Health care facilities can use big data to capture a comprehensive picture of a patient. With the use of big data analytics, health care providers will be able to make better decisions about patient care and resource allocations to improve outcomes and reduce costs. With the wealth of information data analytics provides, administrators can make better administrative and financial decisions while delivering quality patient care.
If the first letter of your last name begins with
A through M, complete population management.
N through Z, complete value-based care.
In Part 1 of your initial post,
Differentiate between predictive analytics and prescriptive analytics.
Explain the meaning of big data and its importance in data analytics.
Describe how big data and data analytics can support value-based care or population management.
Part 2
Data governance is critical to the information technology (IT) governance in a health care organization. Data governance deals with the daily use of data to accomplish patient care. Like IT governance, data governance comprises processes, policies, metrics, roles, and standards that ensure the use of data and information effectively and efficiently.
In Part 2 of your initial post,
Analyze the differences in data governance structures for projects focusing on revenue cycle and quality of care.
Appraise at least two common pitfalls in data management initiatives described in Chapter 11 of the required text.
Sample Answer
Part 1: Analytics, Big Data, and Value-Based Care
Differentiating Predictive and Prescriptive Analytics
Predictive Analytics: Focuses on forecasting future probabilities and trends based on historical data. It answers the question, "What is likely to happen?" In healthcare, this could be predicting a patient's risk for readmission, the probability of a specific disease onset, or future resource needs.
Prescriptive Analytics: Focuses on recommending the optimal course of action to achieve a desired outcome. It answers the question, "What should we do?" This type of analysis uses the findings from predictive models and applies rules, algorithms, and decision models to suggest interventions, such as adjusting a specific patient's treatment plan or optimizing the staffing schedule.
Understanding Big Data and its Importance
Big Data in healthcare refers to extremely large, complex, and diverse sets of information that traditional data processing applications are inadequate to deal with. It is often characterized by the "Four Vs":
Volume: The vast quantities of data generated (e.g., billions of lab results, imaging files, and EHR entries).
Velocity: The speed at which data is created, collected, and processed (e.g., real-time monitoring from wearable devices).
Variety: The diverse types of data sources (e.g., structured EHR data, unstructured physician notes, images, genomics, and social media data).
Veracity: The quality and trustworthiness of the data, which can be inconsistent or incomplete.
Importance in Data Analytics: Big data is the necessary fuel for healthcare data analytics. Without the scale, speed, and diversity of big data, analytics cannot capture the comprehensive patient picture needed to identify subtle patterns, detect complex correlations, and build robust, accurate predictive models. It allows analysis to move beyond simple retrospective reporting to sophisticated, forward-looking insights.
Big Data and Data Analytics in Value-Based Care (VBC)
Value-Based Care (VBC) is a payment model that rewards healthcare providers for improving patient health outcomes while reducing the total cost of care, shifting the focus from the volume of services provided (Fee-for-Service) to the quality and efficiency of care.
Big data and analytics support VBC by:
Measuring Outcomes and Costs: Analytics tools continuously measure and attribute the cost of an episode of care against the clinical outcome achieved. This allows administrators and clinicians to identify high-value pathways and eliminate waste.
Risk Stratification and Care Coordination: Predictive models use big data (EHR, claims, demographic) to identify patients at the highest risk for adverse events, chronic disease progression, or high utilization. This allows VBC organizations to proactively assign care managers, ensuring high-risk patients receive coordinated, preventative interventions, thereby improving outcomes and avoiding expensive acute episodes.