The healthcare industry has been grappling with financial constraints for years, prompting companies to seek cost-saving measures. Despite these efforts, data reveals substantial wasteful spending. According to the Institute of Medicine, the U.S. healthcare system allocates nearly a third of its resources—amounting to $750 billion annually—on unnecessary services and inefficient care.
How can we address one of the most significant challenges facing the healthcare industry? In this article, we will explore how the integration of data and artificial intelligence can provide effective solutions.
Healthcare Data: The Power of Information
Before we move on to AI, let’s start with the key term: healthcare data. The healthcare industry generates an immense amount of data, and its volume is rapidly increasing. An IDC study estimates that the volume of health data created annually surpassed 2,000 exabytes in 2020 and is projected to grow by 48% each year.
What Exactly is Healthcare Data?
Healthcare data encompasses a wide array of forms and sources. One of the most prominent forms is electronic health records (EHRs), which include patient charts, medical histories, treatment plans, lab and test results, and radiology images. However, healthcare data is not limited to EHRs. It also includes, among others:
- Paper records and legacy systems: Historical patient data and non-digitized documents.
- Financial transaction data: Billing information and insurance claims.
- Conversation data: Emails, contracts, and call center records.
- Survey data: Patient and employee feedback and satisfaction surveys.
- Research data: Disease registries, clinical trials, and laboratory research data.
As technology advances, new sources of healthcare data continue to emerge. Wearable devices like health trackers and fitness monitors generate new health-related data. Additionally, the rise of virtual and remote care services, such as telehealth and remote patient monitoring, contribute to the growing volume of digital health data. Even AI-based assistants, such as our GPT-based chatbot called SugarAssist (it offers tailored guidance on blood sugar management, physical activity, and emotional well-being) are sources of medical data that can make a difference.
Complexity of Healthcare Data
When discussing healthcare data, it’s essential to address the potential difficulties associated with processing it. These challenges arise from the diversity of data types and sources, including paper records and data in legacy systems. Healthcare data encompasses both structured data, such as information related to health systems’ financial transactions, and unstructured data, including emails, call center records, and other non-standardized formats.
Another critical aspect is sensitive patient data, which requires special protection measures to ensure its proper processing and security.
We will delve deeper into the topics of data preparation and security in next articles. Subscribe to our newsletter so you don’t miss out on these important issues.
Predictive Analytics: A Game-Changer in Healthcare
And what relationship does the data have to the aforementioned problem of inappropriate business decisions? For most problems associated with unpredicted risks, unnecessary services, and inefficient care, data is the key. This brings us to another important term: predictive analytics.
What is Predictive Analytics?
Predictive analytics is a powerful tool where computer programs analyze past events, occurrences, or patterns to forecast the future logically. This discipline is rapidly growing within the healthcare industry, providing solutions to many problems associated with unpredicted risks, unnecessary services, and inefficient care.
Data is crucial in helping healthcare providers prevent rapid health deterioration and intervene when it matters most. It can anticipate future outcomes and enable informed decision-making.
Predictive analytics can help practitioners answer critical questions, such as:
- Which diseases are patients likely to develop?
- How will patients respond to different treatments?
- Will a patient be a no-show for their next medical appointment?
- Will a patient return to the hospital within 30 days of discharge?
- What are the chances a newborn baby will develop sepsis?
- Will a general ward patient deteriorate further and need ICU admission in the next 48 hours?
- How many extra beds will be needed to serve a rising number of patients in the second wave of a pandemic?
These insights demonstrate the transformative potential of predictive analytics in healthcare, enabling providers to make better-informed decisions and improve patient outcomes.
Healthcare Data Analytics Market Worth
The global healthcare analytics market, in terms of revenue, was estimated to be worth $27.4 billion in 2022 and is projected to reach $85.9 billion by 2027, growing at a CAGR of 25.7% from 2022 to 2027.
