In today’s data-driven world, healthcare data analysis is rapidly becoming an essential tool for improving patient care and outcomes in the healthcare industry. By collecting and analyzing vast amounts of patient data, healthcare professionals can identify patterns and trends that enable them to make better-informed decisions and provide more personalized care to patients.
According to the latest report by marketsandmarkets,
“The market size of Healthcare Analytics accounted for USD 27.4 billion in 2022, and is expected to have an annual growth rate of 25.7% between 2022 and 2027.”
In this blog, we will deepen our understanding of healthcare data analysis and how it improves patient care and outcomes. We will also take some insights from certain interesting applications and case studies of data analytics in healthcare.
Decoding Healthcare Data Analytics
The healthcare industry generates a huge amount of data daily. However, this raw data has limited value until it’s refined and made appropriate for people to understand and apply.
Healthcare data analysis involves analyzing structured and unstructured data using specialized techniques, to identify patterns and gain insights that may not have been apparent otherwise. The ultimate objective is to uncover valuable information and trends that can inform decision-making processes.
As a result of healthcare analytics, the data obtained has numerous advantages and can be applied in countless healthcare applications and healthcare marketing that lead to better patient care and outcomes.
Types of Healthcare Analytics
There are several types of healthcare analytics, each with a unique role in healthcare and the capability of providing valuable insights for healthcare professionals. Access to these insights and data can assist them in improving patient outcomes as well as optimizing costs.
1. Descriptive Analytics
Descriptive analytics enable the healthcare provider to understand “what happened” historically.
It uses historical data and utilize basic statistical measures such as counts, percentages, averages, and standard deviation to provide insights into what has happened in the past.
Typically, these descriptive analytics measures can be monitored over time to determine if a particular practice is meeting its objectives.
2. Diagnostic Analytics
Diagnostic analytics enable the healthcare provider to understand “why it happened”.
It enable a deeper analysis of the data, typically to understand or clarify the insights obtained through descriptive analytics.
It requires analyzing data to comprehend the causes of specific events or results, allowing healthcare practitioners to identify the underlying causes of problems and make corrective decisions.
3. Predictive Analytics
Predictive analytics enable the healthcare provider to understand “what will happen”, and create predictions by analyzing the past data.
It employs statistical algorithms, data analysis, and machine learning techniques to anticipate the probability of future events by examining past data.
This approach can be applied in healthcare to predict disease outbreaks, assess patient readmissions, and monitor medication adherence.
4. Prescriptive Analytics
Prescriptive analytics enable the healthcare provider to understand “what actions are needed” in order to change the prediction.
It provides healthcare practitioners with suggested actions based on the data analyzed by employing statistical algorithms.
Thus, there are four types of healthcare analytics: descriptive, diagnostic, predictive, and prescriptive, that can provide valuable insights for healthcare professionals to improve patient outcomes and optimize costs. Descriptive analytics helps understand what happened historically, diagnostic analytics helps understand why it happened, predictive analytics creates predictions by analyzing past data, and prescriptive analytics suggests actions based on the data analyzed.
What Is The Role Of Data Analytics In Healthcare?
Healthcare data analysis has become an essential element of modern healthcare, owing to its numerous advantages. These advantages include enhancing the quality of patient care, improving outcomes, and boosting the operational efficiency of healthcare organizations.
Let’s take a look at 3 key ways in which healthcare analytics is improving patient care –
1. Continuous Monitoring Boosts Patient Safety
Continuous monitoring of patients is a critical aspect of healthcare that involves monitoring patients around the clock to track potential health issues early on and intervene as required. With the help of healthcare data analysis, healthcare professionals can gain valuable insights into patient vitals, such as blood pressure, heart rate, and oxygen saturation, and identify potential health issues, to manage them without unnecessary delay.
The information provided by healthcare analytics can be used to develop personalized care plans tailored to each patient’s needs for maximum efficacy and successful outcomes.
Healthcare data analysis can also be used to develop predictive models that can forecast potential health issues and alert healthcare professionals when a patient’s vitals fall outside normal ranges. This early warning system can help reduce hospital stays and improve patient outcomes.
2. Personalized Approach Makes Healthcare More Patient-Centered
Recent studies have revealed that
“72% of healthcare providers feel consumer-centric strategies are important, with 25% already investing in them and nearly 90% making related staffing changes.”
In the past, medical treatments were generally prescribed using a one-size-fits-all approach based on limited information derived from broad population statistics rather than tailored to individual patients. Despite the latest scientific advancements, this was the most that could be achieved.
One of the most significant advantages that healthcare analytics has brought to the healthcare industry is the ability to personalize patient care. Every patient is unique, and data analytics can help healthcare professionals tailor treatment plans to meet each patient’s individual needs. By analyzing data from patient records, healthcare professionals can identify trends and patterns and develop personalized care plans, including medicines that are tailored to each patient’s unique needs.
Data analysis also led to a considerable improvement in medical device marketing, enabling healthcare professionals to effectively target the most suitable patients using the insights gained from the data analysis.
3. Improves Hospital Efficiency and Reduces Waiting Time
Healthcare analytics have a phenomenal impact on the improvement in the efficiency of hospital operations.
The Journal of Healthcare Management has concluded that
“With the healthcare data analysis, 79.2% of hospitals have shown improvement in their quality of care. And it was estimated that, if all the hospitals have increased their quality factor performance to 100%, then there would be 24.8% fewer deaths, 15.8% fewer readmissions, and the average length of stay would have been 13.5% less.”
By analyzing data from hospital records, healthcare professionals can identify bottlenecks and inefficiencies in the healthcare system. This information can be used to streamline operations and reduce wait times. By improving the efficiency of hospital operations, healthcare professionals can provide better patient care and reduce healthcare costs.
Thus, healthcare data analysis has numerous advantages, including enhancing patient safety, personalizing patient care, and improving hospital efficiency. Healthcare analytics enables healthcare professionals to monitor patients continuously, identify potential health issues, and develop personalized care plans for each patient. It also helps in improving the efficiency of hospital operations, reducing wait times, and improving patient outcomes.
Real-life Healthcare Analytics Success Story: LifeSigns
LifeSigns is revolutionizing the healthcare industry by redefining how patients are monitored throughout their medical journey, from pre-hospitalization to post-discharge.
This transformation is being made possible by adopting healthcare data analysis processes that include implementing new systems and cutting-edge technology like semiconductors and System on Chip (SoC), advanced networking, wireless capabilities, cloud computing, and analytics.
The highly-regarded LifeSignsiMS software platform, coupled with clinical-grade biosensors, is transitioning patient continuous monitoring from an outdated, manual process to a modern, automated, and wireless one. The system features a robust alert engine that anticipates patient needs, offering unparalleled care and safety. This represents a significant leap forward in healthcare technology.
Conclusion
The impact of healthcare data analysis on the healthcare industry has already started making substantial differences by providing high-quality patient care, reducing costs, and improving the effectiveness of streamlining healthcare operations.
To harness the transformative power of data analytics in healthcare, it is essential to comprehend how this technology can be utilized to tackle challenges faced by healthcare providers. These challenges include staff recruitment and allocation, streamlining operational processes, and elevating patient experiences.
Healthcare analytics played a pivotal role in personalizing a patient-centric approach, continuous patient monitoring, and effectively improving healthcare marketing. As data analytics continues to evolve and improve, we can expect to see more advances in healthcare and better patient outcomes in the years to come.
