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Introduction

Everyone at some point or the other comes down with an illness. We go to the hospital and expect to be in the safe hands of a physician whom we expect to know everything about the sickness and give the right dosage of medicine right? Well, CBS news reported that 12 million Americans are misdiagnosed each year. Physicians are very smart, undergo rigorous training and do everything they can to stay up to date but everyone makes mistakes. Doctors are still humans and can forget, have an oversight or even human error. Also, even if physicians have access to a lot of data needed to treat a patient, it would still take some time for the doctor to modify those data to suit the patient’s unique illness.

People are becoming more conscious about their health, people use wearable devices to monitor heart rate and other vital body organs. Record keeping, compliance and industry regulation creates lots of data for the healthcare industry. Raghuapathi and Raghuapathi, said in 2011, the US healthcare alone reached 150 Exabyte of data. Data analytics expert at Quantzig said, “Analytics help healthcare industry players to form this data and leverage them to derive meaningful insights.” Hence, the healthcare industry is turning to data analytics to help make their services easier and better.

Data on its own is useless, it has to be analyzed by a team of experts to make anything meaningful out of it. In order to get the best results from data analytics, the data has to be clean. Dirty data has to be ‘scrubbed’ and ‘cleaned’ so they can be accurate.

Ten Reasons Why Data Analytics Is Good For Healthcare Industry

1. Lowers Cost: Data analytics lowers administrative cost to the hospital and reduces the cost to the patient. From statistical data, it is shown that a quarter of healthcare cost goes to administrative cost mainly because humans are required for administrative tasks.

Data analytics can also help predict cost for employers that provide healthcare benefits for their staff. The company can use its own database, liaise with insurance companies and the hospital to get the best price and most effective services for their employees.

Hospitals, insurance service providers and pharmaceutical companies can have better control over their supply chain. With better data on what medications or services are most required at the moment, the health industry can make just the right amount of orders leading to significant increase in savings. Increase in savings means more profit for the pharmaceutical company.

2. Facilitate diagnosis: Data analytics can facilitate a clinical decision. It can bring all prescribed medicine, lab test report and medical history of a patient to a single screen. This can help the clinical team to see a fuller view of the patient’s condition and give a better prescription.

Furthermore, data analytics can help the medical team to make a better-informed decision on otherwise tricky cases. For instance, a patient with chest pain in the ER might not necessarily need to be hospitalised. This is often difficult for doctors to know, however, if the doctors enter his complaints, pain points along with their medical judgements, the system can give information on the safety of the patient if sent home.

It is important to note that data analytics doesn’t replace the sound judgment of the medical team. It is only to aid them in making better and informed decision.

3. Check fraud and abuse: Fraud and abuse is a big hole in the pocket for the healthcare industry. According to Payer Fusion, fraud and abuse are estimated to cost the health industry $80 billion in value.

Fraud includes inflating bills, falsifying records, changing or extending dates or magnifying services rendered to make more money. Abuse, on the other hand, is overbilling the patient, rendering services that the patient does not require or not maintaining a proper record.

Data analytics makes the process of payment transparent and also monitors how doctors are treating their patients. This ensures the patient is not exploited or the health system cheated by the patient. George Zachariah, a consultant at Dynamics Research Corporation in Andover, said, “Analytics can track fraudulent and incorrect payments, as well as the history of an individual patient.” He further mentioned that “However, it’s not just about the analytic tool itself but understanding the tool and how to use it to get the right answers.

4. Better care coordination: Data analytics can help with better care coordination among hospitals. Lisa Rapaport’s article on Reuters Health, 2017, revealed that few American hospitals share electronic records. She said more than one-third of hospitals report never using it and less than 50% report actually having the electronic data of their patients. This is an improvement because only 30% of Hospitals reported having electronic data of their patient in 2015.

About 96% of hospitals that have digital records do not share these records among themselves. This is like Windows not being able to send an email to Mac OS. The major reason put forward by hospitals is the amount vendors charge to link hospitals together. Farzad Mostashari, the former Health IT czar at HHs said, “And the vendor is saying, ‘Oh, OK that will cost you $50,000.’ Now, does it cost the vendor $50,000 to build a standard interface? No, it doesn’t cost them $50,000,” he further mentioned, “It’s their opportunity to make a buck.” Data analytics helps create a centralised system that all hospitals can plug into without having to pay exorbitant prices.

