Predictive analytics is staking a claim in the global healthcare market, surging to $1.8 billion in 2017 and expected to climb to $8.46 billion by 2025, according to Allied Market Research.

As artificial intelligence and machine learning developments accelerate and impact the evolution of business intelligence applications, healthcare organizations are more inspired to leverage the advantage of predictive tools that help to streamline productivity, improve patient care, reduce costs, and increase the bottom line.

Applied artificial intelligence (AI) is making an impact on a variety of fronts: from risk assessment, cutting costs, boosting efficiency, enhancing patient satisfaction to managing healthcare staff and improving operations.

The transformation is being seen at top-level offices as well. Healthcare executives are opting for more complex and far-reaching big data analytics. Seeking insights that point to more refined and precise decision-making outcomes, more C-Suite execs are trading their traditional descriptive analytics for analytics that offers predictive insights for all departments throughout the organization. They now recognize that digital technology can be a compelling tool that facilitates value-based care, allowing them to deliver improved patient outcomes while reducing expenditures.

But predictive analytics can bolster the performance of employees at every level of the organization. They allow people to make more informed decisions more quickly and precisely at those times when small differentials can have a big impact. Clinicians, financial staff, and administrative team members can all benefit from alerts that inform them in advance that certain events are likely or imminent.

When timing is a factor, predictive analytics can bring meaningful benefit to the patient as well as the organization. Short response time can make the difference between life or death or be a determining factor for a candidate for clinical trials can be found in time for treatment.

Reducing Spending & Enhancing Outcomes

Risk assessment

Identifying, classifying, and managing high-risk patients “is central to improving quality and cost outcomes,” according to the Association of American Medical Colleges (AAMC).

Together, prediction and prevention can help to reduce spending and enhance health care outcomes. Predictive tools can be used to help companies pinpoint those patients who are at higher risk of developing chronic conditions or experiencing poor health outcomes, and, as a result, help those patients avoid long-term health problems and complications. The patients’ health benefits and costs can be significantly reduced.

By developing risk scores, healthcare organizations can begin to obtain information to help them determine which patients might benefit from receiving certain services or participating in certain activities. Risk scores might be based on data from a variety of sources, including lab tests, biometrics, claims, and patient health data. In the process, more effective treatment solutions can be implemented.

Reduce readmissions

Predictive analytics can also be used to alert providers when certain risk factors reach a threshold, indicating a high probability for readmission within a 30-day window. When informed about the patients who have a higher likelihood of being readmitted within a short time, providers are better equipped to develop follow-up protocols and to design discharge procedures to counteract the likelihood of those readmissions.

A study from the University of Texas Southwestern in 2016 showed that certain occurrences during a hospital stay significantly increased the likelihood of readmission within thirty days. Certain infections, unstable vital signs, and longer duration of stay seemed to correlate directly with rates of readmission.

One index, The LACE Index, takes into account several health factors, including the length of stay, co-occurring diseases, and visits to the emergency room to recognize patients who might be at risk of readmission or death within 30 days of the date of discharge from a hospital. The data helps providers make better treatment decisions for patients, more accurately predict the course of their health, and reduce mortality rates.

Medicare’s Hospital Readmissions Reduction Program (HRRP) further adds a financial consideration to motivate organizations to put the analytics in place. Under HRRP, hospitals and healthcare organizations can reap substantial penalties for excessive numbers of admissions. With the help of predictive analytics, providers reduce unexpected readmissions and avoid the resulting financial hit.

Predicting and responding to patient decline

Once a patient is admitted to a hospital, additional threats to their health abound, which can increase risks to the patient and add costs to treatment.

Hospital-acquired conditions (HAC) are also of concern to healthcare organizations, adding to both risk and costs of treatment of their patients. According to IBM Watson Health, HAC increases costs overall by $2 billion—or about $41,000 per patient.

Numerous conditions can derail patient improvement, including sepsis, pressure ulcers, hard-to-treat infections, and kidney injury. Patients sometimes simply respond poorly to their treatment.

The precision of predictive analytics and machine learning applications assist providers in predicting trends and clinical events more accurately. Providers can more carefully monitor vital patient metrics, enabling them to ascertain downturns in patient conditions and respond with appropriate treatment more quickly.

