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anaplatform Data Consultancy
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Data Analytics for Healthcare

Living in a time of technological advancement, nearly every business is eager to use advanced machine learning and data science in the insurance domain to maintain a reputation and drive wisely with the adoption and execution of algorithmic applications in nearly every field, including healthcare, customer service, and insurance.

The healthcare sector is currently experiencing the biggest technological change in its history. There is now an overriding trend across the entire sector towards precision medicine, often described as personalized medicine. In particular, the use of health and lifestyle data for diagnostic investigations and treatment has become a number one priority. Hospitals and other medical institutions benefit enormously from digitalization and the innumerable opportunities it creates for increased efficiency and quality and the design of new proposals. However, they are also faced with the huge challenges that result from it.

As Anaplatform, We happily guide you through the design and conversion phase of your digitalization strategy, help you access medical documents and data and develop new proposals for qualified medical staff and patients. We do this by putting into practice our many years experience of Machine Learning, Artificial Intelligence (AI) and data analysis.
Current Status of Big Data in Healthcare Industry

According to McKinsey’s report in 2013, the revolution of big data is accelerating the innovation in healthcare industry. By using cloud computing and big data analysis technology, established a patient-centered medical application and service network physical system, to solve the problem of different data storage formats.

Some Online diagnosis and treatment platforms have appeared on the Internet to provide patients with early disease consultation. Some sites allow patients to conduct online diagnosis and treatment with real doctors in the form of online chat, while others use artificial intelligence to realize preliminary triage of patients

What are the benefits of data analytics in healthcare?

Benefits include:

  • Diseae Prediction: make more precise predictions for patients
  • Research & Development: make the research process efficient overall
  • Public Health: in an event of crises deal with the event more effectively and efficiently
  • Evidence-based medicine: using past results from different sources (eg. EMR, or operation data) predict future outcomes
  • Genomic analytics: Analyze Gene data or do Gene Sequencing more efficiently
  • Clinical Operation: Reduce cost for patient diagnosis
  • Pre-adjudication fraud analysis: Analyze operation data to see if fraud exist
  • Device/remote monitoring: Analyze data obtained from IOT devices
  • Patient profile analytics: perform patient specific analytics to see what is the best procedure for that specific patient
Real-time alerts

Clinical support decision is a real-time application. It would offer prescription after analysis of the medical data of a patient. This will help the doctors to analyze their patient’s health conditions and suggest the precaution. If a patient is suffering from any disease, for example, blood pressure issues or a headache, then a sudden increase or decrease of blood pressure or any health issues related to the disease will be analyzed by their concerned doctor and provides the information with appropriate treatment. All the procedure of treatment be carried out by latest techniques of big data.

Evidence-based medicine

Evidence-based medicine provides the doctor with information about the patient’s record and also compares the symptoms that have been stored in a larger database of the clinical data, for which it will be easy for enabling accurate, faster, and more efficient treatments. It is one of the use cases of big data helps in easy decision making.

Hospital readmissions

Big data analytical techniques identify the at-risk patients on the basis of their medical reports, records, and clinical reports and offers them a reduced readmission rate that will help in allowing a patient not to focus on the readmission charges but on their clinical treatment.

Fraud detection

While keeping the observation of different test of patients, keeping their record of health issue it is necessary that all the records or information related to each patient should maintain privacy because each data are unique. Big data analytical technique helps in dealing with fraudulence in the billing, personal identity, patient records, clinical test etc. Insurance fraud has become a national problem where claimants try to obtain money and they use big data techniques to help the patients to prevent them from fraud. They keep on changing the database for security point of view and therefore the insurance company regularly maintains the updating through predictive analysis which plays a crucial role in security concerns.

Huge chunks of changing data are maintained and secured using this big data. Big data analytics technique and its use cases are growing day by day, which helps the different firms and organizations, whether it is social media or health care. It helps in reducing work effort and memory space by gaining high productivity and growth, innovative ideas, reduced time and cost associated to it.

Analysis Steps

The analysis entails many discrete steps:

  • Integrate heterogeneous data types.
  • Ensure the quality of the data upon reception and throughout the analysis.
  • Create data models.
  • Interpret the results of the analysis.
  • Validate the analysis results.
Why Healthcare Analytics ?
Healthcare analytics enables medical directors to identify high-risk disease groups and act to minimize risk and improve patient outcomes.

For example, new preventive treatment protocols could be introduced among patient groups with high cholesterol, thereby fending off heart problems. Also, complex health informatics reports were generated 300% faster than previously, helping BCBSMA service clients more effectively

Big data analytics has the potential to transform the way healthcare providers use sophisticated technologies to gain insight from their clinical and other data repositories and make informed decisions. In this platform, we provide rapid, widespread implementation and use of big data analytics across the healthcare organization and the healthcare industry.

As big data analytics becomes more mainstream, issues such as guaranteeing privacy, safeguarding security, establishing standards and governance, and continually improving the tools and technologies will garner attention. Big data analytics and applications in healthcare are at a nascent stage of development, but rapid advances in platforms and tools can accelerate their maturing process.

The benefits of data analytics in healthcare are realized in seven distinct areas:
  • Support clinical treatment decisions from physicians and other health professionals.
  • Improve the accuracy and speed of identifying patients at highest risk of disease.
  • Provide greater detail in the EHRs of individual patients.
  • Make the provision of healthcare more efficient, which reduces costs.
  • Promote preventive measures by giving patients greater insight into their health and treatment goals.
  • Integrate data from consumer fitness devices and other patient-provided sources of health data.
  • Deliver real-time alerts to healthcare providers by analyzing health data at the collection point.
How we use data analytics in healthcare settings ?

