In the early days of healthcare, diagnosing and treating patients was a hit-or-miss proposition. Doctors did their best, but they were often working in the dark, relying on their experience and instincts to make decisions about what was wrong with a patient and how to treat them. Can you imagine how difficult being a doctor was centuries ago, in many ways making it up as they went along their careers? It is terrifying to think about what it would be like if medicine was as primitive today as it was back then.
Thankfully, those days are long gone. Thanks to analytics and data, doctors now have a much clearer picture of what’s going on with their patients and what treatments are likely to be effective. We have also seen the healthcare industry go from being reactive to more proactive in how they fight sickness and help patients. On top of being beneficial for patients, data analytics has also proven to be very kind to the healthcare businesses themselves, improving efficiency and costs. Every single department in any hospital can see positive change through the adoption of analytics. It is just a matter of taking the data and running with it.
However, this is a lot easier said than done, and having the right pieces in place is the first step to getting the most out of your data and analytics department. Before getting to the insights and recommendations, it is crucial to make sure your staff has what it needs to reach the conclusion that just might save a patient’s life.
What do we need?
Developing an effective healthcare analytics strategy is part of the necessary foundation we need. Before that, however, some of us may wonder what healthcare analytics even means. Healthcare analytics is the process of turning data into insights that can help improve patient care and drive better clinical and business outcomes. When used effectively, analytics can help healthcare organizations identify and track patterns, trends, and relationships related to the delivery of care. This information can then be used to make better-informed decisions about how to allocate resources, improve quality, and enhance the patient experience.
There is a growing body of evidence that demonstrates the impact of analytics on healthcare outcomes. A recent study by the Harvard Business Review found that hospitals that made greater use of analytics experienced a 3.7% reduction in mortality rates and a 4.5% reduction in length of stay. Another study by the University of Michigan found that healthcare organizations that used analytics saw a 5.8% reduction in readmissions and a 4.2% reduction in costs.
There are many different types of data that can be used for healthcare analytics, including claims, clinical, and patient-generated data. Claims data can be used to identify patterns in the use of services and the cost of care. Clinical data can be used to track outcomes and identify areas for improvement. Patient-generated data can be used to understand the patient experience and identify areas where care can be improved.
Healthcare analytics is a complex process, and there are several different tools and technologies that can be used to support it. Data warehouses, data lakes, and data mining tools can be used to collect and store data. Examples of these tools include Informatica, Microsoft Azure, and SQL. Analytics tools can be used to process and analyze data. R and Python are two coding languages that are great for the analytics portion of the process. Visualization tools can be used to create reports and dashboards that help decision-makers understand the data. These are the fun tools that bring data to life, the most popular being Power BI and Tableau.
The use of analytics in healthcare is still in its early stages, and there is much room for improvement. One challenge is that data is often siloed within different departments and organizations. This is common in organizations, but it can lead to several problems. If similar data is stored in several different places, the odds are that there will be slight differences in the accuracy and quality of that data. The last thing we want is for our data to be unreliable. That is why it is imperative to break down these walls between departments and establish a single source of truth. That way, all your data is coming from one place, and there does not have to be any second-guessing of the information you are receiving.
Another challenge is that many healthcare organizations lack the technical expertise and resources needed to use analytics effectively. Most organizations’ budgets go into the actual healthcare side of the business, not so much the information technology side. A variety of different roles are needed to have a functioning analytics department, including data engineers, data scientists, and analysts. It is also important to have leaders in your organization who know how to successfully apply analytics specifically to the healthcare industry since it can be very different from other industries.
The challenge here is that it is hard to find people with the technical knowledge and the healthcare context to pair it with. Thankfully there are courses out there that can educate you in both areas, such as the Executive MHA program at the University of Ottawa. They specialize in building up your management skills tailored to the healthcare sector, coupled with the technical knowledge necessary to lead analytics efforts. In an ever-changing field like healthcare, it is important to be able to keep up with industry trends and well as technological advances that can improve healthcare for the better.
Despite some of the challenges faced in hospitals today, there is a clear opportunity for healthcare organizations to use analytics to improve outcomes. By investing in the right tools and technologies and building the necessary expertise, healthcare organizations can use analytics to drive better clinical and business outcomes.