According to a 2017 study, a predictive analytics tool at the University of Pennsylvania helped to predict imminent cases of severe sepsis or septic shock 12 hours before the condition was initiated.

Another study by the AMIA reported that by when predictive analytics and clinical decision support tools were combined into one solution, sepsis mortality was reduced by 53% and the 30-day readmission rate was reduced from 19% to 13%.

Improving Operational Efficiency

Better response to patient flow patterns

Predictive analytics can also be used to help streamline procedures for scheduling patients. With more flexibility and more accurate response to demand, operations are more efficient and precise. Predicting access, usage, and visitation patterns can help reduce patient wait times, reduce overflow in waiting rooms, and, as a result, improve patient satisfaction.

Urgent care and emergency centers must accommodate fluctuations in staffing that are a result of variations in patient populations. Inpatient services need to be able to have beds available for new patients as needed, while physician offices and outpatient clinics want to minimize patient wait times.

By analyzing utilization rates, Predictive analytics tools can help care centers recognize the inevitable ups and downs of capacity and suggest optimal allocation of time and resources for a selection of doctors and a variety of treatments types.

Resources were stressed at times, and at other times, staff members had few patients to care for and little work to do at Wake Forest Baptist Health in North Carolina, according to Karen Craver, Clinical Practice Administrator. By implementing analytics, the center was able to “flatten out the bell curve” and even out the workflow and increase patient satisfaction.

Reducing no-shows while improving patient satisfaction

No-shows can be a significant drain on healthcare provider resources and operating efficiency. Again, predictive analytics can help to ameliorate the problem, improve outcomes, and reduce costs. Applications can identify patients who are most likely to miss a scheduled appointment without giving notice and offer available appointment times to other patients. Previously unavoidable revenue losses are avoided, and more patients are served effectively.

According to a study from Duke University, patient health data offers the necessary insights to identify patients who are most likely miss appointments. Their predictive models identified 4800 more patient no-shows per year than previous models. Providers can also use the same data to send appointment reminders to patients, including alternate dates and times that the patient can choose from, thereby reducing the likelihood of a skipped appointment.

A pilot program at Boston-based Brigham and Women’s Hospital reduced no-show rates for colonoscopies by 30% using patient data. The organization texted how-to-prepare guides, appointment reminders, and helpful links to their patients who had approaching appointments. A secondary outcome: patients reported feeling better prepared for the appointment.

That’s a significant ROI,” says Adam Landman, vice president and CIO of Brigham. “We have some very encouraging results, and we’re starting to expand that tool to other procedural areas and use cases. This is an example of where it’s meeting a real need, and there are some palpable ROI that resonates with our CFOs that lets us then build this platform out.”

Streamlining the supply chain

Predictive tools can also be valuable to help reduce costs and improve efficiency in the supply chain. Currently, only 17% of hospitals implement automated or data-driven solutions to manage their supply chains, yet

hospital executives are increasingly putting them in service to reduce variation and obtain more actionable insights into ordering patterns and supply utilization.

A survey by Navigant shows that using analytics tools could save hospitals as much as $10 million per year if they’re put in place to monitor the supply chain and to help make data-driven, proactive spending decisions. They can optimize several aspects of the process, from negotiating pricing and reducing supply variations to optimizing the ordering process.

Data Security

Analytics tools that monitor data access, sharing, and utilization can signal intruder alerts and other changes in the data systems that managers need to be aware of. Predictive tools can both evaluate any real-time risks for transactions as well as respond to them based on the results of the evaluation. Once a real-time risk assessment is completed, the system can offer the appropriate option: either granting access, requiring further authentication or even blocking access if necessary based on the data.

What does the future hold? We expect to see applications and solutions merge within a seamless platform, providing unparalleled effectiveness of care, acutely responsive and customized treatments, and highly efficient systems that cut costs and maximize profitability. With benefits seen and felt by both patients and providers, the healthcare tools of the future will combine clinical data, patient-supplied outcomes, clinical decisions, and analytics that streamline operations and provide a more successful patient experience.