Professional Certificate in
Hospital Analytics Course
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- 16 Hours of Intensive Classroom & Online Sessions
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The advancement of Data Analytics has changed the way businesses are done around the world. With the rise in data, analytics organizations can now make informed decisions with the huge wealth of data available. There are more than 6 million developers around the world who are working on data and with all the resources it will be a huge benefit for organizations in taking profitable insights.
Overview of Hospital Analytics Course
One of the fields that are gaining a lot from Data Analytics is Hospital Analytics. The adoption of hospital analytics is proving a great asset to the domain. Smart use of data in hospitals will help them in not only can the smart use of data make hospitals run more economically and profitably it can aid in saving lives and patient care. 360DigiTMG Hospital Analytics course will introduce you to all the latest technological advancements that will help you strategize and provide insights for the profit of the organization.
Hospital Analytics Course Training Learning Outcomes
The field of hospital analytics has been revolutionized by the integration of Machine Learning and Big Data Analytics. Our certification program has been designed specifically for hospital professionals and data professionals who want to learn how to leverage the power of big data analytics, machine learning, neural networks, and deep learning in the hospital industry. This comprehensive course provides an in-depth understanding of how data science and artificial intelligence can be applied to the hospital industry to improve productivity, increase profits, and make data-driven decisions. Through this content-rich program, you will gain a comprehensive understanding of the applications of data science and machine learning in the hospital industry, making it an essential program for anyone looking to excel in the field of hospital analytics.
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Who Should Sign Up?
- IT Engineers: Infrastructure Optimization
- Data and Analytics Manager: Data Management
- Business Analysts: Performance Management
- Data Engineers: Data Processing
- Healthcare Data Analyst: Patient Insights
- Healthcare Business Analyst: Operational Efficiency
- Hospital Administrator: Resource Management
- Clinical Analyst: Clinical Outcomes
- Chief Medical Information Officer (CMIO): Health Information Management
- Health Information Manager: Health Data Management
- Healthcare IT Specialist: Healthcare IT Management
- Healthcare Data Scientist: Predictive Analytics
- Healthcare Consultant: Business Strategy
- Quality Improvement Manager: Quality Management
Hospital Analytics Course Modules
- Introduction to automation in Hospital management systems.
- Use of analytics to analyze electronic health records (EHR).
- Analysis of evidence-based medicine deployment systems.
- Use of analytics for reducing hospital readmissions.
- Use of analytics to reduce waiting times and length of stay.
- Effect of patient care and other facilities on hospital rating.
- Schedule automation.
- Introduction to the various stages of analytics and using an historical example of the various stages can help understand the steps involved in analytics
- Discussion on CRISP-ML (Q)
- Explanation on the importance CRISP-ML (Q) in various Data Science related projects
- Explanation on unsupervised and supervised learning
- Explanation on the training, validation, and testing stages of supervised learning
- Understanding right fit, underfit and overfit scenarios in supervised learning
- Understanding Hyperparameter tuning in the context of overfit
- This module covers the various steps of EDA
- Discussion on the 4 business moment decisions in EDA
- Discussion on univariate, bivariate, and multivariate EDA steps
- Explanation of the various pre-processing steps involved in any Machine learning or Artificial intelligence project
- Brief introduction to feature engineering
- Re-cap of unsupervised learning in ML
- Understanding of clustering in layman’s terminology
- Understanding K-means clustering from a technical perspective
- Understanding the usage of elbow curve and silhouette scoring to decide on the ideal number of clusters
- Use Case: Clustering of nursing facilities using K-means clustering.
- Re-cap on supervised learning
- Understanding of nearest neighbor in ML
- Understanding KNN algorithm and distance metrics
- Understanding of early stopping point in training phase
- Explanation of the need for odd number of neighbors when classifying
- Use Case: Prediction of hospital ratings based on facilities and patient care using KNN.
- Re-cap on Decision Trees
- Understanding the various components of a decision tree
- Understanding the idea behind root node and how it affects the overall tree
- Understanding entropy and information gain concepts of decision tree to make logical choices on root node
- Usage of hyperparameter tuning to optimize the tree
- Use Case: Predicting hospital readmission and recommending approaches to reduce readmissions based on analytics.
- Introduction to the concept of line equation
- Correlation of line equation in ML terms
- Understanding of correlation and its importance in Linear regression
- Differentiating between the equations of SLR and MLR
- Explanation on the OLS concept
- Use Case: Staff schedule prediction based on the number of available staff using linear regression.
- Re-cap on the topics covered before
- Understanding the importance of AI
- Understanding the reasoning behind the concept of neural networks
- Explanation on the perceptron algorithm
- Introduction to multilayer perceptron
- Understanding the concept of weight calculations
- Brief intro in gradient descent
- Use Case: Predicting length of stay in the hospitals and recommending solutions to minimize the length of stay based on analytics.
Trends in Hospital Analytics Course
Hospitals have always held great importance in today's pandemic-prone world. Well, the functioning of hospitals is vital as they handle life-and-death situations daily. With the use of hospital analytics software, the daily functioning of hospitals will become more intuitive and efficient.
Cutting down administrative costs
With the help of Hospital Analytics, the pharmaceutical and data analytics team can help medical institutions in streamlining budgets and make changes that are sustainable and safe.
Suppressing fraudulent behavior
Fraudulent behavior can be quashed if the institutions can have a history of individual patients. They can cut down costs and use time properly. A proper Data Analytics tool can achieve the best solution possible.
Improved patient wellness
Data Analytics can assist hospital staff in recognizing the novel treatment for the patient with their detailed medical history and can provide great patient aftercare with the help of their medical history.
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