One of the biggest revolutions in the last 10 years in the field of medicine has been the advent of healthcare analytics. Leveraging large data pools, machine learning and new diagnostic technologies, practitioners have been able to find innovative ways to improve patient outcomes. Patient data analytics incorporates information from a wide range of fields, including pharmacology, genomics, personnel management and even biometrics. In addition to monitoring patients, organizations are now able to monitor staff members in order to optimize their availability and work efficiency. Let’s take a look at some of the factors driving this revolution.
Patient Data Analytics
Among the hardest things for doctors to do is to hold the whole dataset regarding a single patient in their heads. Computerized systems allow doctors access to massive databases about their patients, and they also permit them to make use of pattern-recognition technologies to get out in front of problems that even a trained physician might not readily see. For example, a team of researchers has developed a method for determining whether a patient is likely to develop thyroid cancer using a system that achieves greater than 90% accuracy in predicting whether a growth is or will become malignant.
Dashboards also permit doctors to readily access both the data and analysis. Where a practitioner 20 years ago might have to wait for a few pages of patient information to be emailed or faxed to them, they can now pull up a full medical history on their tablet. If the doctor needs to dig deeper, they can see information about medications the patient is on, how the person was diagnosed during previous visits and even the times of the year when the individual has had the most trouble. This can make it much easier to spot patterns that lead to a diagnosis.
Collecting and Analyzing Big Data
While we tend to think of big data as a 21st Century innovation, the reality is that researchers have been collecting the data to do hard science much longer. Famous ongoing data collections efforts like the Framingham Heart Study have been active since the middle of the previous century. What has changed, though, is that we now have patient data analytics packages that can compress massive amounts of data into a useful work product in a matter of minutes, hours or days.
This sort of work at scale can also provide early warnings. CDC data is used to produce daily maps and charts showing the progress of flu season each year. Rather than trying to make a best guess based on rumors and news reports, hospital administrators can figure out when to begin stockpiling for flu season by referring to hard data.
While it’s easy to think the best way to improve patient outcomes is to focus on direct treatment options, there’s also a lot to be said for using healthcare analytics to revolutionize medical facilities as organizations. A good analytics package can help an administration decide:
- When to ramp up or cut hours
- How to economize inventories of critical drugs and supplies
- What forms of continuing education to encourage staff members to pursue
- How to get patients to heed the advice of doctors and nurses
Except for small clinics and general practices, most healthcare organizations are fairly large operations. They have to deal with the same problems other corporate entities do, such as taking shipments, ordering new materials and scheduling workers. Analytics can even tell a hospital whether the price-to-performance expectation of doing something like adding a new wing will be worthwhile.
Assessing whether you’re satisfying patients can be a major challenge. Unsatisfied patients may never complain directly to you, instead taking their business to other facilities and expressing their anger on social media. People don’t return questionnaires in great numbers, making it difficult to be proactive even when you strongly want to do so.
Data mining and analytics allow modern organizations to monitor many sources of information regarding both quality control and patient satisfaction. Social media mentions, for example, can be broken down into positive, negative and neutral sentiments. An organization can monitor mentions of its name and the names of its staff members. Machine learning is now so advanced that error correction can be used to identify social media mentions that get the spelling of a doctor’s name wrong.
Information can be compared and contrasted, too. A hospital might compare its analytics regarding staffing against social media sentiment. This can help administrators draw a line between what does and doesn’t drive patient satisfaction. Solutions can then be devised based on what has worked best to make patients happy, and it can all be done without the difficulties associated with satisfaction surveys.
Adopting and applying healthcare analytics, though, demands a cultural change. In particular, an organization must forge a culture of following the data where it leads. If patient satisfaction metrics indicate that a hospital is falling short in key areas, there still has to be a responsive culture present within the administration. With time, however, an organization can develop both the infrastructure and culture required to make better decisions for its patients based on hard data.
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