Improving Patient Satisfaction and Quality of Care Through Big Data

Improving Patient Satisfaction and Quality of Care Through Big Data| HealthSoul

While data has been a buzzword for many years now, the way it works, particularly in healthcare, remains relatively obscure from a public perspective. With strategic implementation, big data can have a transformative impact, both for communities and at the individual level.

It’s all about helping to make decisions faster and more effectively while also maximizing the impact of limited resources. In this article, we take a look at how big data can improve care and overall patient satisfaction.

At the Administrative Level

Many of the most important ways that data can improve patient care will be completely unobserved by the community that is being served. Hospital administrators use data to optimize operations and make the most of their limited resources.

During the height of the pandemic, this meant being able to anticipate surges and to know when to escalate resource implementation on ICU units. Most hospitals are no longer overwhelmed by Covid-19but the American healthcare system continues to struggle.

Now, hospitals all over the country are dangerously short-staffed. Using data, hospital administrators and leadership can figure out where to place people and make schedules that will have the highest possible impact.

They may not have as many people to work with, but through data, they can strategically arrange their personnel so that the community being served won’t notice.

This technology can also be effective from an employee morale perspective. It is very hard to work for any business that is short-staffed. Employees are naturally required to work harder to pick up the slack. That will still be the case even with data, but the numbers alleviate some of that pressure, decreasing the odds of further turnover.

Epidemiology Versus Biostatistics

Much of what we are discussing in this article sounds a lot like the discipline of epidemiology. Epidemiology is a field of study in which entire communities rather than individual people are being treated.

It’s not necessarily a question of improving outcomes for a specific person, but rather about how to make decisions that are best for the largest number of people.

Biostatistics, on the other hand, refers more to the process that epidemiologists use to harvest data, process it, and implement it accordingly.

The distinction is subtle, particularly to those looking in from the outside, but the disciplines are different.

It’s also worth keeping in mind that biostatistics actually can be applied at the individual level to improve patient outcomes.

Wearable Healthcare Technology

Wearables have generated healthcare data for a long time but the invention of IoT (Internet of Things) technology, has advanced the situation considerably. Modern wearables provide up-to-the-second updates that doctors can use to (in certain situations) make life-saving interventions.

Scenario: A patient complains of being fatigued and constantly short of breath. Going up the stairs has been hard lately. They can’t exercise. At work, they feel distant and unproductive. Their doctor gives them an IoT-empowered heart monitor and says that they will keep an eye on things for the next thirty days to find out what is going on.

One week later, the patient is taking a mid-afternoon nap when their heart rate begins to drop. And drop. And drop again. With old technology, chances are pretty good that this is a nap they don’t wake up from. IoT changes that.

The doctor immediately receives a notification of what is going on. So does the company that made the heart monitor. They try to contact the patient and when they can’t an ambulance is called.

Dramatic? Perhaps, but also well within the scope of reality. There are lots of other similar devices. Glucose monitors immediately reach out to emergency contacts when readings get dicey. Blood pressure cuffs that send results directly to the physician.

Data and IoT make the healthcare system significantly less remote. Without this technology, you can really only interact with your doctor by appointment. Often these take weeks to get and when you finally land them they last, what? Five minutes?

With data-powered IoT, patients can get access care during their greatest moments of need.

Fitness Trackers?

Fitness trackers are also a surprisingly valuable source of data, particularly when you can separate myth from fact. For example that 10,000 steps a day thing so many people preach as gospel is, while not quite untrue, fairly arbitrary.

A person who hasn’t been active in the past will improve their fitness by walking or running five miles a day, but the research that says this is the optimal number is basically non-existent.

The data is good for setting and tracking fitness goals and offering your doctor tangible evidence of what your activity levels are like and how they can be improved.

These trackers often monitor other things, like heart rate and blood pressure. Though not as accurate as hospital-grade devices, they can provide good metrics for doctors to pay attention to.

When things are going well, you really only have your blood pressure and heart rate checked once a year. Given how subject to change these things are, that’s no way to get an accurate idea of your health. Fitness trackers provide much more robust data sets.

Big Data Narrows

Big data can be used to improve recommendations and diagnostics at the individual level. For example, there are data-powered diagnostic programs that take symptoms, filter them through databases and algorithms, and provide doctors with a limited number of potential causes.

This, while imperfect, has the potential to save diagnostic time, and get right to the all-important intervention phase.

Data can also be used to make more specific healthcare recommendations by taking a look at what plans have been most effective for people falling into a specific demographic.

Instead of being told to “avoid saturated fats,” doctors can look at a patient’s age, weight, height, and healthcare history, and use big data to make recommendations that have been effective for people that meet these metrics. This could include everything from recommending specific foods, to exercise routines.