How it has already changed lives
To start with, analytics and data have helped doctors to better understand the human body and how it works. In the past, doctors had to rely on their own observations and those of other doctors to try to piece together an understanding of how the body works. Now, thanks to data and analytics, we have a much more complete picture of the human body and how it functions. This has led to better diagnosis and treatment of diseases. Even in cases where doctors have never seen a particular set of symptoms for an illness before, they can access data archives to see if other doctors have seen patients with these symptoms before. The odds are that someone has and has documented everything other doctors need to know in a database. Having access to all this information has been game-changing in how accurately and quickly diagnoses occur.
In addition, analytics and data have helped doctors to develop better treatments for diseases. In the past, treatments were often developed based on trial and error. Doctors would try something and see if it worked. If it did, they would keep doing it. If it didn’t, they would try something else. Now, thanks to data and analytics, doctors can develop treatments that are much more likely to be effective. They can test treatments on large groups of people to see how well they work before trying them on a single patient.
Before computers, it was very hard to keep medical trials and surveys organized. Someone would have to take painstakingly detailed notes and eventually go through all the notes to see if there is any trend or results that they can easily identify. Now it is straightforward to record trial data and pick up on what is and is not working. In turn, this has increased the speed at which treatments are devised. Where in the past it may have taken years to put together effective treatment plans, it is now possible to achieve the same outcomes in mere months.
All of this has led to better healthcare outcomes. In the wake of Covid-19, the world learned just how important it is to be able to identify, research and treat diseases in an expedited manner. The world created the Covid-19 vaccine in less than a year, an unprecedented feat that would not have been possible ten years ago. Thanks to analytics and data, we are living longer and healthier lives.
How data analytics will continue to improve healthcare outcomes
As touched on earlier, healthcare analytics is the application of big data and predictive analytics to the healthcare industry. It is a rapidly growing field that is being used to improve patient outcomes, reduce costs, and increase efficiency in the delivery of healthcare.
There are many ways in which analytics and data will advance healthcare outcomes. One of the most important is population health. Analytics is being used to identify at-risk populations and develop targeted interventions to improve health outcomes. For example, it has been found that type 2 diabetes is more commonly found in Hispanic and African American communities. To combat this, programs have been put in place to improve the quality of school lunches in those areas, as well as to push more healthy food establishments in place of fast-food options.
In the past, healthcare has been largely reactive, only addressing health issues after they have arisen. However, with the advent of predictive analytics, healthcare is becoming more proactive. By analyzing data on past patient outcomes, risk factors, and trends, healthcare providers can now identify potential health problems before they occur. It feels like every day a new study comes out that warns of what habits and foods increase cancer risks (as an example), and this is not a coincidence. With the plethora of data at our fingertips today, we can understand in much greater detail what causes certain health issues. This allows for earlier intervention and prevention of serious health issues.
Another area in which analytics will continue to improve hospitals is in the realm of patient safety. By analyzing data on adverse events and errors, healthcare organizations can identify patterns and implement changes to improve patient safety. If connections are found between certain treatments and negative patient side effects, hospitals can discover that this specific treatment is not safe and discard it promptly. Patient safety is at the very top of every hospital’s priority list, and they can now take safety to a new level by identifying risks before they even occur.
A third way in which analytics has improved healthcare outcomes is in clinical decision-making. With the vast amount of data now available, clinicians can make more informed decisions about diagnosis and treatment. Analytics is being used to develop clinical decision support tools that provide evidence-based recommendations to clinicians at the point of care.
Finally, analytics is also being used to improve the efficiency of healthcare delivery. By analyzing data on resource utilization, workflow, and patient outcomes, healthcare organizations can identify areas of waste and inefficiency. This allows for the implementation of process improvements that can lead to better patient care and lower healthcare costs. A common example of this is staff scheduling. What happens at many hospitals is that more employees are scheduled than needed when the hospital is not busy, and not enough staff members are scheduled when the hospital is slammed. Having either happen too often can lead to many other issues. Through a well-coordinated analytics project, it is possible to optimize a hospital’s staff schedule down to the minute, to always have the optimal number of staff working. You can avoid losing money from overscheduling and ensure you have enough doctors on hand to treat patients efficiently. This is one of many processes that occur in hospitals that can be optimized through the power of analytics.
Healthcare analytics is a rapidly growing field that is having a major impact on the quality of patient care. Analytics is being used in a variety of ways to improve population health, patient safety, clinical decision-making, and the efficiency of healthcare delivery. As the field of healthcare analytics continues to evolve, we can expect even greater improvements in healthcare outcomes.