Healthcare organisations are having their moment when it comes to Big Data and the potential it offers through its analytical capability. From basic descriptive analytics, organisations in this sector are leaping towards the possibilities and its consequent perks of predictive insights. How is this predictive analysis going to help organisations and patients? What are the top roles a Data Analyst look for?
Let’s break this down into ten easy pointers:
- Predicting Patient Deterioration
Many cases of infection and sepsis that are being reported among the patients, can be easily predicted via predictive insights that Big Data offers. Organisations can use big data analytics to predict upcoming deteriorations by monitoring the changes in the patient’s vitals. This helps in the recognition and treatment of the problem even before there are visible symptoms.
- Risk Scoring for Chronic Diseases
Based on lab testing, claims data, patient-generated health data and other relevant determinants of health, a risk score is created for every individual. What this does, is that it leads to early detection of diseases and a significant reduction in treatment costs.
- Avoiding Hospital Re-admission Scenarios
Using predictive analysis, one can deduce risk factor(s) indicating the possibility for re-admission of the patient to the hospital within a certain period. This helps the hospitals design a discharge protocol which prevents recurring hospital visits, making it convenient for the patients.
- Prevention of Suicide
The Electronic Health Records (EHR) provides enough data for predictive algorithms to find the likelihood of a person to commit suicide. Some of the factors influencing this score are substance abuse diagnose, use of psychiatric medications and previous suicide attempts. The early identification helps in providing the mental health care potential risk-patients will need at right time.
- Forestalling Appointment Skips
Predictive analysis successfully anticipates ‘no-shows’ when it comes to patients and this helps prioritise giving appointments to other patients. The EHR provides enough data to reveal individuals who are most likely to skip their appointments.
- Predicting Patient Utilization Patterns
Emergency departments of regular clinics have varying staff strength according to the fluctuations in patient flow. In this case, predictive analysis helps to forecast the utilization pattern and the requirements of each department. It improves patient wait-time and utilisation of facilities.
- The Supply Chain Management
Predictive analysis can be used to make efficient purchasing which in turn has scope for massive cost-reduction. Such data-driven decisions can also help in optimizing the ordering process, negotiate the price and reduce the variations in supplies.
- Development of New Therapies and Precision Medicine
With the aid of predictive analysis, providers and researchers can reduce the need of recruiting patients for complex clinical trials. The Clinical Decision Support (CDS) systems have started to predict the patient response to treatments by analysing genetic information and results of previous patient cohorts. It enables the clinicians to select treatments with more chances of success.
- Assuring Data Security
By using analytic tools to monitor the data access and utilization pattern, it is possible to predict the chances of a potential cyber threat. The system detects the presence of intruders by analysing changes in these patterns.
- Strengthen Patient Engagement and Satisfaction
Insurance companies encourage healthy habits to avoid long-term high-cost diseases. Here, predictive analyses help in anticipating which communication programmes would be the most effective in each patient by analysing past behavioural patterns.
These are possible perks of using tools like predictive analyses in healthcare that optimise processes, increase patient satisfaction, enable better care mechanisms and reduce costs. The role of Big Data is clearly essential as demonstrated and a targeted use can show high-value results!