Imagine your health care organization is challenged with excess readmissions to the hospital. Suddenly, you find yourself on a vendor selection team to evaluate predictive analytics that will help your care management team be more proactive in transition of care planning for your patients.Read More
What causes your organization’s data to go bad? And what steps can you take to maintain data integrity?
Welcome to the final part of our three-part series on data quality. In a previous post, I discussed what it means to have fit data and reviewed six dimensions of data fitness: completeness, correctness, timeliness, uniqueness, format validity and acceptability. Remember, data fitness relates to how fit each of your organization’s data elements are for their ultimate purpose.Read More
How fit is your data? And what does it mean to have ‘fit’ data?
To understand data fitness, you need to first have a good understanding of data quality. A helpful and well-adopted definition of data quality throughout the data quality industry is the fitness to the purpose of use. In other words, the way that you use certain measures, analytics or reports defines its quality or integrity. So when it comes to evaluating your health care organization’s data fitness, you need to think about how fit each data element is for its ultimate purpose.Read More
Reaching a high level of trust in your organization’s performance measurement data is dependent on people, processes and technology all working together to ensure the highest levels of data quality possible. Data quality is crucial for properly measuring, managing and improving your organization’s clinical or financial performance…and it’s a lot like preparing a meal.Read More
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