- The healthcare industry has a large number of sensors and digital devices to investigate and monitor patients’ health. There are also laboratory reports, tests, payer information, emails, notes of health professionals etc. with a wealth of data.
- BryteFlow can ingest structured and unstructured data for analytics and Machine Learning from a variety of sources on a single platform that can be used to identify operational inefficiencies, actionable insights and waste. Real-time predictive data analytics can be used in research, to provide better bedside care, reduce risk and improve safety.
Better medical interventions and patient care
Instead of manually monitoring a patient’s vital signs, healthcare professionals can respond to situations proactively by having wireless sensors record and transmit patient data which can be collected and prepared by BryteFlow. The data collected can alert healthcare caregivers to take pre-emptive action if they sense a worsening of the situation. Over time the accumulated data can be used for healthcare predictive analytics to form algorithms that can predict and avert a medical crisis.
Preventing fraud and financial crimes
BryteFlow ingests very large datasets and prepares them for predictive modeling so healthcare providers can hone in on fraud and criminal practices. Over-use of hospital facilities in short time frames, treatments from different hospitals simultaneously, prescriptions for the same patient presented in multiple locations are red flags that come up when patient billing and records get under the data analytics scanner.
RFID to track caregivers, drugs and equipment
RFID tags, sensors and scans of devices can help record date of manufacture, shipping information, order number and location of devices and items. This radio frquency identification data helps hospitals to keep a track of essential items. They can plan inventory, restocking or even locate some needed item. Data from various sources and devices can be integrated and prepared by BryteFlow so medical facilities can use it for predictive healthcare analytics.
Data analytics and Machine Learning for healthcare research
Cutting edge medical research typically features very huge datasets. For e.g. medical trials involve using all kinds of data right from demographic data, patient data, trial controls and protocols, drug interactions and patient responses. BryteFlow has the capability to ingest and prepare this multi-source data for healthcare analytics and Machine Learning so researchers and doctors can form an informed and precise view of the trial’s outcomes.
Getting to know patients and providing a better customer care experience
The personal and medical data of every patient is logged into the medical facilities system – prescription, treatment details, attending doctors, payments, hospitalization records and more. The data is probably locked into legacy databases which BryteFlow can ingest and prepare to provide a unified view of the patient. Whenever a patient has a query or needs an appointment or needs to make a payment, he can be assured of getting a quick response rather than being subjected to the usual time-consuming barrage of questions. The hospital can send greetings on patients’ birthdays and anniversaries, inform them of hospital offers and promotions and generally build a long-term relationship with the patient.