This substantial growth highlights the increasing reliance on data-driven insights to enhance patient care and operational efficiency.
Several factors drive this market expansion, including the increased adoption of healthcare analytics solutions and services, the growing importance of analytics in healthcare, and the increased use of technologically advanced tools.
The Impact of Predictive Analytics in Healthcare
What are the outcomes? Predictive analytics has demonstrated significant promise in improving healthcare results. By utilizing extensive data for numerous variables, risk prediction has become a crucial aspect of modern healthcare. At the individual patient level, AI-powered risk assessments allow for early intervention in severe and expensive diseases. Data indicates that this approach can decrease the mortality rate from 45% to 24% and reduce instances of cardiac arrest by 80%.
On a broader scale, big data and predictive analytics can anticipate epidemics, offering essential insights that help prevent widespread outbreaks and manage public health more efficiently. During the initial stages of the pandemic, the healthcare industry struggled without effective forecasting tools. This experience highlighted the need for a predictive system to navigate future uncertainties.
Improved foresight helps healthcare organizations prepare for changes in the insurance market, the economy, service utilization, consumer behavior, and future infectious disease waves. Predictive analytics provides a valuable perspective to manage these uncertainties, ensuring that the healthcare sector is better prepared for upcoming challenges. By adopting predictive analytics, healthcare providers can enhance their readiness and responsiveness, leading to better patient outcomes and more efficient healthcare systems.
5 Key Benefits of Predictive Analytics in Healthcare
Predictive analytics offers numerous benefits to healthcare leaders, enhancing both patient care and operational efficiency. Here are some key advantages:
- Reducing Costs: By predicting appointment no-shows , healthcare providers can significantly cut costs. Predictive analytics helps identify patients at risk of missing appointments or being readmitted, enabling timely interventions.
- Streamlining Administrative Tasks: Predictive analytics can expedite administrative processes such as discharge procedures and insurance claims submission. By automating these tasks, healthcare providers can reduce wait times and improve overall efficiency.
- Enhancing Cybersecurity: Predictive analytics can prevent ransom and other cyberattacks by analyzing ongoing transactions and assigning risk scores. This proactive approach helps in identifying potential threats and mitigating them before they cause harm.
- Preparing for Population Health Trends: Healthcare providers can use predictive analytics to forecast upcoming population health trends. This enables them to proactively prepare for changes in patient demographics and disease prevalence, ensuring they are better equipped to meet future healthcare demands.
- Attracting New Clients: By leveraging predictive analytics for personalized campaigns, healthcare companies can create tailored marketing strategies based on data insights to help in reaching the right audience and improving their engagement.
Real-World Examples of Predictive Analytics in Healthcare
Reducing Critical Events
There are numerous remarkable examples of using predictive analytics in healthcare. One example is Ysbyty Gwynedd hospital, which experienced a 35% decrease in critical events and an 86% reduction in cardiac arrests after implementing a predictive analytics solution.
Decreasing Sepsis Mortality
Another notable example is Huntsville Hospital in Alabama, which used predictive analytics in tandem with clinical decision support (CDS) tools to decrease sepsis mortality among younger, older, and chronically ill patients by as much as 53%.
Predicting Suicidal Ideation
Additionally, a study conducted on Korean adults demonstrated that machine learning algorithms could predict more than 80% of suicidal ideation and attempts based on various mental and socioeconomic characteristics.
Early Detection of Ovarian Cancer
The Georgia Tech Integrated Cancer Research Center has made a significant leap in early ovarian cancer detection. By combining machine learning with blood metabolite analysis, they developed a test with a remarkable 93% accuracy for detecting ovarian cancer. This method offers a personalized, probabilistic approach that provides a nuanced and precise probability of disease presence.
Unlock the Power of AI Combined with Healthcare Data
Health data combined with AI has tremendous potential, as shown by the examples in this article. Solutions like predictive analytics can revolutionize patient care and operational efficiency in healthcare.
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