Predictive Analytics: The secret to boosting end-of-life care quality

Abhinav Shashank
Fri 11 Oct 2019
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Struggling with chronic life-limiting illness results in extreme pain, discomfort, and physical as well as mental stress. With no permanent cure available to treat the disease, palliative care can still make a significant difference in enhancing end-of-life care quality by alleviating pain and managing uncomfortable symptoms. 

Regardless of the benefits, most people receive palliative care when their disease has escalated to the last stage or never receive it at all. But, what can evolving healthcare do for a more data-driven, intelligent way to help patients enhance the quality of their life?  

Current Status of Palliative Care

According to Becker’s Hospital Review, palliative medicine is one of the fastest-growing medical specialties in the US, aiming to improve the quality of life for over 90 million patients in the US. 

On average, patients who received palliative care did not survive beyond 38 days, and those who were referred to hospice care died within 20 days. This is extremely alarming since 75% of the healthcare budget is being spent on treating patients with chronic diseases. 

The Challenges In Extending Palliative Care

The timing of care is the factor of higher importance in regard to palliative care. Poor outcomes of palliative care can be majorly attributed to inaccurate scheduling of care. This is particularly a serious problem because ascertaining how long a terminally ill patient will live is very difficult. 

Doctors have all of the resources to make an informed decision, but it is unrealistic to expect them to make an accurate estimate of a patient’s life expectancy. Sometimes they can be correct, but other times, their estimates can widely digress from the actual result

As a result, the patients are either assigned palliative consultation before it’s due, or when it is too late.

Is Deep Learning and Predictive Analytics the Answer?

There are some essential ways in which palliative care can be improved. First, AI It could also be instrumental in identifying the right patients. It can also be applied for prognosis, which could yield an increased amount of accuracy in determining the time needed for palliative care. 

Monitoring tools along with insights based on real-time and predictive analytics can give providers an accurate description of a patient’s current health, and empower patients and their caregivers with objective data to help them make end-of-life care decisions.

Also, implementing a deep learning algorithm on the EHR database can estimate the life expectancy of patients. This machine learning technique can filter the data, and its neural networks can predict how long terminally ill patients are expected to live and who all should be assigned to palliative care. 

Similarly, it could be leveraged to help patients receive an appropriate level of care, allowing earlier discharges and reducing their overall length of stay. Helping to ensure a patient’s care is managed and their pain is minimized, is also a substantial way of increasing patient satisfaction.  

Diffusing The Mental And Financial Agony Of The Patient’s Family

Family members of a terminally ill patient deal with a lot of uncertainty regarding the cost of the treatment. Even those with insurance have to worry about out of pocket expenses, especially now that the percentage of deductibles is constantly rising.

With the AI determined estimates, they can prepare for the patient’s end-of-life costs and treatments in advance and honor their dying wishes. 80% of Americans wish to spend their last days in their homes, but studies reveal that 60% of people actually die in acute care hospitals.

Future Of Palliative Care With Ai-assisted Prognosis

The current priority pf palliative care is not ‘how’, but it is ‘when’ to extend the care. In the quest to improve accurate estimates of life expectancy of the terminally ill, leveraging the power of machine learning will be instrumental. It will be able to help patients in making sure that the decisions in their final days are aligned with the maximized quality of care. It’s about adding life to the days! 

To know more about how a data activation platform can help you in enhancing palliative care, get a demo.

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Tags: Analytics
Abhinav Shashank
Predictive Analytics: The secret to boosting end-of-life care quality

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