Healthcare organizations are sophisticated users of data and analytics. Predictive analytics is more useful in healthcare scenarios. Based on historical data predictive algorithm can forecast the likelihood of an event occurring and the system can generate alerts for this. This would help all the parties like doctors and hospital staff to plan ahead of the event and make more informed decisions. Early warning of possible deterioration or mishap can help save a patient’s life if acted upon very quickly. This would be of immense use in case of emergency care.
As a student of distance MBA in Healthcare and hospital management, one should be aware of this new trend. The healthcare industry has started adopting analytics-based solutions. Such types of solutions are not only helpful in saving patient’s lives but also reduce the cost of healthcare. Let us see some common use cases for applying data analytics in the healthcare domain.
Reduce cost using Risk Scoring
The cost of healthcare has increased manifold in recent years. Admitting the person to the hospital after reaching a chronic condition will prove to be very costly. Prevention efforts can help control these rising costs. Organizations can identify individuals with a high risk of developing a chronic condition.
To do this successfully comprehensive data about each patient needs to be collected. This data includes clinical data generated in labs, claims data, and any other important information like social demographic data, etc.
When this data is fed into a predictive model, risk scores can be calculated. These scores can help service providers to identify high-risk patients. These patients can then be referred to appropriate sources for further treatment. Such early warnings would help save overall expenditure for the patients.
Forecasting appointment No-shows
For the consultation with the health practitioner, we need to make his appointment. Many times you would have observed that though an appointment is taken, patients do not turn up. This not only creates gaps in the schedule for the practitioners but deprives some patients who could have had consultations in the same time slot. Additionally, this has financial ramifications for the physicians and hospitals.
Here, predictive analytics can be used to identify patients who are most likely to skip an appointment without prior notice. Clinics can take some proactive steps like giving more reminders, making transport arrangements if possible, even asking them to shift the appointment to a more convenient time, or offering these slots to other patients.
Predicting utilization patterns
In hospitals, there are in-patient and out-patient wards. Inpatient wards need to keep enough beds for new patients so the ‘availability of beds’ is their primary concern while out-patient wards focus on ‘reducing wait times’ for patients. For this hospital needs to be sufficiently staffed.
Emergency care wards do not operate on fixed schedules. The required staff level varies based on the inflow of patients. Keeping the optimum level of staff based on the patient inflow is one of the key challenges in front of hospital administration.
Using predictive analytics, patient flow patterns can be predicted. This will help deploy enough staff (optimize the cost) and also reduce wait times for the patients.
Selecting the best treatment plan
Let us see how the treatment plan for any patient is decided. After the initial diagnosis, physicians prescribe the plan based on their expert knowledge combined with his/her gut feel and the medical history of the patient. Currently, there is no other reference to which a physician can refer to.
However, if data is made available in the form of patient population cohorts (i.e. relevant groups), physicians can have a reliable alternate reference for best treatment plans. Machine learning algorithms may be able to suggest some of the better treatment plans from which the physician may choose and modify if needed.
This methodology has its advantages. With all this effort, the patient could be directed to the best treatment plan based on his medical history, current conditions, and the observed outcomes of other patients in the cohort This will have a better chance of improving outcomes for the patient.
Optimizing the drug discovery process
Drug discovery is a complicated process involving a lot of effort in testing. It asks for a huge time as well as a financial commitment. Generally, it takes more than 10 years to successfully make a new drug and submit it officially.
Drug discovery is a process of many steps. With data science and predictive algorithms success rate at each of its steps can be predicted based on various biological factors. Advanced computing models can help forecast how the drug would act on the body by simulation exercises without actually experimenting in the lab. This will help companies decide which trials should be focused on.
Real-time alerts
In hospitals, there are advanced systems like Clinical Decision Support (CDS). CDS software analyzes available medical data in real-time and provides advice to medical practitioners in hospitals. However, hospital admissions are costly and doctors want patients to stay away from hospitals. Now this poses a challenge of how to monitor these patients.
New technology of wearable devices can be a solution to this problem. Using these gadgets patient’s health data can be continuously collected and sent to the cloud.
With the help of advanced tools, this massive data stream can be monitored. In case of any alarming situation, a physician would receive alerts so that he/she can take action immediately and avoid further complications.
Healthcare analytics and distance MBA in Healthcare and hospital management
The industry has started seeing the benefits of analytics and big data solutions. As a student of distance MBA in healthcare and hospital management, you need to be aware of application of data analytics and its benefits.