However, hospitals need to improve on sharing patients’ record because, without a system that can extract a patient’s medical history, the patient’s family would have to go from hospital to hospital to get previous records. This can increase the physical, emotional and psychological burden on the family and patient.

5. Improved patient wellness: Hospitals can use Data analytics to check on and monitor their patients. They can use data analytics to ensure that their customers are living a healthy lifestyle. This gives doctors the ability to monitor their patient’s health and well being.

Patients can also work closely with doctors being better informed about their health. Patients would use apps or wearable devices to monitor their vitals. This data can be automatically received by the doctor and he can advise on the best health practice for the individual. This precise data passed to the doctor would give a more accurate prescription for the patient.

6. Improved staff and customer satisfaction rate: Hospitals can monitor and improve on both staff and customer satisfaction. With the aid of data analytics, the medical can be more confident in the decisions they make. They are less stressed because medical conditions that might take hours to diagnose or take decisions on can be done in few minutes. The medical team can also handle more patients in a given time period.
Patients’ conditions are also more accurately diagnosed. They’d receive medications that would work best for them and would not have to use medications that work for most people.

7. More visibility into performance/boost competitive advantage: Hospitals can have more insights into their performance. They can easily have information such as check-in time and the time taken to respond to a patient. The management team can have a full view of where they are lacking and can improve their services.

Hospitals using data analytics make better informed medical decisions on patients and would have better treatment. This would lead to increased patronage and of course with a better system to manage patients, the hospital can manage more patients per time, leading to an increase in market share because they’re using data analytics that’s giving them a competitive advantage.

8. Help researchers develop models: Researchers can now develop models without needing to have years of data or thousands of samples. Data analytics can provide data for researchers and this can improve with accuracy over time.

There are two ways data analytics help researchers in the health sector. Data experts can use predictive analytics or initial models. Predictive analytics uses information in its database. It uses statistical tools to draw conclusions and predict future trends. On the other hand, researchers can use initial models in which they start with a small number of cases and then accuracy is increased over time as more cases are added. It is a learning model that adapts with present-day knowledge and improves in accuracy over time.

It is important to note that researchers need to make use of data across different platforms or electronic data owned by different hospitals. In order to help researchers, the government has mandated electronic records to be compatible with one another. A program really useful to researchers is STATISTICA. STATISTICA is a program that has been used across industries including banking, pharmaceutical companies, and government agencies. This program works seamlessly with more popular programs like Microsoft.

On the other hand, researchers have to pay for data from some companies. However, researchers may find that these systems may not compatible with the industry standard. Using non-compatible data can be grievous when dealing with human life. Therefore, a central system like data analytics working across different platforms is really advantageous.

9. Boost preventive medicine and public health: Data analytics can diagnose diseases very early. Doctors using analytics are detecting diseases a lot earlier before they can become life-threatening. Terminal diseases such as cancer if detected early gives the patient the longest time frame possible to live. Early detection is key in treating many medical ailments or conditions. Data analytics help in spotting these diseases early enough.

10. Personalised treatment: Patients can receive personalised treatment with the help of data analytics. Medications that work for the larger population may not work for a selected few. These few can have medicines made by the pharmaceutical industry targeted at them and this can be very lucrative for the industry. Doctors, using data analytics can prescribe a medication best suited to the patient and not prescribed based on popularity.

Conclusion

A number of health workers express concerns about systems such as data analytics replacing them at work. Although, a robot in China, developed by iFlytek, has successfully passed the country’s licensing medical exam. Liu Qingfeng, chairman of iFlytek, said, “We will officially launch the robot in March 2018. It is not meant to replace doctors. Instead, it is to promote better people-machine cooperation so as to boost efficiency.” In medicine, treating humans requires the human brain. Analytics systems are tools to help the medical team to make better decisions and not to eliminate doctors. Hospitals using data analytics are getting well ahead of their competitors in the industry. Do you want to have a competitive advantage in the health sector? Then get on board with data analytics. It is a formidable tool.

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