Application of data science in healthcare

In recent years, data science has emerged as a transformative force in healthcare, revolutionizing how medical professionals diagnose, treat, and manage diseases. By harnessing the power of data analytics, machine learning, and artificial intelligence (AI), healthcare providers can unlock valuable insights from vast amounts of healthcare data, leading to improved patient outcomes, reduced costs, and more efficient healthcare delivery. In this blog, we will understand scope, applications, roles & responsibilities, and top Indian companies in the sector.

You may like to read who should pursue an MBA in healthcare management.

What is data science in healthcare?

Data science in healthcare refers to the application of data analysis techniques, algorithms, and statistical methods to extract insights from the vast amounts of data generated within the healthcare system. This interdisciplinary field combines principles from statistics, computer science, and domain expertise in medicine to improve patient outcomes, streamline operations, and enhance decision-making processes.

Why data science in the healthcare industry?

Data scientists are in high demand in healthcare because most data in this sector needs to be more structured and easier to access and evaluate. Data science has simplified the handling of a vast amount of information in the healthcare industry, including but not limited to electronic medical records, clinical trial data, genetic information, billing data, wearable data, care management databases, scholarly journals, etc.

4 Pillars of Data Science

Domain Knowledge	Business Knowledge, Expert Systems, User Testing
Math & Statistics Skills	Linear Algebra, Calculus, Descriptive Statistics
Computer Science	Big Data Technologies, Programming, Database
Communication & Visualization	Storytelling Skills, Visual Art, Engagement with Seniors

The application of data science in healthcare has benefited the creation and assessment of policies to improve accessibility, equality, and the general caliber of care given. Data science has immense potential in many fields, including the following:

• Prevention plans
• Diagnosis of diseases
• Delivering more precise prescriptions and customized care
• Post-care monitoring
• Hospital operations

Application of data science in healthcare with examples

1. Clinical Decision Support:

Data science is enabling the development of clinical decision support systems that help healthcare providers make more informed decisions about patient care. These systems analyze patient data, medical literature, and best practices to recommend personalized treatment plans, predict disease progression, and identify potential risks.

2. Disease Surveillance and Outbreak Prediction:

Data science is playing a crucial role in disease surveillance and outbreak prediction. By analyzing data from various sources, such as electronic health records (EHRs), social media, and public health reports, data scientists can detect patterns and trends that may indicate the emergence of infectious diseases or other health threats. This early detection can help public health officials take timely action to prevent the spread of disease.

3. Personalized Medicine:

Data science is driving the advancement of personalized medicine, which aims to tailor medical treatment to individual characteristics such as genetics, lifestyle, and environment. By analyzing patient data, including genetic information, data scientists can identify biomarkers that predict how a patient will respond to a particular treatment, leading to more effective and targeted therapies.

4. Healthcare Fraud Detection:

Data science is helping to combat healthcare fraud by analyzing claims data to detect patterns of fraudulent activity. By identifying outliers and anomalies in billing patterns, data scientists can help healthcare organizations prevent fraud and abuse, saving millions of dollars in healthcare costs.

5. Remote Patient Monitoring:

Data science is enabling remote patient monitoring, allowing healthcare providers to track patients’ health metrics in real time from a distance. By analyzing data from wearable devices and other remote monitoring tools, data scientists can detect changes in a patient’s condition and intervene early to prevent complications.

6. Drug Discovery and Development:

Data science is accelerating the drug discovery and development process by analyzing large datasets to identify potential drug targets, predict the efficacy of new drugs, and optimize clinical trial designs. This can lead to the faster development of new treatments and therapies for a wide range of diseases.

7. Population Health Management:

Data science is helping healthcare organizations improve population health management by analyzing data to identify at-risk populations, predict future health trends, and design targeted interventions. This proactive approach to healthcare can lead to better health outcomes and lower costs.

You may like to read who should pursue an MBA in healthcare management.

Real-world use cases of data science in healthcare

Data science is making significant strides in healthcare, with various organizations applying advanced analytics to solve real-world problems. Here are some notable examples:

1. Predictive analytics

Use Case: Mount Sinai Health System

• Description: Mount Sinai uses predictive analytics to identify patients at high risk of hospital readmission.
• Example: By analyzing patient demographics, medical history, and social determinants of health, they developed a model that helps healthcare providers tailor post-discharge care. This initiative has led to a marked reduction in readmission rates.

2. Personalized Medicine

Use Case: Foundation Medicine

• Description: Foundation Medicine specializes in genomic profiling for cancer treatment.
• Example: They analyze the genomic data of tumors to match patients with targeted therapies. This personalized approach has improved treatment outcomes for many patients with specific types of cancer, enabling oncologists to choose the most effective treatments based on individual genetic profiles.

3. Drug Discovery and Development

Use Case: Atomwise

• Description: Atomwise uses AI and machine learning to accelerate drug discovery processes.
• Example: By analyzing molecular structures and simulating interactions, Atomwise has helped identify promising candidate compounds for diseases like Ebola and multiple sclerosis, significantly reducing the time needed for drug development.

4. Operational Efficiency

Use Case: Cleveland Clinic

• Description: Cleveland Clinic employs data analytics to improve operational efficiency and patient flow.
• Example: Using predictive modeling, they forecast patient volumes in various departments, allowing better staffing and resource allocation, ultimately improving patient wait times and service delivery.

