey (n = 715 preintervention and 437 postintervention respondents). For utilization and patient experience outcomes, we compared pilot practices' performance with 18 comparison practices using difference-in-differences (DID) regression models accounting for practice-level clustering. We assessed pilot practice performance on 31 quality measures from the American Society of Clinical Oncology Quality Oncology Practice Initiative program. RESULTS: There were no statistically significant differences in hospital, ED, or primary care visits between the pilot and comparison groups over time, but there was a significant increase in specialty visits for the pilot group (adjusted DID of 0.07; 95% CI, 0.01-0.13; P = .03). For care experiences, pilot practices improved more on shared decision-making (4.03 DID composite score; P = .013), whereas the comparison group improved more on access (-6.36 DID composite score; P < .001) and exchanging information (-4.25 DID composite score; P = .013). On average, pilot practices improved performance on 65% of core quality measures from baseline to follow-up. CONCLUSIONS: This pilot of patient-centered oncology care showed improved quality but no impact on hospitalizations/ED use and mixed results for patient experiences. Findings are consistent with early evaluations of primary care patient-centered medical homes.

Predicting intentions to engage in cancer prevention and detection behaviors: examining differences between Black and White adults.

Reducing cancer-related mortality rates can be achieved by increasing cancer screening rates and by increasing the number of people who engage in healthy lifestyle behaviors. This study uses the Integrative Model of Behavioral Prediction (IM; Fishbein, 2000) to examine differences between Blacks and Whites in the US in the degree to which attitudes, perceived behavioral control (PBC) and normative pressure contribute to predicting intentions to engage in three cancer screening behaviors (mammogram, colonoscopy and PSA test) and three healthy lifestyle behaviors (controlling ones diet to lose weight, eating fruits and vegetables and exercising regularly). Prior research has demonstrated that these behaviors are effective at reducing incidence and mortality rates for some cancers. Results indicated that for Blacks intentions to engage in all behaviors were driven by PBC. Patterns were more varied for Whites and indicated that normative pressure was a particularly important determinant of screening intentions whereas attitudes were most strongly associated with dieting intentions. Results suggest that interventions targeting these behaviors should be tailored by behavior and by ethnicity.

The effect of predictive analytics-driven interventions on healthcare utilization.

This paper studies a commercial insurer-driven intervention to improve resource allocation. The insurer developed a claims-based algorithm to derive a member-level healthcare utilization risk score. Members with the highest scores were contacted by a care management team tasked with closing gaps in care. The number of members outreached was dictated by resource availability and not by severity, creating a set of arbitrary cutoff points, separating treated and untreated members with very similar predicted risk scores. Using a regression discontinuity approach, we find evidence that predictive analytics-driven interventions directed at high-risk individuals reduced emergency room and specialist visits, yet not hospitalizations.