The need for efficiency – can predictive care help home healthcare providers?

The home healthcare space in the United States is evolving fast. While things were more or less consistent for decades, today they’re changing. This is primarily because of the changes the Centers for Medicare & Medicaid Services (CMS) has announced over the past few months. These are quite significant and are impacting everything ranging from the delivery of care to administrative activities.

The new policies and payment models are making home health care providers reevaluate their current processes. For home health care providers, mainly small and medium-sized businesses, there is an urgent need to increase efficiency and streamline activities to stay competitive in the evolving home healthcare environment.

One of the key ways of doing this is by moving beyond reactive care and focusing on predictive care. Predictive algorithms and deep learning, can increase the likelihood of diagnosis and prognosis when it comes to diseases and help the provision of preventive care before the issue escalates.

By employing a more data-driven, intelligent approach, home healthcare providers can not only help enhance the quality of life of the seniors but also increase operational efficiency.

Home healthcare industry – what’s happening today?

The short answer is, a lot. According to the US Census Bureau, we will reach a demographic turning point in U.S. history very soon. By 2030, it is estimated that every 1 in 5 residents in the country will be a senior.

The growth in the baby boomer population and the need for home healthcare are directly proportional. However, in reality, the number of caregivers isn’t rising as fast as the older population. This means that there will be caregiver shortages in the near future.

In addition to these changes in the population demographics, there are quite a few amendments being made to insurance and payment models in the US.

As per the National Academy of Medicine, in the United States one-third of the health care system’s resources, which amount to a staggering $750 billion annually, are spent on unnecessary services and inefficient care. This is why CMS is looking to enhance the provision of better preventive care, reduce inefficiencies, and decrease avoidable emergency room utilization.

Just recently, the organisation reviewed Medicare Advantage plans and added a range of supplemental benefits which can be availed starting next year. While this is a great news for home healthcare providers, the changes made to payment models for Medicare have been met with mixed emotions.

 The Patient-Driven Groupings Model (PDGM), a major payment system overhaul, will be in effect in less than three months. PDGM features a 432-point case-mix model and sets to reduce the traditional 60-day billing period to 30 days.

The key challenge with PDGM is that it ties reimbursements to patient characteristics rather than the volume of services provided. This means that critical core competencies drive reimbursements and losing focus on those mean a potential loss of revenue.

What’s the impact of all this on home healthcare providers?

 Growing senior population means that home health care providers have to find a cost effective way of dealing with the imminent caregiver shortages.

However, that is a more long-term concern. The biggest short-term issue is to ensure that by Jan 01, 2020, their organisation is ready for PDGM. As reimbursements were previously based on volume, it was easier to compensate for operational inefficiencies and admin overheads.

 However, as home health care providers can’t bill for ‘volume’ of care anymore, they will be compelled to revisit their operational dynamics. Additionally, there is the added stress that the revenue cycle management has been cut into half. This means that claims have to be processed sooner and cash flow has to be managed accordingly.

Why is focus on deep learning models and predictive analytics important?

As the industry moves towards value based and patient centric care, home healthcare companies have to evolve.

Predictive analytics tools can help. They can reduce inefficiencies and improve care, by enabling providers to proactively tailor the care provided to seniors according to their individual requirements.

A systematic approach to delivering value, based on technology can give health care teams the information they need to make informed decisions and provide the ‘right’ care to seniors. Data is at the heart of this approach. Digital systems such as electronic health records (EHR), remote patient monitoring devices, and smart home sensors provide a wealth of data.

This means that home healthcare providers have an unprecedented opportunity to derive correlations and meaningful insights that can improve resource allocation and consequently, patient care.

 Deep learning is a good point to start from. Algorithms analyse data passing through a multi-layered model. Each layer builds on the outputs generated from the previous one and the model becomes more accurate. This means that eventually trained deep learning models can not only make connections but also predict future events and identify challenges beforehand.

Predictive analytics algorithms can put the staff in a better position to plan and deliver care to the patients. For instance, the insights generated can be placed in easy to understand personalized dashboards to highlight the spike in glucose levels of a senior living with diabetes or issue an alert if another senior has fallen inside his home.

This is what LocateMotion sets out to do. Our ‘Sensights’ platform, uses algorithms to track health and activity trends as well as patterns in movement to reduce chances of emergencies such as wandering and falls, while automating routine daily support tasks such as medication reminders. Depending on the severity of each care incident, it facilitates automated or human responses, improving operational efficiencies for homecare providers.

Is predictive analytics really needed?

The biggest concern home healthcare providers have is that traditional caregiver and senior relationships can be impacted when technology enters the mix. Many believe that the human touch is lost somewhere between wearables, data entry, and deep learning.

While this is a valid concern, it is important to realize that predictive analytics is not set to replace caregivers. It is there to provide assistance to home healthcare providers so that they can use their ‘finite’ resources effectively and prioritise situations that require immediate attention.

Another issue is data privacy. The move to data analytics means increased privacy and data security concerns. As personal health data has to be obtained for multiple sources including hospitals, many home healthcare providers find it hard to get access to this information.

Using wearables and sensors is quite helpful but for predictive analytics to work effectively, historic personal data is important too. Unless, data sharing is made easier and there are stronger legal frameworks and standards in place, this will remain a key concern.

What’s next?

Predictive analytics is a key element when it comes to the future of home health care. According to research, predictive analytics is increasingly being adopted across the healthcare industry. In 2019, predictive analytics use (60%) jumped with a significant 13-point year-over-year increase from 2018 (47%).

However, to fully realize the benefits analytical tools and deep learning have to offer, home healthcare providers need to devise a structured and thoughtful approach, involving the right technology partners, the right people, and the right training.

It is easy to see how the caregivers would benefit from predictive analytics. They would have the right information to help them take appropriate steps to ensure that seniors remain healthy and safe. Also, this means that home healthcare providers can transition to the new payment models without worrying about revenue decline.

Another benefit that’s not so obvious is that home care providers with predictive analytics capabilities will have a major advantage in the referral marketplace. This is because this is the direction in which the industry is moving.