{"id":921,"date":"2022-04-04T10:25:00","date_gmt":"2022-04-04T10:25:00","guid":{"rendered":"https:\/\/healthanalyticinsights.com\/?p=921"},"modified":"2022-03-30T00:06:48","modified_gmt":"2022-03-30T00:06:48","slug":"what-is-the-role-of-data-analytics-in-healthcare","status":"publish","type":"post","link":"https:\/\/healthanalyticinsights.com\/?p=921","title":{"rendered":"What is the Role of Data Analytics in Healthcare?"},"content":{"rendered":"\n

Data cleaning is usually what takes up the largest portion of an analyst’s time. Some articles<\/a> report, 80% of an analyst’s time is spent in the data cleaning stage, whereas 20% is spent on the “fun” analytical work. This can include creating complex machine learning algorithms or interactive dashboards and reports.

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Totally accurate representation \ud83d\ude05<\/figcaption><\/figure>\n\n\n\n

This can be a result of multiple reasons. For instance, imagine if you want to collect survey data on

“What were the barriers you encountered when trying to obtain care in this hospital? <\/em>

If we are looking at collecting qualitative data, this can result in a host of data entry errors, from large volumes of missing entries, people not entering the information correctly, people not interpreting the survey questions correctly etc… Trying to transform large swaths of data into actionable information is not easy and there are many areas where there can be a breakdown in this process. <\/p>\n\n\n\n

In this YouTube video, I went through some typical data cleansing issues you might run into: <\/p>\n\n\n\n

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