Predictive analytics in healthcare is fast becoming one of the most important innovations with regard to digital transformation for providers. What is it, why are providers implementing it so rapidly, and why should you care?
Predictive analytics in healthcare might just be one of the biggest things to happen to providers this century.
Take a look at some of most revealing industry stats for predictive analytics in healthcare:
- North America big data analytics in healthcare market size was valued at $9.36 billion in 2017, and is projected to reach $34.16 billion by 2025, growing at a CAGR of 17.7% from 2018 to 2025.
- 82% of respondents in a CWC survey indicated that the top benefit of analytics implementation was improved patient care.
- According to a study by the Society of Actuaries, 93% of health organizations say predictive analytics is important to the future of their business.
It’s clear that there is a future for the use of predictive analytics in healthcare, just as there is in other industries, manufacturing being one of the best examples.
Today, we’ll be taking a look at how predictive analytics came to be such an important aspect in healthcare, its benefits, concerns, and what the future looks like.
What Is Predictive Analytics?
Predictive analysis effectively tells you what’s likely to happen and allows you the opportunity to understand how you’ll be affected in the future.
It will take your data and then use a combination of algorithms and machine learning to establish correlations and likely outcomes.
In healthcare, this kind of forecasting will help you better understand the needs of patients, and from an administration standpoint, gives you insight into admission rates, bed shortages, and many other issues that can then be dealt with more successfully than before.
And this is one fundamental point: modern analytics use is really not so different from what doctors and managers have been doing for years anyway—only now they have access to real-time data that is compiled automatically rather than by hand because of advancements in the technology at our disposal.
It’s worth noting however that predictive analysis is highly dependent on the volume of data sets provided—it can only give back what it receives, and it is an estimate, not a prophecy, so bear that in mind.
How Does Predictive Analysis Work?
In short, predictive analysis works by assessing past data to determine what the future will look like—provided there are no unforeseen changes.
Predictive analysis isn’t a one-and-done system; it requires input from stakeholders and key decision makers in order for it to be effective.
Firstly, businesses should know precisely what they want to use predictive analysis for. Is it to determine when uptake for a program is strongest so you can raise awareness more effectively to patients during particular period? Or is it to get a better understanding of when demand for supplies is highest so you can prepare in advance? The uses of predictive analytics are dependent on specific organizational goals.
Once you know exactly what you’re looking for, you can ask yourself whether you have the necessary data for analysis that can then inform you decision making. Have you been logging the data long enough to be able to recognize patterns in a useful way? Are you logging data at all, and if not, how can you get a procedure in place that will?
Now you’ve answered these questions, you can begin building your analytics model and training your ERP system to aggregate and analyze the data you’re providing for a particular task.
When the data is assessed and insights have been provided, stakeholders can then use that actionable data to make decisions that have positive effects on the providers outcomes.
In effect, predictive analytics isn’t too far away from what decision makers have already been doing for years by assessing their records—only now we have the ability to feed that information into a computer which can far more effectively and quickly analyze large data sets than a human worker ever could.
Benefits of Predictive Analytics
When we talk about improving efficiencies within organizations, business intelligence (BI) is often one of the biggest assets a company can have.
BI is often deployed by them as a means to move away from risky decisions made on gut-feeling, and instead seeks to utilize existing data for analytics and actionable data for more informed decision making.
With successful organizations, only 40% base their decisions on gut feeling. For less successful enterprises, this number jumps to 70%.
As regards its benefits for a healthcare provider, predictive analytics can be used to determine operational inadequacies that would otherwise have been missed.
For example, you can receive real-time data on which wards may need more support, allowing you to make that decision quickly, improving the delivery of care.
This is just one small example, but with aging populations across the Western world, the administration of overstretched providers will become a key factor in the near future.
Having the tools to analyze patterns in patient and staff behavior allows providers to cut down on inefficiencies and distribute their savings (money and labor) to where they need to go.
Accuracy in diagnosis and preventative care
Predictive analytics uses algorithms to help physicians make more accurate diagnoses of their patients to help solve issues before they arise.
This is done by analyzing data sets from hundreds, even thousands, of patients to gain a greater understanding of the patient journey.
This helps give an indication of any issues they might have for diagnosis purposes, and then further allows doctors to better understand how well a patient is responding to treatment.
Using analytics in this way means healthcare providers can intervene earlier and facilitate patient journeys more quickly, more accurately, and with an increased likelihood of a better outcome.
Concerns of Predictive Analytics
Ethical concerns regarding the use and misuse of data by businesses shouldn’t come as much of a shock to decision makers.
The increasing amount of data that is harvested by companies and the number of consumers who are cautious about it has meant that extra care must be taken by organizations handling large data sets.
Research suggests that 70% of consumers would stop doing business with a company if it didn’t adequately protect their data. Just 27% feel that businesses take their data security seriously
Many of these concerns have arisen from the sharp escalation of cyberattacks over the last few years, coupled with some disconcerting realities about preparedness; for example 71% of small and midsized organizations say they’re not prepared for cybersecurity risks.
For healthcare providers, the stakes are extremely high, and compliance with acts like HIPAA requires organizations to have a watertight system for how they handle and protect data.
Providers must have the appropriate precautions in place for when they handle large amounts of data with predictive analytics, and patients must be in no doubt that their information is being shared securely and properly when used for analytical purposes.
One of the enduring issues of the use of predictive analytics (or any AI tech for that matter) is the amount of deference that can be afforded to it and its role in the traditional decision making process that physicians undertake.
For example, there may be significant legal ramifications if a doctor follows a predictive analytics model that is faulty or incorrect.
For these reasons, predictive analytics must not be seen by healthcare providers as a means to replace doctors in any way, but rather to its fullest use it should act as a supplementary tool for them.
Physicians will still have to document their decision making process, taking into account predictive analysis and then making an independent decision.
Providers shouldn’t feel as though predictive analytics is a hurdle, but rather as model for technology that functions in a capacity of assistance.
At the end of the day, humans will have to continue to make decisions using their best judgement.
In any event, taking patient views into account quashes the notion that advanced tech will replace doctors anytime soon—only 50% would be willing to trust an AI nurse or physician with diagnoses, treatment decisions, or other direct patient care tasks.
Future of Predictive Analytics In Healthcare
So far, it appears the benefits of using predictive analytics in healthcare outweigh any current concerns, and healthcare providers agree, with organizations pouring more money than ever into AI, machine learning, and analytics technologies.
More than one-third of provider executives said they were investing in AI, machine learning, and predictive analytics going into 2018, PwC found.
As the technologies mature and data sets that can be used by providers continue to grow, predictive analytics will become a hugely important factor to consider when treating patients.
This will be a certainty in the future; for now providers should be sure that they have the volume of data sets necessary to meet their ambitions—In 2018 Infosys found that half of respondents in a conducted survey felt their data was not ready.
Nevertheless, as patients become more accustomed and comfortable with the use of advanced technology in hospitals, the incentive and necessity to utilize it by providers will not be hampered by patient pushback.
Providers should also think about the ethical considerations—primarily with regard to privacy and the extent to which technology is present in the decision making process—and whether at present they have the means to fully secure data and ensure comprehensive compliance with HIPAA and other standards.
It does appear, however, that predictive analytics in healthcare is a fast-growing and unabated phenomenon within the industry, and something of an inevitability, even for small providers.
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