Real-world risk assessment and treatment initiation among patients with myelofibrosis at community oncology practices in the United States.

Myelofibrosis (MF) is a chronic myeloproliferative neoplasm with a prevalence of 4 to 6 per 100,000 people in the USA. Treatment recommendations are risk-adapted. This study was conducted to evaluate how physicians risk-stratify patients at the time of MF diagnosis, the accuracy of the risk stratification, and its effect on treatment selection. Medical charts were reviewed at US community hematology/oncology practices in the Cardinal Health Oncology Provider Extended Network; patient clinical characteristics, risk stratification, and treatment data were collected. Physician-assigned risk categorizations were compared with data-derived risk categorizations based on the International Prognostic Scoring System, the system recommended at diagnosis. A total of 491 patients diagnosed with MF between 2012 and 2016 (mean [SD] age at diagnosis, 65.4 [11.8] years; 54.8% male, 69.2% with primary MF) were included. Risk categorization was not assigned for 30.1% of patients. Of the patients with a physician-assigned risk categorization (n = 343), a scoring system was used in 49.9%. Compared with data-derived risk categorizations, 42.9% of physician-assigned risk categorizations were incorrect; 85.0% of incorrect physician-assigned risk categorizations were underestimations. Notably, 38.5% of patients with data-derived intermediate- or high-risk categorizations did not initiate treatment within 120 days of diagnosis. Among patients with data-derived intermediate risk, those with an underestimated physician-assigned risk categorization were significantly less likely to receive treatment within 120 days of diagnosis (51.6% with correct physician-assigned categorization vs 18.5% with underestimated risk categorization; P = 0.0023). These results highlight the gap in risk assessment and the importance of accurate risk stratification at diagnosis.

The business of genomic testing: a survey of early adopters.

PURPOSE: The practice of "genomic" (or "personalized") medicine requires the availability of appropriate diagnostic testing. Our study objective was to identify the reasons for health systems to bring next-generation sequencing into their clinical laboratories and to understand the process by which such decisions were made. Such information may be of value to other health systems seeking to provide next-generation sequencing testing to their patient populations. METHODS: A standardized open-ended interview was conducted with the laboratory medical directors and/or department of pathology chairs of 13 different academic institutions in 10 different states. RESULTS: Genomic testing for cancer dominated the institutional decision making, with three primary reasons: more effective delivery of cancer care, the perceived need for institutional leadership in the field of genomics, and the premise that genomics will eventually be cost-effective. Barriers to implementation included implementation cost; the time and effort needed to maintain this newer testing; challenges in interpreting genetic variants; establishing the bioinformatics infrastructure; and curating data from medical, ethical, and legal standpoints. Ultimate success depended on alignment with institutional strengths and priorities and working closely with institutional clinical programs. CONCLUSION: These early adopters uniformly viewed genomic analysis as an imperative for developing their expertise in the implementation and practice of genomic medicine.

An Innovative Interactive Modeling Tool to Analyze Scenario-Based Physician Workforce Supply and Demand.

Effective physician workforce management requires that the various organizations comprising the House of Medicine be able to assess their current and future workforce supply. This information has direct relevance to funding of graduate medical education. We describe a dynamic modeling tool that examines how individual factors and practice variables can be used to measure and forecast the supply and demand for existing and new physician services. The system we describe, while built to analyze the pathologist workforce, is sufficiently broad and robust for use in any medical specialty. Our design provides a computer-based software model populated with data from surveys and best estimates by specialty experts about current and new activities in the scope of practice. The model describes the steps needed and data required for analysis of supply and demand. Our modeling tool allows educators and policy makers, in addition to physician specialty organizations, to assess how various factors may affect demand (and supply) of current and emerging services. Examples of factors evaluated include types of professional services (3 categories with 16 subcategories), service locations, elements related to the Patient Protection and Affordable Care Act, new technologies, aging population, and changing roles in capitated, value-based, and team-based systems of care. The model also helps identify where physicians in a given specialty will likely need to assume new roles, develop new expertise, and become more efficient in practice to accommodate new value-based payment models.

The Cancer Genomics Resource List 2014.

CONTEXT: Genomic sequencing for cancer is offered by commercial for-profit laboratories, independent laboratory networks, and laboratories in academic medical centers and integrated health networks. The variability among the tests has created a complex, confusing environment. OBJECTIVE: To address the complexity, the Personalized Health Care (PHC) Committee of the College of American Pathologists proposed the development of a cancer genomics resource list (CGRL). The goal of this resource was to assist the laboratory pathology and clinical oncology communities. DESIGN: The PHC Committee established a working group in 2012 to address this goal. The group consisted of site-specific experts in cancer genetic sequencing. The group identified current next-generation sequencing (NGS)-based cancer tests and compiled them into a usable resource. The genes were annotated by the working group. The annotation process drew on published knowledge, including public databases and the medical literature. RESULTS: The compiled list includes NGS panels offered by 19 laboratories or vendors, accompanied by annotations. The list has 611 different genes for which NGS-based mutation testing is offered. Surprisingly, of these 611 genes, 0 genes were listed in every panel, 43 genes were listed in 4 panels, and 54 genes were listed in 3 panels. In addition, tests for 393 genes were offered by only 1 or 2 institutions. Table 1 provides an example of gene mutations offered for breast cancer genomic testing with the annotation as it appears in the CGRL 2014. CONCLUSIONS: The final product, referred to as the Cancer Genomics Resource List 2014, is available as supplemental digital content.