Transforming Healthcare with Data: Improving Patient Care and Efficiency

By: Harshit Chandrol

Image courtesy of beachmobjellies; CC-BY-SA 2.0 DEED.

The world is being inundated with data:

  • Data is set to grow with a five-year compound annual growth rate (CAGR) through 2024. It will surpass the data created in the past 30 years in the next three years. A remarkable surge in data creation is underway (1).
  • By 2025, global data creation is projected to grow to more than 180 zettabytes which is a 280% increase from 2020. (2)
  • Despite the vast data generated, a mere 5% from 2022 persisted into 2023. The installed base of global data storage capacity is expected to increase from 6.7 zettabytes (ZB) in 2020 to around 16 ZB in 2025. (3)

Raw data, akin to natural resources, gains value through refinement for practical use. Advanced analytics techniques are pivotal in transforming data into a valuable commodity. Data analytics involves scrutinizing data to identify trends and uncover insights that might not be apparent otherwise. It transforms raw data into business intelligence, benefiting all industries. The healthcare sector, particularly, stands to gain from analytics. Allied Market research notes global healthcare analytics had a $37.15 billion valuation in 2022 and is projected to reach $121.1 billion by 2023. It is growing at a CAGR of 15.9% from 2021 to 2030.

Data analytics will alter healthcare delivery in areas as diverse as cancer therapy, medication research, and disease prediction. The use of advanced analytics techniques, as well as the continued geometric growth in the amount of data available for analysis, will have an impact on nearly every aspect of healthcare, including the accuracy of insurance rates, the automation of administrative processes, and the use of artificial intelligence (AI) in diagnostics.

What are data analytics in health care?

The healthcare business creates a massive quantity of data, but it struggles to translate that data into insights that enhance patient outcomes and operational efficiencies. Data analytics in healthcare is meant to assist clinicians in overcoming (4) barriers to the broad use of data-derived intelligence, such as:

  • Making healthcare data more accessible to colleagues and external partners, as well as more visually appealing for public consumption.
  • Providing accurate data-driven projections in real time enables healthcare providers to adapt to changing healthcare markets and surroundings more swiftly.
  • Increasing data collaboration and innovation across healthcare companies to turn analytics-ready data into business-ready information through the automation of low-impact data management chores.

Analytics tools are classified into three categories:

  • Software that collects data from various sources such as patient questionnaires, case files, and machine-to-machine data exchanges.
  • Data cleaning, validation, and analysis programs that work in response to specific research.
  • Software that uses the analysis results to recommend various activities to attain specified healthcare goals.

How is data analytics applied in healthcare?

Data analytics in healthcare can be used to improve patient care and operational management in a variety of ways. The capacity of data analytics to transform raw healthcare data into actionable insight is projected to have the biggest influence on the following sectors of healthcare (5):

  • Disease research and prediction
  • Hospital administrative tasks can now be automated.
  • Disease identification at an early stage
  • Avoiding needless doctor’s appointments
  • The development of novel medications
  • More precise health insurance rate calculation
  • More efficient patient data exchange
  • Patient care personalization

Using a library of molecular and clinical data (6) to provide healthcare practitioners with a clearer clinical context for a cancer patient’s individual situation and public health experts using data analytics to increase illness prevention and identify vulnerable populations are examples of applying data analytics in the health sector.

Who oversees data analytics for healthcare applications?

Researchers, clinicians, hospital managers, and insurance sector executives are among those responsible for undertaking data analytics in diverse healthcare contexts. The three most prevalent groups of healthcare practitioners who employ data analytics in healthcare are:

  • Practitioners of clinical medicine: In clinical settings, data analytics attempts to reduce patient wait times through improved scheduling and staffing, give patients more options when scheduling appointments and receiving treatment, and reduce readmission rates by using population health data to predict which patients are at greatest risk.
  • Healthcare Payers:  Insurance companies employ data analytics to ensure that they comply with constantly changing rules, analyze claims and prescriptions to target common health issues, and compare price data with quality indicators to discover high-value, low-cost health providers. Insurance companies also employ predictive analytics to detect fraudulent claims and inform providers of at-risk claims.
  • Managers of population health: Public health professionals are increasingly emphasizing prediction and prevention measures above response and treatment. Predictive analytics is used to identify people at high risk of chronic illness early in the disease’s progression. Analysis of lab tests, claims data, data generated by patients, and numerous societal factors lowers the risk of long-term sickness, lowering total healthcare expenditures and improving patient outcomes.

What are the advantages of using data analytics in healthcare?

When analytics techniques are used in the health sector, many various types and formats of data are merged. Electronic health records (EHRs), genomics and post-genomics, bioinformatics, medical imaging, sensor informatics, medical informatics, and health informatics are all examples of health-related data sources.

The first advantage is that clinical data may be analyzed for better medical research. By obtaining and analyzing clinical data from diverse sources, data analytics techniques are being used to boost research efforts in numerous health-related domains (7). EHRs, electronic medical records, personal health records, and public health records are among the most important sources of clinical information.

EHRs digitize a patient’s X-rays and other medical pictures, diagnoses, treatment plans, allergies, and test results. This makes it easier to communicate information, but it also imposes privacy and regulatory compliance obligations that limit how the data may be used (8). EMRs are similar to EHRs in that they contain information from the patient’s paper charts maintained in medical offices, clinics, and hospitals. They are largely used for diagnosis and therapy, with the major benefit of following a patient’s health through years of visits and screenings (9).

The objective of quality improvement in healthcare settings is to treat patients safely and effectively while reducing their trauma. To accomplish this goal, healthcare providers collect and analyze patient data, increasingly in real-time, to gain a better understanding of today’s complex healthcare environments; develop and apply a systematic approach to improving patient outcomes; and continuously develop, test, and implement improvements to healthcare processes.

By evaluating patient data, healthcare professionals may minimize readmission rates, reduce mistakes, and better identify at-risk populations.

Healthcare workers encounter several problems in acquiring timely access to patient data that might assist them in improving the quality of their services. To avoid getting overwhelmed with patient data, focus on a few essential performance indicators and collect only the data required to track those measures. This necessitates doing qualitative analyses of patient data and ensuring that the metrics are available to clinicians and trackable over time to identify areas for improvement.



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