Earlier this year, I had the pleasure of co-authoring an article in Oncology Practice Management with Randy Erickson, RN, MBA, Chief Executive Officer of Utah Cancer Specialists and Linh Mekuria, MPA, clinical informaticist with XIFIN, Inc. The article, titled “The Intersection of Precision Medicine and Data Analysis in the Community Oncology Setting” explores the role of precision medicine and data in the journey of patients with cancer.
Once a patient receives a cancer diagnosis, they begin a journey with a multidisciplinary healthcare team that gathers important data to help optimize their treatment plan. Utah Cancer Specialists (UCS) assesses patients during key points across this treatment journey to examine whether the best clinical decisions are being made to support the patients’ clinical outcomes. The effectiveness of this approach is dependent upon strong relationships and clear communication within the multidisciplinary care team, including physicians, surgeons, radiologists, diagnosticians, pharmacists, nurses, and patients.
Using Data and Analytics to Improve Clinical Outcomes
Integrating community oncology care team workflows and connecting disparate health care systems in a meaningful manner is essential to the success of precision medicine. At UCS, data aggregation and curation across unlinked systems in a longitudinal approach to classify individuals into subpopulations that differ in their susceptibility to a particular disease, or their response to a specific treatment, is a priority. Linking disparate data sources together (e.g., diagnostics and financials) enables UCS to analyze, track, and forecast costs, the effects of future treatment interventions, and perform data-driven risk stratification. All with a single goal of improving clinical outcomes for patients with cancer.
Advancements in data and analytics also provide opportunities to better understand the cancer population. For example, UCS is able to identify and report on important indicators such as “How many patients with metastatic non-small-cell lung cancer tested positive for EGFR and, as a result, were prescribed a tyrosine kinase inhibitor?” and “Of the patients who were initially placed on systemic therapy and later moved over to a tyrosine kinase inhibitor, what was the turnaround time for the test results?”
Using data and analytics also enables UCS to identify important gaps in knowledge. For example, “How many patients with lung cancer were tested, and of those tested, how many had a positive result?” and “Were all testing options made available to obtain the best clinical outcome?” Filling these types of knowledge gaps helps shed light on treatment prescribing patterns and close the loop in a complex system whose collective objective is to ensure the best clinical outcome for patients.
Read the full article to learn more about how UCS identifies and reports on key indicators and important gaps in knowledge.View Article