A few weeks ago I drafted a LinkedIn post on predictive analytics in healthcare. As I wrote, I realized then that this topic needed to be covered in greater detail than what LinkedIn would allow.

Analytics, after all, has arguably been one of the most significant revolutions across industries. In the last decade, we have seen analytics become a significant part of the healthcare industry as well. Some of the reasons that analytics have made an impact in the healthcare sector could be defined as follows:

• There is more structured and unstructured data available to healthcare institutions owing to the wider adoption of EHR’s and other digital tools
• Technologies such as Machine Learning and Artificial Intelligence have matured and have given us the capability to play more with this data
• There has been increased adoption of Cloud computing infrastructures in the healthcare environment giving these institutions more data analytics capabilities
• Healthcare technologies and systems have become more user-friendly prompting greater adoption
• Leadership has recognized the importance and benefits that data analytics bring have realized that analytics is no longer a ‘good to have’ capability. It is a ‘must have’ to survive in this new world order.

Can data analytics alone help the healthcare sector?

But is data analytics enough to cope with the rising demands of the healthcare ecosystem? Can we be content leveraging analytics to evaluate historical data to gain information about the past event? Is that information of any ‘value’ if it is not put to work to improve the outcomes for tomorrow?

Predictive analytics, for healthcare, can prove to be a silver bullet. It will give doctors and healthcare environments the capability to do what they are doing more effectively and on a much larger scale. It will give these stakeholders of healthcare the capacity to understand relationships between external forces and biology to re-engineer the clinical pathways that we are used to and truly personalize care.

How does predictive analytics do so? Simplistically explained, predictive analytics combines new datasets emerging from the vast ocean of data points, (EHR, behavioral, social, biometric and data generated by the technologies and tools) in use and helps us make sense of it. It will help us make the best decisions, customize care, deliver better patient experiences, reduce costs…basically, efficiency at its best.

This brings us to the next question – “why do hospitals need to adopt predictive analytics today”. This can be answered thus:

Prevent and control chronic diseases

There is a lot of information that lies in EHR’s, our smartphones, smart watches social and behavioral data. Predictive analytics gives the healthcare ecosystem the capability to analyze this data in conjunction with real-time data. This helps healthcare stakeholders identify the health status of each individual.

Chronic conditions such as heart disease, diabetes, etc. can be prevented with better care. Leveraging predictive analytics, we can now identify patients who are ‘at-risk’ of such chronic diseases to enable early interventions and also create personalized care treatments for those who are ‘at-risk’.  By examining the vast sea of data and the several factors that influence predicting future outcomes, predictive analytics helps us identify individuals with high risks of developing chronic conditions and proactively help those patients in avoiding long-term health problems.

Reduce readmission rates

Predictive models can be applied to health systems to identify patients at risk for poor outcomes. These patients are likely to be readmitted to the hospital facilities. By capably identifying these early indicators, doctors can recommend patient-specific interventions that improve patient prognoses. And thereby reduce the readmission rates.

For example, a patient comes in for a hip replacement surgery. What are the chances you can identify that he/she will develop a myocardial infarction later if he/she does not present the apparent symptoms? But if you were leveraging predictive analytics, you could leverage the data at hand and identify that the patient would be at risk before the disease struck by simply analyzing all the relevant patient data and markers. In the same way, you can identify if a person is at-risk of infections, bed sores or any other hospital-acquired conditions.

Predict appointment no-shows and cut down on revenue losses

Patient ‘no-shows’ or absenteeism for scheduled visits and procedures are a huge drain on the doctor’s time. However, this occurrence happens frequently in clinics and hospitals. This often leads to treatment delays, unnecessary wait times and inefficient use of the clinical resources at hand. Quite obviously, these absentees also lead to revenue loss.

By leveraging predictive analytics, hospitals can identify scheduled “no-shows,” and patients who show a higher chance of missing their procedural appointments simply by leveraging electronic health data. The hospital can then double-book this appointment with a patient who is more likely to show up in the slot. By employing this predictive overbooking model over the traditional booking model, hospitals can improve access for existing and potential patients, increase staff productivity and reduce unnecessary spending and revenue losses.

Staff optimization in emergency rooms and critical care centers

Hospital administrators today are under increasing pressure to optimize resources at all levels of staff, reduce wait times in the emergency department and improve patient care, quality and satisfaction levels. Statistics reveal that more than half the hospitals in the US report overcrowding in the emergency department. A delay in attending to a patient in the emergency or critical care centers have many risks associated. Increase in the length of stay, higher mortality rates, medical errors and financial losses are just the tip of the iceberg.

Using predictive analytics, hospitals can solve almost every aspect of emergency and critical care by enabling staff optimization. Predictive analytics can help predict the inflow of patients at a more granular level such as the hour of the day, the day of the week or month of the year or seasonal, weekly or hourly patterns. Leveraging analytics-based forecasts hospital administrators can capably assign the right number of staff members to the right location and capably prevent all these above-mentioned challenges.

Optimize the supply chain

In the current healthcare environment, cutting costs has become a priority. The healthcare supply chain presents itself as the ideal candidate here. By connecting predictive analytics tools to supply chain management, the hospital gains more transparent insights into the value of the products moving through it. Predictive analytics also helps hospitals more accurately determine patient health trends and consequently determine what supplies they will need. This helps the hospitals manage devices and supplies more optimally, efficiently and proactively.

Interestingly, predictive analytics has been an integral part of traditional healthcare and medicine, whether technology-enabled or not. The overabundance of data just gives us the capability to extend this traditional medicine and healthcare approach to go beyond individual experience and guesswork. And by doing so, we make the hospital environment more optimized, efficient and patient-centric.