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Predictive Analytics Tools That Improve Cancer Treatment

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Businesses in all industries are leveraging predictive analytics (PA) to make better decisions. The healthcare industry, in particular, has made great strides thanks to PA. The technology is helping healthcare professionals predict diseases ahead of time while providing doctors with data on the most effective treatment options.

Healthcare organizations are investing in predictive analytics to improve their oncology departments due to the urgent nature of a cancer diagnosis. With recent advancements in predictive analytics, doctors can more effectively identify risk factors and develop personalized treatment plans for cancer patients.

Health Information Technology Platforms: Improve Decision Making in Oncology

One exciting application of PA in the oncology world is the CancerLinQ initiative, which uses patient data to help doctors make more informed treatment decisions. Oncologists can easily access a patient‘s entire medical history and other relevant data. The system then compares this data with the profiles of similar patients in order to suggest the best treatment plan. The tool has been useful in helping healthcare professionals make decisions about complex cases.

In addition, CancerLinQ looks at a patient’s pain levels and vitals to predict which medications and tests would be the most useful in treating the patient. The tool can offer these insights by analyzing the success rate of multiple treatments for patients in various stages of cancers.

CancerLinQ’s goal is to offer real-time feedback to oncologists so they can compare their treatment decisions with clinical guidelines and the suggestions of their fellow doctors. It reveals personalized insights about patients while also predicting the best treatment route based on patterns observed in similar cases.

Health Information Technology Platforms: Meet Clinical Standards

CancerLinQ also puts oncologists under the microscope, as the tool monitors whether doctors are meeting cancer care standards. The tool examines various quality-care metrics, such as whether an oncologist keeps tabs of a patient’s cancer stage a month after their first visit to the doctor’s office.

CancerLinQ is using PA to ensure that patients are getting the best treatment. By monitoring the performance levels of doctors, the tool provides an incentive for oncologists to put in the highest quality work possible.

Risk Stratification Models: Reduce Chemotherapy-Related Hospitalizations

The risks of being hospitalized due to chemotherapy can be reduced greatly with predictive modeling tools that monitor a patient’s risk level. This is accomplished with risk stratification, which uses a patient’s vitals and other clinical data to assess the chances of them being hospitalized. The risk stratification model looks at a variety of factors, including the type of chemotherapy the patient is undergoing, the cancer stage, their age, their vitals and their BMI or weight. Moreover, the tool compares these datasets with earlier data about the patient, as factors such as weight loss can indicate that the patient is at risk.

The PA tool uses this data to produce the patient’s risk stratification score—either high, moderate or low. Doctors can then make treatment decisions based on a patient’s score. The tool is still in its early stages, but it is one example of how oncologists are leveraging patient data to reduce the risks of dangerous side effects related to cancer treatments.

Enhanced Screening: Detecting Lung Cancer Earlier

It doesn’t take a medical degree to know that smoking cigarettes for an extended period of time puts you at higher risk of getting lung cancer. However, detecting lung cancer early on can greatly reduce some of the condition’s more severe symptoms.

In 2015, Chesapeake Regional Healthcare rolled out a lung cancer screening campaign that uses predictive analytics to identify and educate population segments at a higher risk of getting lung cancer. Chesapeake Regional Healthcare was able to get 5.21% of new patients, as well as 9.17% of all patients it targeted in its marketing campaign, to get lung screenings.

The organization worked with cloud-based analytics services provider Tea Leaves Health to put data from the initiative to good use. Together, they used PA technology and self-reported smoker data to more accurately predict who may need a screening. They contacted more than 25,700 at-risk smokers over the first two and a half years of the initiative, screening hundreds of returning patients and some new ones. Chesapeake Regional Healthcare’s campaign has been a success in terms of identifying patients who are at risk for lung cancer.

Machine Learning: Predicting Leukemia Relapse and Remission

Indiana University-Purdue University Indianapolis researchers combined PA with machine learning to predict relapse rates for acute myelogenous leukemia (AML) patients with a whopping 90% accuracy. Their algorithm uses a patient’s bone marrow and real-time medical data and compares it with data from past patients and healthy individuals.

Researchers believe the tool may soon replace traditional tools that predict remission and relapse rates with less accuracy. The traditional method is cytometry, which analyzes various components of cells—including cell count, size, shape and structure—to learn more about blood or bone marrow. The Indiana University’s tool takes data from cytometry to extract insights from a patient’s cell information with a higher degree of accuracy.

All these applications of predictive analytics tools in oncology are essentially attempting to achieve the same goal: to save lives by predicting a patient’s best care regimen with a higher degree of accuracy than traditional methods. In addition, Predictive Analytics in oncology has the potential to lower patient and hospital costs, creating a more effective and efficient healthcare system across the board.

If you’re hoping to develop an oncology tool that uses predictive analytics, you will need a developer who has experience with PA technology. At 7T, we specialize in custom mobile app development with extensive experience in predictive analytics. We’re also proficient with other emerging technologies, including augmented reality, virtual reality, artificial intelligence, blockchain and natural language processing.

7T is headquartered in Dallas, but we also serve clients in Austin, Houston, and beyond. If you’re ready to discuss your project, we hope you will contact us today.

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Shane Long

As COO and President of 7T, Shane Long brings experience in mobility that pre-dates the term “smartphone” and the release of the first iPhone. His work has helped revolutionize the growth of mobility by bringing to market one of the first graphics processors used in mobile phones, technology that after being acquired by Qualcomm lived well into the 4th generation of smartphones, as well as helped pioneer the first GPS implementations in the segment. With a strong engineering and business background, Shane understands how the rise of mobility and Predictive Analytics is crucial to greater business strategies geared toward attaining competitive advantage, accelerating revenue, and realizing new efficiencies. As the leader of a B2B mobility solutions provider, he partners with business leaders including marketers and product developers to leverage enterprise mobile applications, big data and analytics, and mobile strategy.

Shane earned a B.S. at Texas A&M (whoop!) and studied mathematics as a graduate student at Southern Methodist University.


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