Problems and solutions of health data analysis

in today’s modern world, healthcare providers are aware that detailed knowledge of data is a prerequisite for informed clinical decision-making. However, many organizations still face the challenge of using data analysis tools effectively.
So why is it worthwhile to deal with health data analysis and what problems does it offer solutions to different providers and organizations? Below are some examples of these.


According to a study conducted in an ophthalmic clinic, patient data stored in the electronic health record was only 23.5% consistent with data reported by patients. When patients reported three or more symptoms, those recorded in the electronic record differed sharply from these.
Health data is recorded from a variety of sources and formats – such as photos, videos, handwritten notes, digital records, and more. Many organizations face the challenge of capturing data accurately and aggregated, which also makes it difficult for them to access and use it.
Healthcare providers, employers, insurers, patients and others, all parties to healthcare, collect data in some form, but there is no collaboration between them to make this data available in one central place, concatenated. This, of course, makes things difficult for all parties.


Predictive analytics can be used to generate patient path statements and disease predictions that can create a more efficient, results-oriented healthcare. Increases efficiency, positive outcomes, improves costs and more.
For all this, you will need clean, uniformly formatted, transparently stored and quality data, both from external and internal sources. In order to improve the efficiency of data recording, it is important that you plan in advance the data collections to be carried out during the various projects so that you will have quick and easy access to this information at a later date.


As mentioned above, data from different sources are usually disordered and some of them are never processed. The sharing of electronic health records is therefore also a challenge. It is important that both patients and staff, billing and performance data are recorded.
The vast majority of serious medical errors (roughly 8 out of 10 cases) stem from communication problems during the transition between different stages of care.
For example, some data collections receive new data about a patient’s medical condition at regular intervals. However, other data, such as place of residence, change only a few times during a person’s lifetime. Providers need to know exactly which data needs to be updated manually and which should be recorded automatically. This not only saves time, but also preserves the quality of the data collection.
The unpredictability of data or the frequency of change is more difficult to see for organizations that do not pay attention to maintaining their data collections. When updating the database, make sure that each entry occurs only once, as unnecessary copies can make the work of doctors and other stakeholders more difficult.


To provide a reliable patient experience, machine-learning algorithms need clean, error-free data sets that do not contain multiple entries. This is the only way specialists can get useful, predicted data based on a patient’s previous medical condition.
Healthcare organizations therefore need to learn how to manage data efficiently to get the best possible results with these solutions.


To be transparent and easy to interpret, data often needs to be visualized as interactive graphs and other forms. Of course, if these data come from different sources, it can be a very complex and lengthy job to collect them into a single detection tool. However, data presented transparently can make things much easier for healthcare providers.
Using colors, for example, is a popular data representation technique, as everyone knows that red indicates critical problems, yellow indicates a warning, and green indicates the correct condition.
However, most organizations don’t really go beyond that, and they disregard established data representation techniques such as interactive reporting, labeling information, charts distributed in true proportions, and so on.
Complex flowcharts on which information overlaps only make it difficult to interpret the data accurately and thus make decisions.



Data representation sheds light on key health information, helps to discover correlations and patterns, and makes data analysis more relevant. Interactive reports, graphs, charts, heat maps, and other elements can all be useful in displaying relevant data.



Most organizations prefer to store the data they collect in their own facilities for security and easier access. However, these solutions are often costly, and it is easy for different departments to store their own data independently.
Clinical documents (whether administrative data, patient records, etc.) often have complex wording that can be time-consuming to interpret and process. Processing such documents can be a surprisingly serious challenge – just think of storing paper-based entries as images or Word, possibly PDF files.



With digital document management, healthcare organizations can save money and time. All this allows for more secure data storage and makes the data available to data subjects in one place, in a searchable form.
Thanks to more advanced language recognition technologies, tools are now available that allow doctors to dictate their comments in real time and intelligently select the most important information from them. This data is then available to all other physicians, reducing the potential for error in human communication.

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