Optimizing Care for Cancer Patients: Biomarker Testing and Documentation
In the first post of this series, we introduced our research partnership with AstraZeneca to better understand the trends and care gaps for managing patients with five main cancer types. In the second post, we described our findings related to assessment and care coordination. Here, we summarize findings related to biomarker testing.
Biomarker testing1 analyzes tissue, blood or other body fluid samples to identify genes, proteins or other molecules that may indicate a disease or condition, such as cancer, or that look for changes in a gene or chromosome that may increase a person’s risk of developing cancer or other diseases. It can also be used to help diagnose certain types of cancer, plan treatment, determine treatment effectiveness, make a prognosis or predict whether cancer will return or spread to other parts of the body.
Somatic and germline biomarker testing are specific to oncology. Somatic testing looks at tumor tissue for mutations. Germline tests look for gene mutations that can be passed down in families.
Discussions with provider research participants revealed multiple challenges with biomarker testing2-5:
There may be a long turnaround time for testing with solid tumors, tissue availability delays and germline testing delays, all of which may present problems for patients who need urgent treatment.
With significant data on emerging, actionable biomarkers and the interpretation of results for next-generation sequencing, it can be difficult for providers to keep up with the evidence in real time.
Capturing structured results from biomarker testing is inconsistent. Particularly with third-party vendors, orders may be placed and results received outside of the electronic health record (EHR), results may be uploaded to the EHR in a delayed timeframe, and not all staff have access to results in the portal.
Results may be received as summary data that omits comprehensive information, and unstructured results may not be useful for clinical tools such as dotphases, clinical decision support alerts and more.
(Dotphrases are abbreviations used in EHR documentation that serve as shortcuts to insert longer pre-set text or templates – helping with rapid documentation of patient information such as lab results, patient demographics and treatment history.)
With these points in mind, our researchers looked at EHR unstructured notes from two health systems. Manual review of records found documentation of testing that was not being captured – and in some cases, patients were also declining testing.
To help address these and other challenges, Premier and AstraZeneca developed a testing and treatment toolkit that can help streamline the biomarker testing and documentation process and make better use of the EHR. The toolkit includes biomarker/pre-treatment checklists, a testing and treatment tracker dotphrase and a dotphrase implementation guide.
Provider research participants shared positive feedback on the resources, calling them valuable and concise, useful. Information is provided all in one place for chart reviews, data capture and quality measures, which can also help streamline prior authorization.
The toolkit includes a biomarker data case study, which highlights successes, learnings and tips from a study site in optimizing data integration and tracking of biomarker results in the EHR. It includes background information on the current state of biomarker testing, information on specific IT and data challenges that exist in tracking and organizing biomarker data, an IT diagram/flowchart and additional tips, insights and considerations.
Want to learn more? Contact Misty Anderson for additional information on this project and Diane Loughlin if you are interested in getting involved with future research studies.
Authors:
Misty Anderson, MBAHM, BSN, RN, LSSMBB, oversees the Improvement Science team of Premier Applied Sciences (PAS). She is responsible for building standards that support evidence-based review, qualitative analysis, research and quality methods, content management and training for new and existing customers within PAS.
Cate Polacek, MLIS, CMPP, ELS, is a Senior Medical Writer on the Applied Research team within PAS. She provides writing, editing, academic research and publication services across PAS. She writes journal articles, patient and provider education, white papers, study protocols, literature reviews and qualitative analyses for all major therapeutic areas.
Nicholas Travis, MSN, BSN, RN, FNP-C, is a Manager of Content Generation on the PAS Improvement Science team. He develops tools, resources and other clinical content to support various projects. He creates frameworks, care pathways, webinar presentations, clinical support tools, patient and provider facing materials, and other clinical content for projects.
Erika Klump, MS, is the Technical Product Director on the Data, Technology and Innovation team of PAS. She works with research teams, health care systems, engineers and informaticists to ensure PAS projects have the structured or unstructured data and technology needed for project objectives.
Erica Robichaud, PT, DPT, MHA, is the Project Owner and Director on the Improvement Science team. She is responsible for overseeing the project, ensuring the quality and timeliness of deliverables and maintaining customer services. Her collaboration with the team, sites, providers and subject matter experts is key to the successful execution and completion of the project.
Andrew Long is an Analytics Developer on the Data, Technology, and Innovation team of PAS. He works to develop data pipelines integrating structured and unstructured data. He informs and implements data validation and optimization strategy for leveraging NLP solutions and supports various other PAS data solutions.
Nancy Rios is a Research Analyst on the Data, Technology, and Innovation team. She collaborates closely with cross-functional teams including research, health care systems and technology experts. Her responsibilities extend to developing and implementing data validation procedures, leveraging natural language processing solutions for data optimization and informing data quality enhancement strategies.
References:
1. National Cancer Institute. Biomarker testing. Accessed March 26, 2024. https://www.cancer.gov/publications/dictionaries/cancer-terms/def/biomarker-testing
2. Zarinshenas R, Amini A, Mambetsariev I, et al. Assessment of Barriers and Challenges to Screening, Diagnosis, and Biomarker Testing in Early-Stage Lung Cancer. Cancers (Basel). Mar 3 2023;15(5)doi:10.3390/cancers15051595
3. West HJ, Lovly CM. Ferrying Oncologists Across the Chasm of Interpreting Biomarker Testing Reports: Systematic Support Needed to Improve Care and Decrease Disparities. JCO Oncol Pract. Aug 2023;19(8):530-532. doi:10.1200/OP.23.00010
4. Hess LM, Michael D, Krein PM, Marquart T, Sireci AN. Costs of biomarker testing among patients with metastatic lung or thyroid cancer in the USA: a real-world commercial claims database study. J Med Econ. Jan-Dec 2023;26(1):43-50. doi:10.1080/13696998.2022.2154479
5. American Cancer Society. Survey Findings Summary: Understanding Provider Utilization of Cancer Biomarker Testing Across Cancers. 2021. Accessed March 5, 2024. https://www.fightcancer.org/sites/default/files/national_documents/provider_utilization_of_biomarker_testing_polling_memo_dec_2021.pdf