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A Brief “How-to” Essay with Commentary
By Staff Reporters
According to a Standish Group survey, more than 70% of Health Information Technology applications go over budget and time and medical data warehouse applications are no exception. However, if you adopt a process, an oriented development approach and implement a rigorous project management discipline, your increase the likelihood that your medical data warehouse will be effective.
Key Steps
This is a simplified list, but reveals some of the key steps needed to build a medical data warehouse.
- Extracting data from the data sources – can be very challenging as data might reside on different systems and this forces you to prioritize what data you want and what role that plays in your patient relations management decision-making. This step involves moving data from the source (for example to your Web site) to a central location (e.g. your marketing data mart).
- Transforming the data – a key activity after data extraction. This is critical to have cleaner data and involves modification, enhancement or elimination of data based on the job instructions.
- Loading the transformed data into a dimensional database.
- Building reports for decision makers (e.g. this could be a report for your marketing management outlining the analysis of your latest patient acquisition campaign).
Assessment
The first 3 steps – Effective data extract, transform and load (ETL) processes represent the number one success factor for your medical data warehouse project and can absorb up to 70 percent of the time spent on a typical warehousing project.
Conclusion
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Filed under: "Doctors Only", Information Technology, Practice Management | Tagged: health information technology, Heath Data Warehouse |
The Leading Business Education Network for Doctors, Financial Advisors and Health Industry Consultants 
More on Building an e-Health Data Warehouse
[Remember to add medical data incrementally]
Even in a small medical practice with few employees you will be amazed to know how fragmented data can be and in how many data islands it resides – medical charts, op reports, patient brochures, e-mail contact lists and address books, spreadsheets and so on. It can be an overwhelming task to gather all this information in a central electronic repository. It is best if you take an incremental approach to integrate the patient data available in electronic format and then proceed to more manual and laborious components such as business cards, paper lists. Healthcare organizations may house patient data in central repositories thereby providing the enterprise with a memory but it is data mining that adds intelligence to the enterprise.
And so, to convert information into insight through data mining, ask yourself the following questions:
• How good is my data? – A healthcare provider who sends the company brochure to its long standing patient to sign up for its services certainly needs to ask itself this question.
• Application of models to new data – when you “model” data using data mining techniques to obtain patient insights, beware that the data you model could be different from the data you are going to apply this model to.
• How long can you use your model? The ability of a “model” to classify, estimate, predict diminishes over time. Therefore your models need to be maintained and enhanced requiring ongoing maintenance
• How long does the prediction stemming from data mining remain viable?
• Interpreting the results – can be a source of unexpected challenges. This task requires experts who can not only understand and interpret the results for you and what they mean to you but also be objective and be consistent
• What is our privacy policy and strategy? – It is imperative for healthcare organizations to be proactive and self-regulate with a coherent privacy policy and design their systems to comply with this strategy. This will affect the way you manage, mine and communicate data.
• What tools should I use? – There are several data mining techniques available today, decision trees, on-line analytical processing (OLAP) queries, neural networks etc. but no one technique solves all business problems. Commercial software may be linked to particular data mining technique(s).
Finally, you need to decide what works best for your technical/ business environment? Is any integration effort required, and if yes, how much will it cost me? How user friendly are the tools and how much should I invest in training?
Dr. David Edward Marcinko MBA
http://www.BusinessofMedicalPractice.com
[Editor-in-Chief]
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Electronic Health Records as a Predictor of Physician’s Electronic Exchange Capability
1. 55 percent of all physicians had computerized capability to send prescriptions electronically vs. 78 percent of physicians with an EHR.
2. 67 percent of all physicians could view electronic lab results vs. 87 percent of physicians with an EHR. than among younger (30.3%) or older (35.4%) adults.
3. 3.42 percent could incorporate lab results into their EHR vs. 73 percent of physicians with an EHR.
4. 35 percent could send an electronic order to a lab vs. 54 percent of physicians with an EHR.
5. 38 percent could provide clinical summaries to patients vs. 61 percent of physicians with an EHR.
6. 3 1 percent exchanged patient clinical summaries with another provider vs. 49 percent of physicians with an EHR.
Source: U.S. Department of Health & Human Services
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