Benefits of data analytics in healthcare sector
As healthcare advances, the amount and complexity of data it generates keeps growing. If used well, this data can seriously improve patient care and how operations run. Get a better grip on healthcare data analytics, find ways to tackle the challenges of putting it into action, and see how data analytics is changing the healthcare field. However, implementing data analytics in healthcare is not without challenges, such as data security, standardization, sparsity and data structure. In this article, you will learn more about the benefits and challenges of healthcare data analytics, and how it can revolutionize the healthcare industry.
What is data analytics
Data analytics is the process of studying raw data to spot trends and make smarter decisions. This applies to all kinds of organizations, especially in healthcare.
The world is getting flooded with data:
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Between 2010 and 2020, data grew by 5,000%.
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Every three years, the yearly data total doubles, and it’s estimated to hit 79 zettabytes in 2021.
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Netflix uses predictive analytics to impact 80% of movie and TV show choices for its 100 million subscribers.
Type of data analytics
There are four types of data analytics:
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Descriptive analytics: This tells what happened in a certain period of time. Did the views go up? Are sales stronger this month than last?
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Diagnostic analytics: This explains why something happened. It involves more data sources and some hypotheses. Did the weather affect beer sales? Did the latest marketing campaign impact sales?
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Predictive analytics: This predicts what is likely to happen in the near future. What happened to sales the last time we had a hot summer? How many weather models forecast a hot summer this year?
A 2021 study by a University of Michigan research team shows the positive impact that predictive analytics can have on patient treatment. The researchers developed a sensitive blood test that predicted how well patients with HPV-positive throat cancer would respond to specific treatments. The researchers found that their method could predict treatment effectiveness months earlier than traditional scans -
Prescriptive analytics: Use of historical data to suggest an appropriate action. In health care, prescriptive analytics is used to guide business decisions and to prescribe treatment plans for patients. Some of the common uses of prescriptive analytics in healthcare are identifying a patient’s risk of developing diabetes, allocating ventilators for a hospital unit, and improving diagnostic imaging tools.
Data analytics in healthcare
Healthcare data analytics is crucial for evaluating practitioners, spotting abnormalities in scans, and predicting disease outbreaks. The industry is full of valuable data, mandated by regulations to be kept for a specific time. This has made healthcare a focus for “big data” experts, handling huge amounts of unstructured data.
Benefits of data analytics in healthcare
Optimizing operation and management
Healthcare providers have embraced various strategies to boost operational efficiency and uphold superior care standards:
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Waste reduction. The U.S. healthcare industry grapples with an annual $1 trillion recoverable waste expense. Utilizing big data intelligently could eradicate 50%–75% of these costs, potentially saving $500 billion to $750 billion, according to Datavant CEO Pete McCabe. Through comprehensive analysis, healthcare organizations can pinpoint areas for enhanced efficiency and cost-cutting measures. Predictive analytics, for instance, accurately predicts medical supply demands, aiding administrators in optimal purchase decisions.
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Cost-effective use of technology. Integrating analytics and advanced technologies demands substantial investment and time. Implementing data analytics involves focusing on operations that promise superior outcomes, fostering a cost-effective framework centered on measurement, comprehension, and enhancement.
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Increasing hospital capacity. Instead of constructing new facilities, healthcare providers are employing analytics techniques to better manage bed demands and other healthcare resources, offering a cost-efficient alternative.
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Sustaining outcome improvements. Efforts to implement data-driven enhancements to healthcare operations often underestimate the resources needed to sustain the improvements. To ensure the long-term success of analytics efforts, organizations should engage all stakeholders at every stage of the process.
Supporting the work of medical personnel to deliver better diagnostics
Data analytics and intelligent data solutions are pivotal in supporting healthcare professionals in several ways. For instance:
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Enhanced Diagnosis and Treatment: Comparing current medical cases with past ones aids doctors in refining diagnoses and treatments, resulting in improved patient outcomes.
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Early Disease Detection: Data analytics enables the timely identification of diseases, facilitating prompt medical interventions for better patient care.
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Epidemiological Risk Control: Analyzing data helps in identifying and controlling pathogenic risks, contributing to better public health outcomes by managing potential disease outbreaks.
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Predictive Medicine: By analyzing data on common diseases, predictive analytics identifies high-risk patients, enabling personalized medicine and tailored health management for both individuals and communities.
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Proactive Healthcare: Patient profiles analysis helps identify candidates for preventive measures, encouraging proactive lifestyle changes and preventive care strategies.
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Disease Prediction: Data analytics plays a vital role in foreseeing disease occurrences, progression, complications, and their influencing factors, aiding in better patient care.
While data analytics is instrumental in healthcare, its effectiveness hinges on data quality, making high-quality data an indispensable factor for reaping these benefits.
Enhancing patient care and experiences
Through patient data analysis, healthcare providers can diminish readmission rates, minimize errors, and effectively pinpoint at-risk groups. Utilizing various patient data such as blood sugar levels, temperature, blood test outcomes, and individual care preferences contributes to these evaluations. The primary factors shaping the care quality patients receive encompass:
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Patient-Centric Health Goals: Understanding patients’ anticipated health outcomes and their priorities in healthcare decisions.
