Vicky Mahn-DiNicola, RN, MS, CPHQ

Vicky is the Vice President of Clinical Analytics and Research at Medisolv. She has over 20 years of clinical analytics and product management experience, as well as a strong clinical background in Cardiovascular and Critical Care Nursing, Case Management and Quality Improvement. She has been successful at partnering with innovative thought leaders and executing strategy for new models of care delivery, case and quality management programs, performance measurement and benchmarking.

Recent Posts

Part One: Diagnosing the Problem of Diagnostic Error

In 1986 my 18-year old sister was in a rollover car accident near Breckenridge, Colorado. She was air lifted to a trauma center in Denver with a closed head injury and a severely fractured left femur. After three days on a ventilator in a medically induced coma, she awoke with no permanent brain injury. Our frightened family was overjoyed. She was still in traction in the ICU to stabilize her femur until we could get her to surgery the following day. 

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Evaluating the Power of Predictive Analytics: Statistics Basics for Clinicians and Quality Professionals

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. 

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Six Root Causes of Poor Data Fitness

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.

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Six Dimensions of Data Fitness

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.

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The Five Stages of Health Care Data Quality Maturity [INFOGRAPHIC]

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.

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