The importance of data in healthcare

As you can see, the healthcare industry is improving very quickly nowadays. Currently, it comes with modern technologies, tools as well as devices in order to gather as much data about patients as possible. There are also other technologies such as machine learning, big data or artificial intelligence, which are all revolutionizing the industry.

Nevertheless, despite all the advantages of new technologies and information sources, it is rather difficult to maintain an effective healthcare data management system while also defining its true value. What’s more, digital protection of patients’ individual data is a big challenge. Actually, we are witness the basis of a really intricate healthcare field.

The key leading to effective patient engagement and satisfaction lies with the data gathered and generated by third parties, as well as the information which is made analysis throughout numerous sources. This will not only increase the care level but also the recognition of a healthcare company. in other words, improving data quality plays an important role. It is necessary for a healthcare center to look for a right place to adopt a right data management strategy with good collection methods and high-quality tools from the beginning when they put their first steps into dealing with data.

  1. Difficulties in healthcare analytics

Issues related to information security and collaboration mean that we need an ultimate solution. Let’s take a look at the following significant problems that may prevent you from generating data effectively.

The first challenge is poor data quality, which also means data actualization. One-off databases turn out to be less effective in terms of generating actual data. This comes from the fact that databases will operate independently from source systems, leading to problems when setting up high-quality data.

Currently, many healthcare providers are taking advantage of Spreadmart to generate necessary data. Nevertheless, this process asks for guarantee which the generated information matches the MRNs. On the other hand, there is an opportunity for a mistype in any of the patient MRNs, when writing them down in the spreadsheet in a manual manner. What is more, some MRNs may alter frequently in the source system. You should consider this.

The next difficulty is intricate collaboration. You can think of a healthcare company which makes use of EDW in order to limit the number of data silos. Excel spreadsheets are the method used to gather and maintain self-created data, although this will lead to another data silo. Consequently, the process of collaboration of the given data silo will become a challenging difficulty.

For instance, you have two people in your company who deal with self-generated data and save it in a spreadsheet. They are free at the end of their working day to enter that data. You deliver to them the spreadsheets which require staff to enter updated records and both of them will make notes themselves. It means that the information will be offered in two different versions. What’s more, you will never be capable of controlling a lot of different corrections and updates and it is not possible to define which information has been changed. Locking the documents and providing approach to the only person who takes the responsibility of that task may seem a good solution. However, this method is not ideal because every person depends on a specific schedule and this may lead to unsuitable use of working time.

The next problem lies with data security. As you can witness, data security has always been a big problem in terms of data collection in nursing and other fields related to healthcare. A lot of companies and organizations had to pay huge fines due to such problems as lost laptops. Currently, healthcare vendors do not control digital security tools on the market and they are not focusing on installing the required software.

Normally, data collection tools in healthcare are saved on laptops and personal computers, which may easily result in data safety issues. Using platforms based on cloud for storing data may be a solution although it will ask for some personalized training for employees.

  1. The most effective health data collection methods

The healthcare industry has got a lot of different tools in order to generate necessary data. Those methods help people deal with evaluating the project, attaching interview protocols, including surveys and examinations as well as managing focus groups and so easily.

The case here is to opt for a set of tools that relate to specific activities done by a company. Those activities will support in defining what data collection methods are the most suitable ones. As soon as there is a demand to get primary data, you will need to opt for methods which relate to your own situation.

The first thing you should do is to set up an outline or make a plan that provides you with a clear understanding of what methods are the best to adopt. Your plan should include such questions as what data you need to collect, what tools you should use, whether there are any current tools for your purposes or who will take the responsibility of gathering the information.

  1. Examples of the high-quality data collection tools

The first tool is 360 Degree Patient Views. Companies are supposed to adopt integrated systems in order to get a whole picture of their patients. Institutions can also benefit from the current solutions such as healthcare CRM that offers reports, measurements and analysis on a lot of different issues.

Another tool to take a look is personalized patient care. Currently, almost 90 percent of hospitals located in the United States are making use of EHR while about 60 percent of private healthcare vendors are also using this system to improve their patients’ well-being.

Last but not least, generating necessary information is not enough. You need to take advantage of it in a right manner for every patient so that your care service is of higher quality than other providers as patient data is becoming a strategic asset at a fast pace.



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