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We live in an era where big data has made significant improvements to computer systems, ecosystems, companies and mathematical models. Healthcare industries may very well be on the verge of a massive breakthrough through the implementation of machine learning tools.
These tools are driven by advanced algorithms that have the ability to go through incredible amounts of data in no-time (at least compared to human analysis).
This major expected development also applies to the drug and alcohol rehab industry, where costs are high, fall back rates are high and many people can’t afford the current treatment pricing models.
The use of big data can both challenge and improve the industry, as well as predict future trends. The amount of available data is ever-increasing which means that access to unstructured data such as therapist notes, medical prescriptions, reports, lab test results, data of rehab clinics and financial data can be analyzed to improve the industry by a long stretch.
Big data is undoubtedly the way forward to both affordable healthcare as well as greatly improved efficiency of patient treatments.
In this article, I’ll talk about how big data is paving the way for affordable healthcare and how algorithms are incredibly beneficial for both the patients and professionals in the rehab industry.
Predictive Analytics to Advance Rehab Outcomes
The Electronic Health Records (EHR) is a relatively new tool introduced by the U.S. government that aims to improve to quality of health care while at the same time reducing its costs. It’s a database of diagnoses, treatments and how diagnoses were treated.
The volume of the database has shown a rapid growth of patient details, which is also a valuable tool for researchers and such.
The U.S. government has been actively supporting the use of EHR and pumped billions of US dollars into the tool, which is registered in writing in the Health Information Technology for Economic and Clinical Health (HITECH) Act.
The HITECH Act stimulates the move forward of the healthcare industry to big data, both the collection as well as the analysis. The main goal of collecting such vast amounts of data is to share client data by clinicians such as rehab and addiction clinics countrywide in order to decrease costs, speed up diagnoses and subscription based on similar cases and use the overall data to improve patient outcome.
Big data provides the ability to analyze both structured and unstructured data sets that can be used to analyze certain trends in rehab clinics, whether patient conditions showed significant improvement and match treatments with results.
Moreover, the data can be used to address the risk of diseases, what diseases but also the rate of falling back on drug or alcohol use.
The next step is to actively run predictive analytic algorithms through big data sets that enable the early diagnosis of certain patients and perhaps even determine the cause. By doing so, it’s expected that the biggest issue in rehab – fall back rates – can be reduced drastically.
In general, the earlier the correct diagnosis is made, the better patients can be treated and avoid expensive follow ups, complications and other issues. Rehab physicians can easily miss important indications in the early stages.
Finally, machine learning tools have the ability to include a lot more different data points and medical factors than most physicians. Simply put, the algorithm can be improved by adding features to the code and expand its analytic model.
Identify High-Risk Rehab Patients
There’s a large group of repetitive patients and patients who constantly fall back into their old patterns In the drugs and alcohol rehab industry. Obviously, this is an incredibly cost-heavy group and requires special attention.
The use of big data sets can be a great supporting tool in the prediction of certain patterns of the high-risk group, why patient fall back in old patterns, what treatments were most successful and what treatments generally have little effect.
The earlier discussed predictive analysis and early care is often not applied by rehab clinics, which increases the chance of falling back into old patterns. Extensive analysis can even improve patient treatment and have algorithms determine personal-tailored treatment plans.
Whether there’s sufficient data at this point to reach such improve is hard to tell. There’s probably more data required of this group to improve treatment plans going forward. But if the approach to this group is improved, the costs can be decreased significantly which translate into a massive improvement working towards affordable healthcare.
Reducing Staff Costs
Rehab clinics can use big data to recalculate their current business model, treatment cost and other expenses. Analyzing the clinic can have significant impact on the price of treatments, which increases its availability to those who can’t afford the current treatments.
Thus, it’s not only improving the efficiency of the clinic but also opening doors to people who previously didn’t have access to proper healthcare.
Big data can better predict staff allocation based on historical data, what type of patient require more attention or even during what times a year it’s busier in the clinic.
When the services of clinics is better utilized, the patients will benefit greatly from that as well.
Prevent Medical Prescription Mistakes
It can be hard to imagine, however, treatment prescription mistakes are one of the biggest cost-heavy problems for healthcare institutions, including rehab clinics.
There’s always the factor of a human error included during the decision making process when prescribing medication to patients. Patients could be at risk by taking wrong medication which may put them in a disastrous situation with a fatal outcome.
Big data is one of the most important factors in decreasing the numbers of human errors. Analysis of prescriptions can be run in real-time and scan a patient’s history of medical records, the diagnosis and the last prescription. An algorithm has the ability to flag any prescription or diagnosis that raises concern.
This is especially important to rehab therapists and physicians who prescribe and treat a lot of patients throughout the day, which significantly increases the risk of human errors.
Dedicated software flagging questionable prescription and treatment plans would be the way forward.