The healthcare industry is wrestling with financial restrictions for years, prompting companies to search for measures to save costs. Despite these efforts, the data reveals great spending. According to the Institute of Medicine, the American health care system devotes approximately a third of its resources 750 billion dollars annually– On unnecessary services and ineffective care.
How can we treat one of the most important Challenges facing the healthcare industry? In this article, we will explore how data and artificial intelligence can provide effective solutions.
Health care data: Information strength
Before we move to artificial intelligence, let’s start with the main term: health care data. The healthcare industry generates a huge amount of data, and its size is increasing quickly. The IDC study estimates that the extent of the annual healthy data has been overlooked 2000 Exabytes in 2020 It is expected to grow 48 % every year.
What exactly are health care data?
Healthcare data includes a wide range of models and sources. One of the most prominent models is EHRS, which includes patients, medical history, treatment plans, laboratory and test results, and radiology. However, health care data is not limited to EHRS. It also includes, among other things:
- Paper records and old systems: Patient’s historical data and non -digital documents.
- Financial transaction data: Insurance and insurance demands.
- Conversation data: Email messages, contracts and communication center records.
- Survey data: Patient reactions, employee and satisfaction surveys.
- Search data: Records of diseases, clinical experiences and laboratory research data.
With technology progress, new sources of health care data continue to appear. Services that can be worn like health tracking devices and fitness screens create new health data. In addition, the rise of virtual and remote care services, such as remote care and distance monitoring of the patient, contributes to the increasing volume of digital health data. Even artificial intelligent assistants, such as the GPT -based Chatbot called Sugarist (It provides guidelines designed about blood sugar management, physical activity and emotional well -being) are sources of medical data that can make a difference.
Complex healthcare data
When discussing health care data, it is necessary to process the potential difficulties associated with their treatment. These challenges arise from the diversity of data types and sources, including paper records and data in old systems. Health care data includes both organized data, such as information related to financial transactions for health systems, and unorganized data, including emails, communication center records and other non -standard formats.
Another decisive aspect is sensitive patient data, which requires special protection measures to ensure appropriate treatment and safety.
We will delve into the topics of data and security preparation in the following articles. Subscribe to our newsletter so that you do not miss these important issues.
Predictive analyzes: change the game in health care
What is the relationship explained by the above -mentioned data represented in the inappropriate business decisions? For most unexpected risk problems, unnecessary services, and ineffective care, the data is the key. This leads us to another important term: predictive analyzes.
What are the predictive analyzes?
Prediction analyzes are a powerful tool where computer programs analyze events, events or previous patterns of predicting the future in a logical way. This discipline grows rapidly in the health care industry, providing solutions to many problems associated with unexpected risks, unnecessary services, and ineffective care.
The data is important in helping health care providers prevent the deterioration of fast health and intervention when it is more important. You can expect future results and enable enlightened decisions.
Practic predictive analyzes can help answer critical questions, such as:
- What diseases are likely to develop patients?
- How will patients respond to various treatments?
- Will the patient will be a lack of attendance for his next medical appointment?
- Will the patient return to the hospital within 30 days of exit?
- What are the chances of developing a newborn blood rot?
- Will a general wing patient determine more and need to accept the intensive care unit during the next 48 hours?
- How many additional beds needed to serve an increasing number of patients in the second wave of the epidemic?
These ideas show the transformative potential of predictive analyzes of health care, enabling service providers to make better enlightened decisions and improve patient results.
The health care data analysis market deserves
The global health care analysis market, in terms of revenue, was estimated at $ 27.4 billion in 2022 and is expected to arrive 85.9 billion dollars by 202725.7 % annual growth rate grows from 2022 to 2027.
This great growth highlights the increasing dependence on data -based ideas to enhance patient care and operational efficiency.
Many factors drive this expansion in the market, including increasing the adoption of health care solutions and services, the increasing importance of health care analyzes, and the increase in technically advanced tools.
The effect of predictive analyzes on health care
What are the results? A predictive analysis showed a great promise to improve health care results. By using extensive data for many variables, risk prediction has become a decisive aspect of modern health care. At the individual patient level, risk assessments on behalf of artificial intelligence allow early intervention in severe and expensive diseases. The data indicates that this approach It can reduce the death rate From 45 % to 24 % and reduce heart attacks by 80 %.
On a wider scale, huge data and predictive analyzes can anticipate epidemics, providing basic visions that help prevent widespread spread and public health management. During the initial stages of the epidemic, the healthcare industry struggled without effective prediction tools. This experiment highlighted the need for a predictive system to navigate in cases of uncertainty in the future.
Improving insight helps health care organizations to prepare for changes in the insurance market, economy, use of services, consumer behavior, and future infectious diseases. A predictive analysis provides a valuable perspective to manage these uncertainty, which ensures that the health care sector is more ready for the upcoming challenges. By adopting predictive analyzes, healthcare providers can enhance their willingness and response, which leads to better results for patients and the most efficient health care systems.
5 The main benefits of predictive analyzes in health care
Prediction analyzes provide many benefits to health care leaders, which enhances both patient care and operational efficiency. Below are some of the main advantages:
- Reducing costs: By predicting dates, health care providers cannot significantly reduce costs. Predictive analyzes help to identify or re -accept patients at risk of losing appointments, allowing timely interventions.
- Simplify administrative tasks: Prediction analyzes can accelerate administrative processes such as discharge procedures and insurance claims. By automating these tasks, health care providers can reduce waiting times and improve total efficiency.
- Promoting cybersecurity: Performing analyzes can prevent ransom and other electronic attacks by analyzing continuous transactions and setting risk degrees. This pre -emptive approach helps to determine and relieve potential threats before causing harm.
- Preparing for population health trends: Healthcare providers can use predictive analyzes to predict upcoming population health directions. This enables them to proactively prepare for changes in the patient’s population composition and the spread of diseases, ensuring that they are better equipped to meet future health care requirements.
- Attracting new customers: By taking advantage of the predictive analyzes of personal campaigns, health care companies can create marketing strategies designed on the basis of data visions to help access the appropriate audience and improve their participation.
Examples in the real world of predictive analyzes of health care
Reducing critical events
There are many great examples of using predictive analyzes in health care. One example is Ysbyty Gwynedd HospitalWhich witnessed a 35 % decrease in critical events and a 86 % reduction in heart attacks after implementing predictive analyzes.
Low septic deaths
Another noticeable example is Huntville Hospital in Alabama, which used predictive analyzes Up to 53 %.
Prediction to think about suicide
In addition, a study on Korean adults showed that machine learning algorithms could predict them More than 80 % From suicide thinking and attempts based on various mental, social and economic characteristics.
Early detection of ovarian cancer
The Georgia Center for Integrated Technology for Cancer Research made a great leap in detecting early ovarian cancer. By combining machine learning with a blood future analysis, they developed a test with great 93 % accuracy To detect ovarian cancer. This method provides a personal and probability approach that provides an accurate and accurate possibility of the presence of the disease.
Opening the strength of artificial intelligence with healthcare data
Health data besides artificial intelligence has huge potential, as shown in examples in this article. Solutions such as predictive analyzes can revolutionize patient care and operational efficiency in health care.
If you want to know how to successfully implement the artificial intelligence technology in your organization and avoid the risks associated with it, download our electronic book “How to implement artificial intelligence in your company.“Transfer your operations and harness the strength of artificial intelligence today
