Analytics used in Healthcare and Medicine
Healthcare Analytics refers to the use of vast amounts of collected data to provide organizations with actionable insights. These insights are developed through analytical disciplines to drive fact-based decision making. In turn, these decisions improve planning, management, measurement and learning.
As healthcare organizations around the world are challenged to reduce costs, improve coordination with care teams, provide more with less, and focus on improving patient care, analytics will be especially important. Primary care physician and nursing shortages are requiring overworked professionals to be even more productive. Plus, new businesses entering the market and new approaches to healthcare delivery will increase competition in the industry. Building analytics competencies can help healthcare organizations harness big data to create actionable insights that can be used by healthcare providers, hospital and health system leaders, and those in government health and human services to improve outcomes deliver value for the people they serve.
As tumultuous as the current healthcare environment is, it’s expected to become even more complex over the next several years. Challenges such as evolving market dynamics, increasing governmental regulation and more demanding consumers will require smarter, more informed decisions from organizations so they can remain competitive and deliver value in their communities.
There are Four (4) Key Types of Data Analysis:
- Descriptive Analysis, which examines and describes something that’s already happened
- Diagnostic Analysis, which seeks to understand the cause of an event
- Predictive Analysis, which explores historical data, past trends, and assumptions to answer questions about the future
- Prescriptive Analysis, which identifies specific actions an individual or organization can take to reach future outcomes or goals
Descriptive Analytics can be used to determine how contagious a virus is by examining the rate of positive tests in a specific population over time. Diagnostic analytics can be used to diagnose a patient with a particular illness or injury based on the symptoms they're experiencing.
Diagnostic Analytics can be used to diagnose a patient with a particular illness or injury based on the symptoms they’re experiencing.
Predictive Analytics can be used to forecast the spread of a seasonal disease by examining case data from previous years.
Prescriptive Analytics can be used to assess a patient’s pre-existing conditions, determine their risk for developing future conditions, and implement specific preventative treatment plans with that risk in mind.
Analytics Potential Pitfalls to Avoid
While data analytics has the power to drive positive change, it can create and perpetuate issues.
When handling patient data, keep in mind that it’s sensitive, Personally Identifiable Information - PII and must be protected at all costs. As a medical professional or administrator, it’s your responsibility to keep your patients’ information secure while improving their health and well-being.
It’s also important to catch and correct Biases in Artificial Intelligence - AI, Machine Learning - ML, Deep Learning, Natural Language Processing - NLP, Natural Language Understanding - NLU, Natural Language Generation - NLG, Neural Machine Translation - NMT/MT, Computer Vision and Machine Vision algorithms when analyzing data. Because people write algorithms, they often display Human Bias.
With Ethnic Group (race), Gender, Sexual Orientation, Socioeconomic Status, and Geographic and other Demographic factors contributing to unequal health care access, algorithms meant to draw conclusions about the population as a whole may not receive data from certain minority groups and generate inaccurate, potentially damaging conclusions.
Artificial Intelligence - AI, Machine Learning - ML, Deep Learning, Natural Langue Processing - NLP, Natural Language Understanding - NLU, Natural Language Generation - NLG, Neural Machine Translation - NMT/MT, Computer Vision and Machine Vision algorithms learn based on the data they’re given, so ensure your data comes from a truly representative sample comprised of various demographics before drawing conclusions.