Data analytics in healthcare can be applied to every aspect of patient care and operations management. The analyses investigate methods of improving the provision of clinical care, enhancing disease prevention, and measuring the effectiveness of various treatment options.

The ability of data analytics to convert raw healthcare data into actionable intelligence is expected to have the greatest impact on these areas of healthcare:

  • Research and prediction of disease
  • Automation of hospital administrative processes
  • Early detection of disease
  • Prevention of unnecessary doctor’s visits
  • Discovery of new drugs
  • More accurate calculation of health insurance rates
  • More effective sharing of patient data
  • Personalization of patient care
Using patient data to improve health outcomes

The goal of quality improvement in healthcare settings is to treat patients safely and effectively while minimizing the trauma associated with their treatment. To achieve this goal, healthcare providers collect and analyze patient data, increasingly in real time, to gain a clearer understanding of today’s complex healthcare environments; to develop and apply a systematic approach to improve patient outcomes; and to continuously develop, test, and implement enhancements to healthcare processes.

By analyzing patient data, healthcare providers can lower readmission rates, reduce errors, and better identify at-risk populations. The types of patient data used in these analyses include blood sugar level, temperature, blood test results, and the patient’s own wishes for care. The primary factors that influence the quality of care patients receive are:

  • The health outcomes that patients expect and that matter most to them.
  • How the processes that healthcare providers use impact patients’ desired outcomes.
  • How the resources, equipment, regulations, and other aspects of healthcare infrastructure affect the quality of care that patients receive.
Supporting clinical studies

Clinical studies are conducted in a controlled environment and their outcomes are evaluated for statistical purposes. Machine Learning methods are ideal for deriving answers from data material. They cannot only provide answers to questions but also discover patterns that have yet not been recognized.

We can analyze your clinical studies retrospectively (secondary analysis) and/or gather any information prospectively. In doing so we can develop new analytical procedures, such as for the study of chronic diseases related to the immune system.

Text Mining for medical documents

A significant amount of information vitally important to routine medical procedures exists in the form of text documents. These documents can be wide ranging and can include specialized medical literature and study outcome publications as well as physician’s letters and medical reports.

We offer Text Mining solutions based on "Natural Language Understanding". It involves extracting the essential information from all of the relevant documents and making it available in a structured format.

Digitalization in medical facilities

The healthcare sector can benefit enormously from digitalization and the opportunities it creates for increased efficiency, quality and new ways of working.

We help you identify and unlock this potential. We also support you with the design and targeted implementation of a made-to-measure digitalization strategy.

Working together we can determine which procedures need to be digitalized and what specific application scenarios might look like.

Data Sources

Many different types and forms of data are combined when analytics techniques are applied in healthcare settings, medical research, and public health departments. Health-related data sources include electronic health records (EHRs), genomics and post-genomics, bioinformatics, medical imaging, sensor informatics, medical informatics, and health informatics.

As in every period, in the field of medicine, which is the most important research area of our day, the data of the patients are continuously recorded. While the recorded data sometimes seem to be insignificant alone, it is possible to obtain important information that is hidden when it is analyzed together with other data. Thanks to the valuable information obtained, it helps the development of the health sector and the correct diagnosis of the doctors.

Different Types of Data for Medical Domain

In the history of medical domain, the medical data only specific to text but now it varied from text to image, sound and also now follow different way to capture different types of data regarding work domain. The data is of a wide range of types and sources. The data are of a wide range of different types and sources which are gathered from different sources like image data of X-ray type, and data that are collected electronically relate to any physical issues or relating to this other structured data. The extensive variety of structured, unstructured, and semistructured data are of different dimensions which helps in making different methods from the medical point of view, and it becomes very challenging and interesting by applying big data analytics.

Medical Images

Medical images represent the procedures used to image the inside(interior) of a patient’s body for clinical analysis. It is generated commonly by using X-rays, Magnetic Resonance Imaging (MRI), Microscopy Image, Optical Coherence Tomography (OCT), or Position Emission Tomography (PET)

Clinical data comes not only in the form of clinical notes or images, but also in many other forms like demographic notes (Gender, age, location, and marital status), results of laboratories tests, physiological measurement results, etc.

Clinical Notes

Text is one of the most essential categories in EHRs since it contains important information on the patient’s health status, like tests results, diagnosis, treatments, etc. Besides that, other facultative data, such as the medical history of the family, allergies, are also collected and recorded in order to aid in clinical decisions and to avoid diseases or misapplication of treatments.

Electronic Health Records

Electronic health records (EHR) is an advanced and electronic version of the health information system that provides documentation on illnesses, previous consultations, and examination results.

Patient biological records or health history details have been stored traditionally in files and folders. Sometimes it leads to erroneous information while keeping the data and leads to wrong prediction about patient disease. Electronic health record (EHR) [12] is one of the widespread methods to keep the patient details using big data techniques. EHRs keep the details of each patient’s health chart and their medical reports, which helps in reducing the need for duplicity in tests and the associated cost.

Our Services

We offer a variety of services for Healthcare data analysis:

Have a question ?

If you're looking for a reliable partner to help you unlock the power of genetic data in healthcare, look no further than anaplatform. Contact us today to learn more about how we can help you advance your research efforts and improve healthcare outcomes.