Physician Data | Hospital Events | Operational Analysis | Data Analysis | Reports
Data Collection | Analysis Sectors | Lectures
Data analysis is a complex process that starts with initially defining data elements to the final information driven decision-making. Many methods for handling and analyzing data are often grown organically, and organizations never know if their processes are sufficient or even effective.
Hamilton Numbers can help your healthcare organization with all aspects of data collection and analysis.
Physician data- Can your organization measure the difference between physicians or physician groups? Medically? Financially?
- Does your organization know when a particular physician's medical practices differs significantly?
- Can your organization measure the effectiveness of your physicians or physician groups?
- What sample size do you need to measure and predict the occurrence of events?
- With what accuracy can your organization predict falls? infections? medical errors?
- Do you know if the increase (or decrease) in events is statistically significant (in other words, not random)?
- Can you predict extremely rare events? or calculate the risk of rare events in your organization?
- Is the number of beds in your ICU sufficient? How much down time with the addition of another bed create? Is the number of beds optimal?
- What is the maximum potential wait time for your ER? ICU? Outliers can have a significant effect on averages, which are often published numbers.
- Can you predict the amount of idle time for your ER staff? How will the addition of a new staff member affect the overall wait.
- Does your organization use OLAP? statistics? predictive analytics? data mining?
- Do you use publicly available data sets for data comparisons?
- Who chooses the key process indicators? Are they statistically validated?
- Do you use the most current technology available for displaying information?
- What is the graphical language in your organization. Is it standardized and well understood?
- Are your reports part of an established overall process?
- How is your organization collecting and structuring data?
- Can your organization trace a key process indicator to its source?
- Is the quality of your data based on standard practices?
Hamilton Numbers will do retrospective, predictive, and concurrent data analysis. Retrospective analysis involves most traditional methods used to analyze data, and is what most individuals typically consider to be data analysis. Predictive analysis is contemporarily known as predictive analytics. It utilizes the results of restrospective analyses to builds models and predict future events. Concurrent analysis involves the incorporation of concurrent, or 'live,' data to improve the accuracy of predictive models, but may also be used for monitoring systems and event recognition.
Hamilton Numbers will work with large, small, simple or complex datasets or databases. Solutions usually depend on a combination of several factors. The data structure and the desired information are first and foremost, but often particular analyses and solutions are predetermined by the business domain. For example, solutions relating to market basket analysis naturally apply to purchasing behavior.
When applicable, and especially with data mining and model building, we use the CRISP-DM (www.crisp-dm.org) methodology in our analysis.
Analysis services offered by Hamilton Numbers includes:- Statistical Analysis
- Data Mining
- Graphical Analysis
- Exploratory Data Analysis
- Distribution Analysis
- Time Series Analysis
- Mathematical Modeling
- Optimization Methods
- Quality Control Methods
- Data Structures
- Statistical Analysis
- Predictive Analytics
- Data Mining
- Graphical Analysis
- Time Series Analysis
- Mathematical Modeling
- Quality Control Methods