How Artificial Intelligence is going to disrupt Home Health Care?
Nowadays, there is a lot of buzz surrounding data science. We hear lots of fancy terms like Big Data, data analytics, machine learning, AI, and so on. We are living in an age where these technologies have already started revolutionizing our lives, our workplaces, public service sectors, our homes, our cars, factories, and whatnot. Data science has allowed industries to run with zero or minimum human intervention, increased accuracy as well as precision, and with greater speed and efficiency. But perhaps, the biggest potential of data science lies in the health care industry.
Thousands of fatalities occur every year due to late diagnosis, misdiagnosis, medication or prescription errors, incorrect dosage, mistakes in taking medication or in aftercare, and so on. If data science is applied to healthcare, and especially home health care, all these issues can be effectively handled. Moreover, hospital admission and care are extremely costly and often inaccessible. Home health care powered by data science can reduce the instances of hospital care manifold. With recent advancements in medicine, many statistics will show that the accuracy of diagnosis has increased. However, errors due to lead-time bias, length bias, overdiagnosis, etc. can often lead to bloated, misinterpreted numbers. Present detection techniques are just not powerful enough to characterize each cancer accurately based on diagnosis and prognosis. Neither can they predict the possibility of malignancy at a later stage. This is exactly what machine learning (ML), especially deep learning, is set to change.
Home health care encompasses all those health care services that are availed by patients at home. A person might want to receive home health care for the following reasons:
- Aftercare following hospital treatment
- Less serious illnesses or injuries that do not require hospital admission
- A chronic condition with non-debilitating symptoms that require regular health care
- Regular checkups for those with a tendency or potential to develop a disease or disorder
- Diet and exercise regulation
- A disability or debility
- Geriatric care
These are but a few cases; there are many other reasons one might want to get home care for.The most common services you get under home health care are:
- Monitoring of vital signs like heart rate, blood pressure, breathing rate, temperature, blood sugar, etc.
- Administration of injections
- Recuperative care
- Home births
- Timely medication administration
- Caregiving for the elderly
- Nannies for newborns
And much more. In fact, there are registered agencies that you can call for home care services. These home health care agencies ratify and employ home caregivers whom you can hire for specific periods for a given price.
Home health care has a much bigger consumer base than hospital care. Hospitals themselves are running on AI. This only goes on to show how much more important data science, especially Big Data and AI is for the home care industry. The procurement of data, its analysis, and organization using AI will go a long way in helping people easily utilize the treasure trove of data to provide for targeted help to people. Just consider the following use cases.
A big chunk of people does not even use home health care to its full potential because of ignorance. They do not even know that the problem they are facing can be solved with home health care. Instead, they rush to the hospital for the most insignificant reasons. With the help of Big Data and AI, the customer base can be correctly identified and segmented. The Marketing Mix Modeling employs existing marketing and sales data to identify the best marketing and promotional tactics for each segment in terms of reach, effect, and ROI. Employing this, more people will be educated about the benefits of home health care. More people will be able to reap its benefits.
It cannot be denied that healthcare is not equally affordable for all. People in different economical echelons can afford different levels of healthcare. The same applies to home health care too. Pricing the services equally for all is not a viable solution; they must be priced equitably. By using data science, customer segments based on income can be created, and accordingly, various healthcare services can be priced in a stratified manner to make them accessible and affordable to all.
One of the biggest advantages of having data science on the side of home health care is to make the sector more organized and fluid in its operations. Data science can be used to coordinate and adjust the working times and schedules of caregivers without any clashes so that no one in need of care has to wait or be neglected. Moreover, it allows the performance of the caregivers to be monitored down to the smallest detail. This allows schedule-makers to arrange services to optimize the performance of the caregivers and improve the quality of service provided.
Most people nowadays use wearables like fitness trackers and quick checking devices like blood glucose monitors to keep track of their health values. Their only utility does not lie only in informing you if your levels are healthy or not. These devices can also be connected to an app or network monitored by home care institutions for real-time monitoring. These people are far more qualified to diagnose an oncoming condition or emergency from the values. They can alert you about whether you need to take a drug, visit the hospital, change your diet or exercise, increase or reduce the dosage of a medicine, go for a checkup, and so on.
One of the pros of data science lies in its ability to analyze an immense amount of data and use it to predict the reality of a situation. A doctor may not be able to notice small changes in values or may not even think about doing a certain check or test. But these data, if available all the time to an AI-powered application, can yield vital information regarding the medical condition of a patient. It can be used to predict if a medical emergency is oncoming and accordingly, they can be asked to admit themselves to a hospital. Timely treatment thus obtained can save millions of lives.
One of the biggest mistakes people make after being discharged from the hospital is to ignore the after-treatment care. They neglect medications and diets, give slack with the physical therapy, put unnecessary stress on the body, ignore hygiene, etc. As a result, often, there is a relapse or other problem that requires readmission. Data science, especially AI, can be used to remind the patient of these aftercare needs, monitor their recuperation, and alert of any unexpected changes that they can take precautions against. This largely reduces the instances requiring readmission into the hospital.
If data science is applied to home health care, it will require access to the requisite data needed to make possible its functionality. Nowadays, this has become quite easy with the deluge of sources of medical data. The most common way of accessing medical data is through past medical records of people. Real-time data is also accessible from health-monitoring devices, especially wearables and instant checking devices. In the age of IoT, the browsing history of people can also be analyzed to give a clue into what symptoms they may be having. Another step ahead would be using even external data like the weather, pollution levels, freshness of food, purity of water, etc. to analyze the cause of a medical symptom.
This remains the primary cause of caution in all systems powered by data science. Data privacy is a big concern, especially when it comes to health data. Medical records and information of a person can be exploited in several ways to harm and use the person for evil causes. It can even become a matter of life-and-death. If a malicious person skews the medical data of a patient in the system, it could endanger their life. Until security systems are made robust, reliable, and impenetrable, data science in home health care will remain a matter of concern.
Data science has already started changing the way most industries operate. Home health care is no different. In fact, data science is already being used in health care to help make diagnoses, prognoses, treatment, and cure easier and better. Data science in home health care can start with extending a part of its applications in general health care to it. After that, the systems can be improved and augmented to include every other section that may or may not be covered under general health care. The future is bright for data science-driven home care and it is advancing every minute.
Strategic Intern- NeenOpal Analytics @Omkar Shindekar
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