Measuring Healthcare Quality Landscape and Its Metrics

The Healthcare Quality Landscape is one that garners a lot of attention and that’s because it is important that every healthcare organization operate with the highest possible quality. Quality measurement is also important because, in order to improve quality, you first have to measure it. You can improve on something that’s not being measured. Below we discuss the various metrics that can be used to measure quality.

Completeness

Completeness as a quality measurement metric is most used in terms of data. Data that is incomplete cannot be of high quality. If there are gaps in the data it means that whatever information is gotten from the data would be incomplete or inaccurate and that communicates low quality. To ensure completeness of data, data should be recorded properly and collected from reliable sources.

Data incompleteness is a commonly occurring problem and it can be resolved in several ways. One way is by ensuring that the data cannot be entered into the database or submitted unless it is complete. So, for example, you cannot submit an online form unless you fill in all the compulsory fields. This is done to ensure data completeness and it also ensures that data is complete when it is gathered and results in less time wasted fixing mistakes revolving around incomplete data. This data collection method is more effective online as with paper forms it is difficult to enforce.

Uniqueness

Unique data is not necessarily high-quality data but more often than not, that’s the case. uniqueness is ensured when the piece of data has only been recorded once. When unique data is present it means that the healthcare organization has insights into something that they previously did not and this translates to more possibilities and higher quality care.

Timeliness

Of what us is great data if you can’t access it when you need it? That is why for data to be regarded as good quality data it has to be timely. The data reporting system has to be frequent, immediate, and exact. If the data is dated and then used for decision making, the quality of what’s produced would not be good and might even be dangerous in some situations. When data is dated it becomes useless if it’s not updated frequently.


Validity

Good quality data must be true and honest. This sounds dramatic but it is very important. Imagine having a data folder that’s supposed to contain dates and then it’s filled with names or random alphabets, that would render it useless. In order to ensure that data is valid, it must be set at the place of entry to only accept what has been deemed valid. So if at data entry it asks for age, a person can only input their age in the specified date format. A good everyday example of this is when you need to choose a password and whatever system you are using keeps insisting on you adding a letter and an alphabet in caps. This is done to ensure the validity of data.

Accuracy

This goes without saying but good data must be accurate. Accuracy determines whether the data and information you hold is correct or not. Keep in mind that data can be valid but not accurate. Just because someone enters a date of birth does not mean that it is their true birth date.

Consistency

Consistent data is good data. When data is consistent it means it can be compared across other data sets and through time. Ensuring the consistency of data ensures that data can be compared with future data sets and past data sets as well.

Data Profiling Explained

No data is perfect and its common to find a point of compromise for the data you want. If you spend time ensuring the data is 100% accurate, you will never achieve what you need to with the data. It’s best to perform regular data audits, cleaning, and updates to all the insights you get from the data can be trusted.

Data profiling is the process of going through and examining data to determine if it is accurate, complete, and/or up to the standard set. It is done mostly when data is being exported, imported or merged with another data set. Data profiling also checks for duplicate and redundant data. In conclusion, data profiling ensures that the data is trustworthy enough for use.