November 15, 2021

Predictive Analytics Holds Great Promise for Home Care

We all live in an information-driven world where data is considered a priceless resource for many reasons. However, there’s often too much data surrounded which at times make the bigger picture more fragmented than being clear. Predictive analytics can be described as a branch of advanced analytics that helps in making predictions about the, unknown future events or activities that lead to bigger decisions.

Predictive Analytics in Home Care:

To better understand the various possibilities of predictive analytics in homecare, it is first important to acknowledge the different ways through which homecare & healthcare can benefit from this discipline which includes:


Remote-patient monitoring:

Remote patient monitoring systems aim to gather patient data and analyze these details to provide insight into situations where patients might be at risk, need guidance, need to be hospitalized or be readmitted. The predictive analysis technology brings together various patient-driven strategies, cutting down on wait times, and includes targeted therapies to ensure better health outcomes.

Efficiencies for operational management:

Predictive analytics and big data currently play an integral part in homecare organizations’ business intelligence strategies. Real-time reporting can provide timely insights into patient data and can be used to dynamically adjust the predictive algorithms in line with new discoveries, insights. Homecare providers can now have a stress-free work environment where recurring tasks are automated, allowing the care providers to focus on delivering friendly and efficient customer service to the patients.

Increase Productivity and Patient Satisfaction:

Integrating predictive analytics to organizations can create a better work culture that empowers the caregivers to be more productive. This system of algorithms lowers the risk of error and hence, making the entire process hassle-free for the patients, thus increasing customer satisfaction.

Population-based medicine and risk management:

Homecare and healthcare providers are now collecting data from electronic medical records to find connections between disparate diseases or using the information to predict outbreaks, pandemics, and the spread of contagious diseases in the future. This can help governments take appropriate measures to control the outbreak, reduce the number of affected regions and minimize fatality in society.


So, looking into all this, predictive analytics is surely a hot buzzword and is something that most of the industries, including healthcare, home care are implementing.


But Where Are We Now?

As homecare organizations develop more sophisticated predictive analytics capabilities, they are beginning to transform from basic descriptive analytics towards predictive insights but at the same time, home care providers must be aware of risks. Here are a few identified barriers:


As society continues to move into a new era of decision-making supplemented with digital technologies we need to still understand and overcome the risk emerging for predictive analytics including the centralization of data which presents a tremendous risk in terms of security and integrity of the patient data.
Predictive analytics still have a long way to go before being integrated into an enterprise’s security regime. Like all new technology, predictive analytics have hefty system requirements. Coupling predictive analytics with machine-learning can enable cyber security to shed its current unmanageable strategic stage and detect impending threats with confidence.

Lack of Data sharing & consistency in data

Different organizations including hospitals & home health agencies use different programs and software to store data. Not all of these systems are compatible, and therefore there is a lack of data sharing across organizations, which leads to incomplete data sets. Another issue is the data consistency, i.e., data using predictive analytics is fragmented at times and therefore the care providers need to analyze several different types of data to draw conclusions, which can be cumbersome. In the case of data sharing, to overcome this, it is important to have executive leadership set the precedent and to be clear about the purpose and intended data use.


To Conclude With:

The increasing investments on digital tech and the exponential increase in homecare & healthcare database volume, is effectively increasing the adoption of predictive analytics.


“According to the reports, the global healthcare predictive analytics market was valued at USD 3.75B in 2019 and is expected to reach USD 28.8 B by 2027, at a CAGR of 29 %”.

This shows that there is a huge amount of investment flowing into this sector and it won’t be long before we start seeing these kinds of technologies in our daily lives. It’s just the tip of the iceberg that we have seen, and there is still so much potential within predictive analytics that is yet to be explored.

Even though there are a few drawbacks, the intention of this technology is to make the world a better place and therefore, the advantages do outweigh the disadvantages.

Tags digital transformation