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5 ways big data helps the healthcare industry

Written by: Nivrutti Shinde

Analytics has been the driving force when it comes to innovation affecting the state of being. While most people associate data analytics only with software projects and pertaining processing, they tend to forget that data analytics have a major say in many fields. In fact, it has been a savior in the field of medical technology. Big Data in the Healthcare Industry has been changing the very face of Healthcare.  

Big Data Analytics for healthcare helps hospitals in various pivotal ways. There are several use cases amidst the medical innovation which have used analytics to create a life-saving invention. The adolescent mental health has been at the forefront of it. Various organizations have begun to use analytics to track various facets and onset of mental illness in any particular patient

Powerful statistics to probe deeper

 According to MentalHealth.org, these are some of the powerful statistics that tell us the state of the mental illness in the world: 

  • Mental health problems are one of the core reasons for the overall disease burden worldwide. 
  • Mental health and behavioral problems (e.g. depression, anxiety, and drug use) are reported to be the primary modes of concerns in the 20 to 29-year-old age group. 
  • Major depression is supposed to be major contributor to the burden of suicide and ischemic heart disease.
  • It is estimated that 1 in 6 people every week experiences a common mental health problem

These statistics are extremely alarming and staggering. At such a time a proper measure of the severance and recurrence of the illness is the need of the hour. Precision plays an important role in this and precision can only be achieved by data analytics. 

Why Analytics?
Data Analytics is mainly predictive in nature. The predictive modeling of data analytics helps us a great deal with the mental illness diagnosis and the state of despair that it has reached. Let us see the top 5 ways in which Big Data benefits the Healthcare and mental illness sector: 

1) Track measures

Hospitals are one place that is flooded with a wide range of patients. At such a time hospitals need to ensure that all the patients are getting the best care possible and are being treated regularly. A robust Healthcare data analytics solution helps a great deal as the organizations can then pull in the EHR data make a track of various measures in retrospect with both the mental health and the patient’s progress. This would include some vital questions such as:

  • If the patients are being monitored regularly?
  • Are they making their visits on a timely basis?
  • Are they taking their medications in accordance with their prescriptions?
  • Have they complained of any uneasiness in the recent past?
  • Did they have any latest episodes?

With mental health being in question even the smallest and the minutest of the details are of vital importance. Data analytics helps in making the entire process swifter. 

 

 

2) Monitoring medications

Monitoring the drug utilization of complex drugs is critical for mental health patients. There are additional risks and hazards involved regarding the side effects of the medication as well.  Innovation in the Healthcare industry using Data analytics can help and make a systematic profile per patient with the respective fields which are deemed necessary according to the severity of his/her situation. The total view of the portfolio helps in giving a comprehensive idea on the stage the patient is in. The field range can be a mix of various factors and measurement criteria in which data analytics will bless with its intelligence. Be it the number of medications allowed or the duration of their use or the side effects that it has, with the help of data analytics all of this can be tracked under one profile.

3) Depression screening

Analytics always has an upper hand when it comes to accumulating a wide range of data. This property can be put to a fair use when it comes to collecting data gathered during the depression screening and profiling processes. This paves the way for the hospitals to realize with the retrieved data if there are certain departments that see a higher rate of depressed people. Along with this, a characteristics analysis can also be made to trace out the commonalities to certain groups of people who are depressed. Additionally, a trend analysis can be carried out to find out to gauge the timely observation curve and conclude whether the screenings in totality have turned out to be an effective process or not.

 

4) Behaviour Tracking

Like the fitness watches and fitness apps that we use which tracks your heartbeat, sleep cycle, food and dietary needs to calculate your daily fitness levels, mental health analytics, and data analytics combined can help track the mental state of a particular individual in question. This can be easily done by combined tracking in the rise and fall of heartbeats, anxiety levels, sleep cycles, etc. With the help of data analytics, we can also add several new alerts onto it for individualistic benefits.

For eg. – If a particular person has a very poor sleep cycle and is suffering from anxiety or hypertension then the data gets collected and the person is notified to sleep for 8 hours and meditate at least for at least 10 minutes a day along with combining a 10-minute walk and 5-minute deep breathing routine. This not only delights the individual in the form of a diversion but also tracks his health progress with the assigned solutions also being tracked.

 

5) Future Predictions and Feedback

Metrics and Feedback informed treatments help a great deal in predictions. FIT (Feedback Informed Treatment)  is psychotherapy metrics that draw on historical data which is used to predict the risks associated with the clients and the risks which they are subjected to on further degradation of their health. These metrics are primarily the surveys that clients fill out during their therapy which consists of detailed accounts of progression through weeks of therapy. The algorithm can further predict when the clients are at a higher risk of dropping out at one particular stage of their treatment. 

Conclusion:
Data analytics is still evolving in terms of it overall targeted growth especially in the mental health sector along with added emphasis on the IT healthcare solutions, but meanwhile even the existing resources can help a great deal to enhance the developments in the mental health sector to not only prevent mental health-related mishaps but to also broaden the scope of medication and recovery.



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