Skip to main content

Unfortunately we don't fully support your browser. If you have the option to, please upgrade to a newer version or use Mozilla Firefox, Microsoft Edge, Google Chrome, or Safari 14 or newer. If you are unable to, and need support, please send us your feedback.

Elsevier
Publish with us
Connect

5 ways clinical pathways can improve oncology care

22 October 2022

High-quality pathways support the standardization of oncology care, helping your institution perform more predictably and sustainably in value-based models

Caption: © istock.com

In recent years, there have been important developments in oncology treatment. Advancements in drug discovery, innovations with tumor detection technology and the explosion of targeted therapies have improved outcomes and brought hope to patients and their families.

While these breakthroughs have a positive impact, they also create complexities in the way care is delivered. Oncologists, experiencing a shift in treatment paradigms, are spending more time staying abreast of practice changes. Oncology practices may also find that their profitability is impacted due to the rising cost of therapies and reductions in reimbursement margins.

In my role overseeing the commercial strategy of Elsevier’s oncology clinical pathways, I have had the honor of engaging with some of the leading oncology practices across the country. During these conversations, oncologists and oncology service line leaders have underscored the need for their practice to perform successfully and sustainably in value-based care models. High-quality clinical pathways are a powerful tool to help them achieve this goal through care standardization and predictability of practice patterns.

Pathways support predictable performance in value-based models in several key ways:

1. Decreasing unwarranted care variation with evidence-based decision support

Breakthroughs in cancer detection and treatment are transforming patient care. However, this expansion of treatment options can lead to unwarranted variations in treatment and unpredictable patient outcomes.

High-quality clinical pathways are designed to support oncologists in their treatment decisions by identifying the most optimal treatment regimen for each patient. Pathways consider the hierarchy of efficacy, toxicity and cost when evaluating and prioritizing treatment options. Furthermore, pathways can also support automated ordering of the selected treatment, minimizing the risk of errors from manual order entry.

Pathways help ensure that patients receive consistent care across an institution’s care delivery network, regardless of whether the patient is being treated at an academic medical center or a community practice.

2. Minimizing care coordination gaps with workflow-embedded multi-disciplinary tools

As oncology care has become more sophisticated and holistic, most patients are treated by a multi-disciplinary team of care providers. This integrated approach results in patients feeling more supported throughout their care journey. However, when care gaps occur during hand-offs within a multi-disciplinary team, patient outcomes may be negatively impacted. In fact, across the U.S. health system, it is estimated that $45 billion is wasted opens in new tab/window due to gaps in care coordination.

Pathways are powerful care coordination tools, minimizing the risk of these gaps in care. Oncology pathways account for a multidisciplinary approach to care, incorporating decision support for medical oncology, radiation oncology, surgical oncology, palliative care and clinical trials. Pathways help standardize referral points to radiation or surgery, support data sharing across treatment modalities and provide visibility into treatment decisions made by the rest of the care team.

3. Promoting appropriate use of precision oncology

With the advent of precision oncology, patients now have additional treatment options available, particularly for advanced disease. However, it can be difficult to remain abreast of the required biomarker testing and new indications for precision oncology treatments. An analysis of biomarker testing opens in new tab/window on Stage IV non-small cell lung cancer (NSCLC) patients found less than 25 percent of patients received EGFR testing.

High-quality oncology pathways can help physicians identify when patients may benefit from precision oncology treatments by indicating which patients should be screened for biomarkers. Furthermore, pathways support precision testing by identifying the biomarkers that have been shown to be therapeutically actionable. Finally, based on the test results, pathways can help identify which patients would benefit from precision oncology therapies and support oncologists in selecting the most optimal targeted therapeutic.

In this manner, pathways support the appropriate use of precision oncology from within the clinical workflow. Ensuring that patients are not over- or under-tested, and that targeted therapies are used only when appropriate, helps balance cost management with optimizing patient outcomes.

4. Minimizing penalties from avoidable emergency department visits

Many patients undergoing cancer treatment experience severe symptoms impacting their quality of life. When these symptoms become unmanageable, patients may need to go to the emergency department (ED) for care. However, many of these visits are avoidable – a recent study opens in new tab/window showed that 67 percent of ED visits in the first year of cancer treatment are for preventable causes. ED visits often result in a higher total cost of care, and many value-based programs have penalties associated with this.

Since high quality pathways prioritize therapies with the lowest toxicity profile, the likelihood of patients experiencing severe symptoms is already decreased. Supportive materials like symptom management and triage disposition content can also be used to standardize where patients seek care and manage low acuity symptoms in lower cost settings, such as at home or in office.

Data published opens in new tab/window by a large multi-site health system that used Elsevier’s oncology pathways showed that stage II breast cancer patients treated on-pathway had an average 2-year total cost of care of $111,000 vs $200,000 for patients treated off-pathway – an $89,000 cost savings. These same patients also went to the ED less frequently: 12 percent vs 18 percent.

Pathways benefit patients and support value-based care by helping to decrease ED visits and inpatient admissions, reducing the associated costs and minimizing penalties from avoidable ED visits.

5. Optimizing practice patterns with timely analytics

To participate successfully in value-based models, practices need access to timely, complete and accurate data to identify and address areas of unwarranted care variation. However, many practices do not have the access to data they need, either because it is captured in an unstructured or incomplete format or because there are delays in availability.

Data entered into pathways are captured in a structured and complete manner. This allows oncology practices to use these data to better understand their patient population, determine practice patterns, and identify areas of care variation. Furthermore, these data are available in a timely manner, allowing practices to continually optimize their operations. Pathway data can also be used by pre-certification teams to submit information for payer utilization management programs.

With these data, practices can optimize their operations, predict future practice trends, and evaluate their performance within a value-based model. Practices may also choose to share these data with key stakeholders, such as payers, to support the design of value-based models based on real-world data.

Conclusion

Looking ahead, we expect to continue to see the adoption of value-based care models. Oncology practices participating in these models will require tools to help them remain successful. High quality pathways are a powerful solution to help oncology practices standardize care and achieve predictable performance in value-based models.

Related stories and resources