Healthcare industry is generating enormous amounts of structured and unstructured data. Finding innovative insights from data is of particular interest to Providers to incentivize predictive and preventive health management. This leads to healthcare industry focus on breaking data silos and leverage intelligent algorithms backed with advance analytics to create value. New age machine learning algorithms including natural language processing, pattern recognition, and deep learning are enabling better healthcare. I would like to bring up the following developments,
- Algorithms for Cybersecurity: Patient privacy and Cybersecurity are key focus areas for every healthcare provider. Sophisticated algorithms are aiding human skills with an ability to patrol security perimeters with more sensitivity and responsiveness. Algorithms identify patterns of normal usage and alert or flag events that are out of the ordinary by calculating a risk score for specific events as they happen based on the similarity or not to the normal behavior observed for the user performing the specific events. Supervise machine learning can cull things out that are less risky or classify them in terms of making multiple categories, like in terms of malware families. Getting Healthcare industry is in an early stages of implementing cognitive technologies ensuring security and reduce the rising threat of ransomware.
- Algorithms for Unstructured Data (EHR to MRI Data): Extracting usable meaning from voice recordings of patient interactions, PDF images of faxed lab reports, and free-text HER are critical to an effective healthcare provision. This is where algorithms like natural language processing (NLP) can turn images of text into editable documents, extract semantic meaning from those documents, or process search queries written in plain text to return accurate results.
- Algorithms for Clinical Support: Another important focus area is an ability extract meaning from large volumes of free text for better clinical decision support. Algorithms are helping ranging from precision medicine techniques to augment physician-guided diagnosis. Identifying and addressing risks quickly can significantly improve outcomes for patients with number of serious conditions, both clinical and behavioral. The silo nature of data organization and analysis limits the insights that doesn’t tell us a great deal about whether or not the patient has actually gotten better as a result of accessing that care. Semi-supervised and unsupervised machine learning algorithms like Clustering & Dimension Reduction can help improve the processes and dig deeper into that data and all the other variables that impact an individual’s life.
- Algorithms for Pathology and Imaging: Healthcare organizations in improving patient outcomes is relying on Improved imaging analytics and pathology. Machine learning can supplement the skills of human radiologists by identifying subtler changes in imaging scans more quickly, potentially leading to earlier and more accurate diagnoses. Google research published during March 2017 – a new approach to imaging analytics driven by machine learning algorithms can identify metastasized breast cancer with sensitivity rates that exceed other automated methods and even rival human pathologists, is one of the best examples.
21st Century Healthcare industry has a tremendous opportunity to seize with smart usage of data science breakthroughs and evolution of intelligent machine learning algorithms.