- Challenges in healthcare analytics
- The most efficient health data collection methods
- Examples of the advanced data collection tools
- Importance of patients' data collection
The healthcare industry is developing vastly and quickly. Today, it features modern technologies, tools, and devices to collect as much information about patients as possible, coupled with big data, machine learning, AI and computer vision shaping the new trends and standards for the industry. Numerous sources to pull out data from enables a higher level of insights, patterns visibility, and other vital issues transparency that can be used to build an effective personalized treatment approach.
However, in spite of all the benefits of new technologies and informational sources, it is still rather hard to maintain an efficient healthcare data management system, while also defining its true value. Moreover, digital protection of patients' personal information turns into a real challenge. We are, in fact, witnessing the foundation of a more complex healthcare landscape that no matter how advanced may seem at the moment leaves much to be desired just yet.
The key to efficient patient engagement and satisfaction hides in the data collected and enhanced by third parties, as well as the information that is analyzed across various sources. Not only will this increase the level of care, but also the recognition and visibility of a particular organization. For this reason, improving data quality is vital. We have a team of experts who deal with healthcare data quality and deliver efficient solutions to organizations and private providers. Our website is the right place to find answers, whether you need to modify an existing collection method or implement advanced tools from scratch.
This is why we at this point should evaluate the ever-developing data collection tools and methods to try and predict the track the whole system is taking and what it will become over the years.
Challenges in healthcare analytics
Questions related to information security and collaboration still call for an ultimate solution. Here, we have three significant obstacles that prevent us from generating data as effectively as possible.
- Poor Data Quality
Data actualization is the initial problem. One-off databases appear to be less efficient when it comes to generating actual data. This results from the fact that databases generally operate separately from source systems, causing problems when building high-quality data.
Let's take MRN, for example. Most healthcare establishments use Spreadmart to generate necessary data; however, this process requires assurance that the obtained information matches the MRNs. On the other hand, there is always a chance for a mistype in any of the patient MRNs, when writing them down manually in the spreadsheet. Moreover, some MRNs may change from time to time in the source system. Such possibilities should also be taken into account.
- Complicated Collaboration.
Imagine a healthcare establishment that uses EDW with the aim of reducing the number of data silos. On the other hand, Excel spreadsheets are still the only way to collect and maintain self-created information, though this causes another data silo in the face of this method. As a result, the process for collaboration of the given data silo turns into a tough challenge.
For example, we have two people in our institution who handle self-created data and record it in a spreadsheet. They have some free time at the end of the working day to enter that data. We send them spreadsheets asking employees to enter updated records, and both of them will make notes in their exclusive individual manner. In other words, the information will be provided in two different versions. Moreover, you will never be able to track multiple corrections and updates, and it is impossible to tell what information has been updated. Locking the documents and providing access to one and only person responsible may seem a good solution to this problem but, unfortunately, this method is not an end-all, as every person relies on a particular schedule and this may result in inappropriate use of working time.
- Data Security
Efficient data security has always been a huge concern when it comes to data collection in nursing and other healthcare fields. Organizations and companies are usually left with paying enormous fines. A single stolen laptop from one healthcare organization results in almost $1.8 million, simply due to the information encrypted on the laptop. Healthcare providers seldom track digital security tools on the market, and they are hardly experts in installing the necessary software, as well.
As a rule, data collection tools in healthcare are mainly stored on laptops and PCs, which often leads to data security problems. Using cloud-based platforms for storing data may be a good solution to this problem, though it will require some customized education for staff members.
The most efficient health data collection methods
The healthcare industry has received a variety of different tools to generate necessary data. They make it easy to handle the following tasks:
- Evaluate the project;
- Attach interview protocols;
- Include surveys and examinations;
- Manage focus groups and more.
The idea is to choose a set of tools that refer to specific activities performed by an organization. Those activities will help to define what data collection methods are the best options. Once there is a need to obtain primary data, you will need to choose methods that refer to your particular situation.
The first thing you’ll need to do is to build an outline or plan giving you a clear understanding of what methods are best to implement that should contain answers to the following questions:
- What data do you need to collect?
- What tools do you need?
- Are there existing tools for your purposes?
- Are there any tool samples or templates available?
- Who is responsible for collecting the information?
Examples of the advanced data collection tools
- 360 Degree Patient Views
Organizations are supposed to implement integrated systems to receive a complete picture of the patient's journey. Institutions can benefit from already existing solutions, like healthcare CRM, that provide reports, measurements, and analysis on various issues.
- Personalized Patient Care
Recent statistics reveal that 90% of all US-based hospitals and healthcare organizations are using EHR. About 60% of private healthcare providers have also adopted the system to benefit from even more comprehensive insights into patients' well-being.
- Practical Use of Big Data Insights
Generating necessary information is not enough. We need to use it wisely in favor of the patient, to bring care service to a new level. Patient data is rapidly turning into a strategic asset for all healthcare organizations, without exception, and medical facilities require it if they want a leg up on the competition.
Importance of patients' data collection
In addition to the fact that big data can unlock more than $300 billion, there are some other reasons for providers to embrace data.
- Informed decisions for improved quality
Generated data may allow staff to make more informed decisions, eventually leading to improved quality in the industry, overall. Organizations do still suffer from the lack of more meaningful information in order to understand what their patients’ needs are. Advanced technologies let professionals examine individuals remotely, providing them the care they deserve. In-depth research and analytics will make it easy to define the most efficient problems and sources that call for modification or improvement.
- The bigger picture with aggregated data
Some organizations still keep data separately, using several repositories. Instead, they should create a comprehensive source system to get full access to insights and gauge their performance on a macro level.
- Higher medical facility performance
We should note that the healthcare industry involves not only providers and physicians, but also third parties like, insurance companies, registries, etc. Secured and meaningful data is a good opportunity for organizations to save money. All they need is to represent their healthcare facilities in a more accurate way using data collection tools and advanced methods.
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