5. Population Health Management

Use Case: Geisinger Health System

• Description: Geisinger integrates data science into population health management strategies.
• Example: They analyze health data to identify at-risk populations and implement targeted interventions, such as their ProvenCare program, which offers bundled payments for specific procedures, improving care consistency and outcomes.

6. Clinical Decision Support Systems (CDSS)

Use Case: Epic Systems

• Description: Epic Systems provides electronic health record (EHR) software with built-in CDSS functionalities.
• Example: Their system alerts physicians about potential drug interactions or allergies, enhancing patient safety and ensuring evidence-based clinical decisions.

7. Remote Patient Monitoring and Telehealth

Use Case: Philips Healthcare

• Description: Philips offers remote monitoring solutions for patients with chronic diseases.
• Example: Their wearable devices track vital signs and send data to healthcare providers. This allows proactive management of conditions like heart disease, where deviations in monitored data can trigger early interventions.

8. Natural Language Processing (NLP)

Use Case: Mayo Clinic

• Description: Mayo Clinic utilizes NLP to extract insights from unstructured clinical notes.
• Example: By applying NLP to analyze physician notes, they can identify trends and improve patient care strategies based on findings from the narrative data.

9. Operational Risk Management

Use Case: Intermountain Healthcare

• Description: Intermountain Healthcare employs data science for operational risk assessment.
• Example: They analyze historical surgical data to predict potential complications, allowing them to implement preventive measures and improve surgical outcomes.

10. Fraud Detection

Use Case: Optum

• Description: Optum, a healthcare analytics company, uses data science to detect fraud in healthcare claims.
• Example: By analyzing billing patterns and claims data, they employ machine learning algorithms to flag suspicious activities, helping reduce fraudulent claims and save costs.

Roles & responsibilities of a data science professional in healthcare

The efficiency of healthcare workers is increased due to the constant improvements and innovations in the industry. The important role of data science in healthcare is crucial to integrating data modeling methods throughout the industry. Data scientists can make use of the available data to draw conclusions and develop models for the future. 

A data scientist’s primary functions in the healthcare sector include the following:

• To gather patient information or data.
• To assess the needs or requirements of the hospital.
• To organize and classify data so that medical practitioners can use it.
• To execute data analyses using a variety of tools.
• Implement various methods to draw insights from unstructured or raw data.
• Using the assistance of the development teams to create predictive models.

Data scientists assist healthcare providers by giving them the knowledge they need to treat patients effectively.

Top Indian companies in healthcare

1. Manipal Hospitals

Manipal Hospitals is a chain of multi-specialty hospitals located in India. It is known for providing high-quality medical care and advanced treatments across a wide range of specialties. The hospitals have a team of experienced doctors and healthcare professionals who use the latest technology and equipment to provide a wide range of services, including diagnostics, outpatient care, inpatient care, and surgeries. The hospital also offers a variety of programs and services that are designed to help patients and their families manage their health and wellness. Some of the key specialties offered by Manipal Hospitals include cardiology, neurology, oncology, orthopedics, and urology. They also have a strong focus on providing patient-centered care and making healthcare accessible to all. 

2. Apollo Hospitals Enterprises Ltd.

It is a chain of Indian hospitals established in 1983, with its headquarters in Chennai. Hospitals, pharmacies, and diagnostic facilities are its products. Apollo Hospital is the leading corporate hospital in India and a pioneer of the private healthcare revolution.

3. Aster DM Healthcare Ltd.

Aster DM Healthcare, one of the top healthcare organizations in India, was established in 1987. Its main office is in Dubai. The corporation runs hospitals, clinics, diagnostic facilities, medical centers, and pharmacies in the Middle East, India, and the Philippines.

4. Dr. Lal PathLabs Ltd

The headquarters of Dr. Lal PathLabs Ltd. is in Delhi, India, and the company was established in 1949. The business offers diagnostic and related medical testing and services on a global scale. Currently, the firm operates several laboratories and provides a variety of additional health examinations and tests.

Conclusion

Applications of data science in the healthcare system are numerous. Patients and doctors are increasingly making use of this technology’s potential. Data scientists play a crucial role in the healthcare system, and you ought to be qualified for this position. Additionally, you can advance your career in this industry by completing online data science courses. 

At Online Manipal, you can find the best online data science courses from the prestigious Manipal Academy of Higher Education (MAHE). Graduates of this data science program will be well-equipped to take the next step in their careers. Thanks to this curriculum, you’ll gain the technical know-how to stand out in data science. The program aims to equip you with the analytical and leadership skills necessary to succeed in various fields by combining machine learning, big data analytics, and statistics. 

Head to Online Manipal to learn to apply machine learning and predictive modeling, work effectively in teams, create strategic and tactical suggestions, and manage processes, projects, and people using real-world data.

Key takeaways:

• Medical imaging, drug development, genomics, predictive diagnosis, and other areas of healthcare rely heavily on data science.
• Medical facilities can use data science and analytics to improve patient care by reducing wait times for diagnosis and delivering more effective, safer treatments.
• Undoubtedly, future data science applications will be more beneficial than current ones, as they are already doing wonders for society.