A reference research paper: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7088441/
UNDERSTAND PATIENT DATA
Healthcare-workers are one of the most challenged in terms of availability of time and information. If Patients' data are available to doctor even before patient enters his/her chamber, then doctor will save a lot of time and be able to diagnose far more accurately. Patient Data can also help Hospital management to predict remission-rates, schedule appointments and plan financials better. Few cutting edge applications of use of patients' data are:
(1) Feed data to AI models to get many possible pre-diagnoses which doctors can combine with his knowledge to arrive at final diagnosis. AI models are not to replace doctors but to aid them just like other instruments and tools.
(2) The X-rays , CT Scans , MRI etc. are important for diagnosis but they are often unclear and require doctor with years of experience to detect small anomalies. However Computer vision can be used to de-noise these images and then Big data and AI based diagnostic tools are used to to aid doctor in accurate diagnosis with help of expertise of thousands of other doctors on a similar image.
(3) Data-science and AI provide the groundwork for drug discovery. Mutation Profiling and the metadata of the patients are used to develop compounds.
(4) Data-science has greatly contributed to Genomics. This has made possible to treat even genetic disorders by gene repair treatments.
DIGITAL HEALTH RECORDS
Healthcare sector is a rich source of data. This data, when computerized, form digital/Electronic health records (EHR). EHR may include:
PERSONALIZED CARE
There is renewed focus on integrated patient-centered care in which the patient’s specific care needs and other characteristics must be addressed. While it is practically impossible to develop care models and intervention programs for each individual, programs can be created for groups —each with specific needs, characteristics, or behaviors—to allow care delivery and policies to be tailored for these groups of patients with similar characteristics. The creation of these groups is known as patient segmentation.
ANALYZE LATEST TRENDS
New trends are emerging daily in Health-Care sector. At the same time health-care professionals are the most time-starved persons and can not keep with all new trends. Here is the role of an ‘Expert System’, that derives data from some of the world's most reliable healthcare research centers, classifies them disease-wise and provides summarized view with option to go in detail. Doctors just need to glance at a dashboard and can get selected cutting edge research delivered to them.
The word ‘Trend Analysis’ is also used in other context. As we saw during recent COVID epidemic the government must be able to predict trends in constantly evolving trajectory of disease so that limited resources can be deployed efficiently and quickly.
PREDICT HEALTH OUTCOMES
A predictive model uses historical data, learns from it, finds patterns and generates accurate predictions from it. In healthcare domain, it finds various correlations and association of symptoms, finds habits, diseases and then makes meaningful predictions. Such models are used to improve patient care, chronic disease management , increasing the efficiency of supply chains and pharmaceutical logistics. One topic increasingly becoming popular is “Population Health Management”. It is a data-driven approach focusing on prevention of diseases. Using predictive models, hospitals can predict the deterioration in patient’s health, provide preventive measures and start an early treatment that will assist in reducing the risk of the further aggravation of patient health.
IMPROVED INTEGRATED MANAGEMENT
In most foreign countries large hospitals with hundreds of patients is a norm. Same is the case with corporate hospitals in India. Such big establishments must be data driven for efficient management. For example data science can help in:
PREVENTIVE TREATMENT
The best way to transform healthcare is to recognize risks and recommend prevention plans before health risks become a major issue. Through wearable and other tracking devices that take into account historical patterns and genetic information, it’s possible to recognize a problem before it gets out of hand. For example,
BETTER PATIENT EXPERIENCE-REDUCED DOCTOR STRESS
In several researches conducted across many developed countries, it has been found that adoption of digitization improves ‘Patient Satisfaction Level’ by around +11.3 to +15.6%, reduces 'Healthcare staff's stress levels' by 8.5 to 11%, improves ‘Overall Productivity’ by 12 to 13.8% and reduces ‘Public Health Expenditure’ by 6 to 7%. Considering the massive amount of money spent on healthcare and the critical nature of the profession, these metrics are very impressive.
BETTER PROGNOSIS, OPTIMIZED GROWTH
Data-driven healthcare establishments have registered consistently better patient outcomes. More satisfied patients lead to higher growth. At the same time, the same data helps to design optimal growth strategies with minimal risk. The beauty of data based solution is, with growth and time, more data is available and with more data, the machine learning models become even more accurate.