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Impactful Healthcare Procedures: Assessing how healthcare provider methods influence the desired health results of patients.
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Healthcare Infrastructure and Resources: Evaluating how facilities, equipment, regulations, and other healthcare infrastructure elements influence the overall quality of patient care.
Supporting scientific and research activity
Data analytics techniques are being utilized to enhance research endeavors in various health-related domains through the collection and examination of clinical data from diverse origins. Essential sources of clinical information encompass Electronic Health Records (EHRs), electronic medical records, personal health records, and public health records.
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EHRs combine a patient’s X-rays and other medical images, diagnoses, treatment plans, allergies, and test outcomes into standardized digital formats, facilitating easy data sharing. However, these formats impose privacy and regulatory constraints that limit data usage.
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Electronic medical records, similar to EHRs, encompass details from a patient’s paper charts created in medical facilities and are primarily used for diagnosis and treatment. Their significance lies in tracking a patient’s healthcare across multiple visits and screenings over time.
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Personal health records maintain a patient’s health treatment history, intended for personal health management without replacing the legally maintained medical records by healthcare providers.
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Public health records represent a promising resource for medical research. Notably, the National Cancer Institute’s Cancer Research Data Commons (CRDC) serves as a cloud-based platform linking data analytics tools with repositories storing diverse data types such as genomics, proteomics, comparative oncology, and imaging data.
Challenges
Data Structure Issues
In the healthcare sector, a major portion of data remains unstructured, commonly derived from natural language processing. This data tends to be fragmented, scattered, and lacks uniformity. Challenges persist in gathering and analyzing unstructured data, even within a single organization. Notably, Electronic Health Records (EHRs) face issues with interoperability among different entities, complicating data sharing and analysis. Consequently, unstructured data poses a considerable obstacle for healthcare professionals aiming to utilize data analytics for enhancing patient outcomes and operational efficiency within healthcare systems.
Missing Data and Data Sparsity
Within healthcare, absent Electronic Medical Record (EMR) data can stem from inadequate data collection or documentation lapses. Inadequate collection arises when patients aren’t screened for specific medical aspects, while documentation gaps occur when patients are screened, but negative outcomes remain unrecorded due to human error or lack of documentation. Additionally, data sparsity worsens the issue as patients have few hospital visits, resulting in limited recorded medical features.
However, addressing missing data and sparsity involves various techniques like imputation. These methods fall into two categories: those assuming random data absence, including simpler approaches like mean imputation and more complex methods like maximum likelihood and multiple imputations, and those assuming non-random absence, such as selection models and pattern-mixture models.
Security Issues
Implementing data analytics in healthcare raises considerable privacy concerns, notably due to legislation like the Health Insurance Portability and Accountability Act (HIPAA). Exposing healthcare data on open-source platforms amplifies its vulnerability to potential breaches. The sensitive nature of healthcare data further intensifies confidentiality worries. Centralized data storage increases susceptibility to cyber-attacks. Consequently, prioritizing data privacy and security remains paramount when deploying healthcare data analytics solutions.
Data Standardization Issues
Healthcare organizations commonly rely on Electronic Health Record (EHR) systems to manage patient data within their networks. However, the lack of standardization and compatibility among EHR platforms within and across organizations presents a substantial challenge for data analytics.
Data stored in non-uniform formats and the lack of standardization complicate the acquisition and cleaning of data. This lack of uniformity limits interoperability, a significant challenge for healthcare data analytics. Standardizing data is vital for global sharing and analysis, yet the diversity in language, standards, and terminologies remains a hurdle.
Various standards development organizations, such as Health Level Seven International (HL7), developed standards that provide for global health data interoperability with the goal of solving the lack of data standardization in healthcare. HL7 standards, such as FHIR (Fast Healthcare Interoperability Resources), which has shown a significant adoption rate, promise to bring healthcare organizations to a single data standard, thus eliminating data standardization issues.
Several standards development organizations, such as Health Level Seven International (HL7), have developed standards to facilitate global health data interoperability with the goal of solving the lack of data standardization in healthcare. HL7 standards such as FHIR (Fast Healthcare Interoperability Resources) exhibit significant adoption rates and aim to establish a unified data standard across healthcare organizations, potentially resolving data standardization issues.
See more: How We Successfully Migrated a Dataset of 2 Million Patient Records To The Cloud
Lobster Technologies solutions
Healthcare’s integration with data analytics holds significant promise for enhancing personalized patient care and tackling emerging health challenges. The increasing availability of diverse data and innovative strategies to address analytics implementation hurdles have broadened the role of data analytics in healthcare. This expansion has notably contributed to improved patient outcomes and the enhancement of medical procedures and treatment quality.
Are you looking for guidance with your next big data analytics project? Lobster Technologies team previously worked on data analytics solutions for healthcare and is ready to help you realize the full potential of intelligent data. Get in touch with us today to schedule a free project evaluation.