Healthcare Analytics and Hospital Efficiency

The amount of data produced in healthcare settings has grown exponentially, as hospitals around the world have integrated new connected devices, software solutions and analytics tools into their operations. According to the World Economic Forum, hospitals worldwide produce 50 petabytes—10 to the 15th power bytes—of data every year.¹

Some of this data comes in the form of patient electronic medical records (EMRs), but it also comes from the hospital itself in the form of operational data. This includes admission periods, types of diseases treated, device usage, drug usage, financial data, imaging data and much more. However, it isn’t the collection of data itself that is important for improving hospital efficiency; it’s what hospitals and medical companies do with that data.

This has become increasingly clear as the world continues to battle the global pandemic. Now, more than ever, viable data that can be leveraged to achieve discernible patient outcomes is a priority in hospital settings, as are the analysts who can drive such progress.

Making Hospital Data More Viable

The first step in leveraging hospital data is making it more viable. Currently, hospitals are collecting data, but too much of it goes unused. For example, about 90 percent of hospital data comes from medical imaging technology, but as much as 97 percent of it goes untapped.²

To address this challenge, hospitals must ensure their data is structured and usable. This means ensuring it is reliable, actionable and validated.

Reliable Data

The reliability of data relates to its availability. That is, data must not be siloed in legacy computing systems where it can’t be leveraged by healthcare analytics tools and emerging technology like artificial intelligence and machine learning.

Analysts must have reliable sources of data to run calculations and draw insights. This generally means creating a single source of truth for all healthcare data analytics, in which data from disparate sources can be funneled, sorted, analyzed and executed upon. Usually, this is accomplished with software, but it also requires the keen minds of data analysts.

Actionable Data

Actionable data is that which is useful in decision-making. Typically, actionable data has gone through analytics and data processing so that it can be presented understandably to decision-makers.

Data analysts will often use data visualization techniques to ensure data is understandable to hospital executives and other decision-makers. Through a thorough understanding of data outputs, analysts can make their insights more communicable.

Valid Data

Data validation is the process of cleaning data and ensuring it is both correct and useful. Some data integration platforms validate data automatically, but validation can also be performed by humans.

Data validation may involve checking the source of data to ensure the data is clean and ensuring data is formatted correctly to be properly analyzed and presented. For example, if two data points are included in a set, but each uses a different type of measurement, it could skew the results of the analysis.

How Data Analytics Improves Healthcare

Data analytics is already helping hospitals become more efficient and protect their patients. Several use cases of data technology have been deployed in hospitals worldwide, leading to results like the following.

Reducing Burnout Among Hospital Staff

Burnout among hospital staff—especially nurses—was a significant problem before the pandemic struck, and it has only become more serious. According to Becker’s Hospital Review, nearly 1 in 5 nurses leave their first job within a year and RN turnover can cost up to $6.4 million for large acute-care hospitals.³

Predictive analytics can help to accurately forecast staffing requirements multiple weeks in advance, helping schedulers ensure every shift has all the resources it needs. With the right data, the scheduling process can even be automated, freeing up scheduling staff and ensuring minimum requirements are met for FTE commitments.

These steps help to reduce hospital staff feeling as if they are “short-staffed” every shift, reducing burnout.

Reducing Falls Risks

Patient falls are among the most prominent problems at hospitals, occurring at a rate of up to 5 per 1,000 bed-days.⁴ Numerous fall-prevention technologies have already been deployed in hospitals to address this problem, such as specialized beds and alarm systems.

Now, experts are using algorithms and machine learning technologies to identify which patients are most at risk of falling while being treated at in-patient facilities. This can help medical providers identify which tools and protocols are necessary to keep at-risk patients from falling.

Identifying Factors That Lead to Infection Transmission

Hospital-acquired infections (HAIs) are another core problem of in-patient treatment. A single infection can spread to other parts of the hospital, complicating treatment efforts and increasing costs. According to the Centers for Disease Control and Prevention (CDC), about one in 31 hospital patients has an HAI.⁵

Healthcare data analysts can use data sets to identify which factors lead to these dangerous infections, how they are transmitted and what can be done to prevent them. Hospitals are already using HAI surveillance systems to address this challenge.

Improving Hospital Workflow Inefficiencies

Decisions made by senior management to fine-tune hospital workflows can affect everyone in the hospital, from staff to the patients themselves. Decision-makers need viable data insights before making any decisions regarding communication, information sharing, staffing and more.

Data analysts are in a unique position to provide these insights. Through analysis and visualization, healthcare data analysts can help key decision-makers execute their efficiency plans more effectively.

Improving Patient Satisfaction

Data can assist in numerous ways to improve the patient experience.

Hospitals can collect data on patient satisfaction through surveys and the analysis of clinical outcomes. Patient feedback can be obtained on subjects like:

• Staff responsiveness
• Staff communication
• Cleanliness
• Pain management
• The usefulness of discharge information

Factors like these can be measured by a number scale, then fed into software to identify trends and areas needing improvement.

Hospitals can also improve the patient experience through personalization, much in the same way as other commercial enterprises assist their customers. Patient data can be utilized at the point of care to ensure patients have a streamlined experience and that their unique needs are met during their stay at the hospital.

Healthcare Analysts are More Important Than Ever

Proficiency in health technology was already important, but the pandemic has made it essential to running hospitals effectively and improving clinical outcomes. Today’s hospitals and medical organizations need professionals who are trained to gather and analyze data, so it can be used to improve our systems.

If you’re interested in the healthcare technology field, you don’t need to be a nurse or doctor to get started. Find out how EmergingEd’s healthcare technology courses can boost your career.

Sources:

1. Retrieved on September 15th, 2020, from weforum.org/agenda/2019/12/four-ways-data-is-improving-healthcare/
2. Retrieved on September 15th, 2020, from hitinfrastructure.com/news/ge-unveils-edison-healthcare-artificial-intelligence-platform
3. Retrieved on September 15th, 2020, from beckershospitalreview.com/hr/nearly-1-in-5-nurses-leaves-first-job-within-a-year-survey-finds.html
4. Retrieved on September 28th, 2020 from psnet.ahrq.gov/primer/falls
5. Retrieved on September 15th, 2020, from cdc.gov/hai/data/index.html