No Average Patient – Leveraging Data for Precision Healthcare

No Average Patient – Leveraging Data for Precision Healthcare

The evolution of healthcare has come a long way since local physicians made house calls and homespun remedies were formulated using items from the kitchen spice rack. Today’s healthcare is driven as much by the promise of emerging technologies centered on data processing and advanced analytics as by developing new and specialized drugs. This has ushered in a new era of precision healthcare focused on the uniqueness of each patient and the multitude of variables that factor into more precise and effective detection and treatment regimens. 

What is Precision Healthcare?

Precision healthcare is a rapidly evolving data-driven approach that tailors treatments and prevention strategies to the individual characteristics of each patient, rather than a one-size-fits-all or groups of cohorts approach that matches the treatment to the disease itself. Precision healthcare leverages data and AI-powered advanced analytics to predict and prevent disease and to identify the most effective treatments for each patient.

The foundation of precision healthcare is the use of data from a variety of sources. And by combining and analyzing these different data sources, healthcare providers can gain a comprehensive understanding of each patient’s unique health profile, including their genome sequence, microbiome composition, health history, lifestyle, diet and environmental factors.  The ability to analyze the more “granular level” of data of the individual and to compare and look for a ‘similar’ individual is key to establish the cohort of 1 with a level of precision.

How Data Drives Precision Healthcare

One of the key advantages of precision healthcare is the ability to use large datasets to identify patients at a higher risk of developing a particular disease. For example, if a patient has a family history of colon cancer, genetic testing can be used to determine if they have an increased risk of developing the disease. This information can then be used to develop a personalized prevention and screening plan, which may include more frequent colonoscopy screeningsor other interventions.

Data is also used to identify the most effective treatments for each patient. By analyzing a patient’s genomic makeup using machine learning (ML) algorithms, healthcare providers can identify specific mutations or genetic markers that may indicate a particular treatment will be more effective than others. For example, a patient with a certain genetic mutation may respond better to a specific chemotherapy drug than other patients with the same type of cancer.

In addition to genetic data, precision healthcare also uses data from other sources, such as electronic health records (EHRs) and wearable devices, to monitor patient health and identify potential health issues before they become serious. 

For example, wearable devices can track a patient’s heart rate, activity level, and sleep patterns, providing insights into their overall health and well-being. This data can be used to identify early warning signs of potential health problems or adverse reactions to their care plan, allowing healthcare providers to intervene early and prevent more serious complications.

Data is also critical for research and development in precision healthcare. By using ML to analyze large amounts of data from diverse populations, researchers can identify new patterns and relationships that can inform the development of new treatments and prevention strategies.

But the use of data in precision healthcare also presents a number of challenges. Precision healthcare relies on the ability to collect and analyze massive amounts of data from a wide range of sources – unstructured, semi-structured, structured and streaming, including those mentioned above.. This data must be accurate, complete, and standardized in order to be useful for analysis and decision-making with  the ability to monitor, predict and take action in real-time.

Focus on Breast Cancer

Consider mammogram mass detection for breast cancer. Radiologists spend a lot of time manually evaluating digital images looking to identify asymmetries, irregular density, clusters of calcifications, as well as areas of thickening skin. Detecting these abnormalities as well as changes in tumor size and appearance is crucial in determining the subsequent course of a patient’s treatment plan. This can be a tedious process open to interpretive or perception errors. 

With Cloudera Machine Learning radiologists have a force multiplier at the edge, as ML-powered computer vision can analyze mammograms as well as CT scans, X-rays and MRIs with greater speed and accuracy and detect abnormal masses that are not visible to the human eye. This gives radiologists a supportive advanced analytics solution to make more informed clinical decisions, provide more timely diagnoses and ultimately increase caseload.  

Solving The Data Challenge with Cloudera

Cloudera solves the data challenge by giving healthcare organizations a hybrid data platform that can manage and analyze data across the full data lifecycle – data distribution, data engineering, data warehousing, data science, and machine learning. 

Healthcare organizations can ingest, process, store and analyze any type of data, in the data center, in public and hybrid clouds or at edge locations. By analyzing data at rest, in motion and streaming, healthcare organizations can use advanced analytics, machine learning and AI to identify patterns, detect anomalies and predict potential outcomes.

To learn more about how Cloudera supports Precision Health, join me in person at HIMSS23. We’ll be presenting a Lunch and Learn at the event and food will be provided.

Register Now

When: Wednesday, April 19 at 11:00 AM – 12:15 PM CT 

Where: McCormick Place West Building

2301 S. Indiana Ave.

Level 4 | W470 A

 Chicago, IL 60616

Nasheb Ismaily
Senior Solutions Engineer, Public Sector
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