Streamline Your Cancer Care Practice With Oncology Analytics
Oncology analytics helps cancer care centers collect, analyze, and integrate cancer-related data. The information they get as a result helps them improve patient outcomes, reduce costs, and pace up research.
Generally, oncology analytics include:
- Analysis of clinical data from EHRs
- Real-time evidence gathering from patient outcomes
- Predictive modeling for appropriate treatment selection
- Payer and health care system analysis
- Clinical research
As of now, analytics adoption is happening on a large scale across the US. Practices and health systems are investing large sums into it. And that’s justifiable if you just look at the available data. Cancer resulted in 9.6 million deaths worldwide in 2018 alone. The total number of cases reported was 17 million.
This resulted in the healthcare bodies raising the ante in cancer research. The FDA approved 66 indications of oncology-related medication in 2020. That led to a high number of treatment options becoming available. Surely, this created hope for both the patients and practitioners. But it also increased complexity.
With overall cancer care spending exceeding $200 billion in 2020, practitioners suddenly faced the need to get large amounts of data. The challenge was the lack of centralization and smart data processing.
Things started improving when IT firms started working on healthcare data. The development of oncology data analytics systems made RCM more manageable. These smart systems use data from all sources (such as EMRs, pathology reports, genomic sequencing, and imaging reports) and create unified insights that support effective treatment planning.
Why Oncology Analytics Is Important
Let’s understand this from a scenario. You walk into a large treatment facility where practitioners use insights from thousands of similar cases to make clinical decisions. Not only do they predict which treatment would work, but they also personalize treatment plans based on your medical history and genetic makeup. This is not a fictional scenario. This is exactly what oncology analytics has made possible.
Analytics are the algorithmic minds that can gather, process, and organize scattered information to create actionable insights. They can identify patterns no human can recognize. The amount of data they can deal with and the number of parameters they can process; it reveals a lot about how far technology has advanced.
The way these systems work is incredible. They can flag early warning signs in weeks instead of waiting for months’ data. This allows oncologists to use evidence-based data instead of making educated guesses.
This entire effect sets the stage for a widespread adoption of value-based care models. Hospitals can now actually achieve precision in treatment planning and administration. The situation now allows practices to adhere to policies like MIPS more than ever.
Benefits of Oncology Data Analytics
Analytics helps cancer treatment centers and hospitals in the following ways.
Outcome Tracking
The system provides a real-time view of whether treatments work. It continuously tracks metrics related to:
- Treatment responses
- Survival rates
- Quality of life
This provides clinicians with enough data to act quickly if something isn’t working.
Treatment Optimization
As mentioned above, analytics use data from thousands of patients with similar conditions. This gives the system collective experience and parameters to suggest the best possible treatment. These suggestions are personalized as the system also uses EHRs and genetic profile information.
Quick Approval
This one is a real lifesaver. Hospitals and treatment centers can pitch prior authorization requests with evidence-based data. This helps payers make quick approval decisions.
Data Types And Sources For Oncology Analytics
The tech used in oncology analytics systems makes them compatible with both formal and unconventional sources of data.
- Structured data: This includes information like patient age, medication doses, and blood pressure readings.
- Unstructured data: This data type covers more nuanced information about the patient. For instance, the doctor may write, “Patient looks fatigued but in high spirits.” Now, this is not a quantified detail. But it helps practitioners a lot in treatment planning.
- Wearable data: This one is a more contemporary way of gathering data. It requires the patients to wear special trackers or update their details through apps. These details can help reveal side effects and recovery patterns rather quickly.
- Multi-omic data: This data helps with more precise medication and therapy. It involves tracking both genomic sequencing and proteomic/metabolomic data tracking. This provides comprehensive details about mutations and tumor behavior.
Challenges in Analytics Data Integration
The technological progress has been immense in cancer care. But it hasn’t completely resolved all the data integration challenges. This is because most of these challenges are more organizational than technical.
- Data silos: Many organizations still have separate systems for labs, radiology, and EMRs. It would require years for them to replace these systems with modern/centralized ones.
- Quality problems: Even the most modern systems require human interaction. This is where the chance of error always exists. Any wrong entry can render an entire process or at least a part of it invalid.
- Privacy issues: Cancer data is one of the most sensitive types of health information. When a patient gives it to the doctor, they expect its complete security. Some patients get particularly concerned about the safety of their genetic information and treatment details. This situation becomes even trickier when you add the authorization of data access to researchers to the equation.
The Role of AI
Artificial intelligence has the capability to fix most pitfalls of oncology analytics. That’s because it performs functions like pattern recognition and predictive modeling. Both these features process large amounts of scattered data to get actionable insights. This requires analyzing large datasets, which AI’s sophisticated algos are quite capable of doing.
Another essential feature of AI is NLP, which is particularly useful in getting insights from unstructured clinical and pathological data. This feature builds context out of unstructured data and puts it into a structured format. Modern NLP models can analyze symptoms, treatment effects, and side effects listed in the medical documents.
AI is here to stay and revolutionize cancer care. It will only get better with consistent feedback and data inputs. Experts are adjusting its algorithms as of now. It is just a matter of time before it gains the ability to train itself using the available clinical data and form new parameters.
Wrapping Up
Oncology analytics has significantly influenced cancer care, and in a good way. It has resolved many data-related challenges that have obstructed quality care for a long time. Some challenges do exist, but let’s not forget this technological evolution has just gotten out of its infancy. There is plenty more to come.
That said, integrating data analytics requires proper planning. With many vendors out there, you can expect many to use attractive sales pitches yet overlook the features you want the most. Be sure to have a good idea about your requirements and pick a solution that fulfills those needs.
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