Mery Ramirez, Author at Data ConnecXion

Sep 04

Data Integrity, Collection, and Reporting in Health Care

By Mery Ramirez Healthcare

By Mery Ramirez

Why should data integrity, collection, and reporting matter to a health professional? The words “integrity” and “integration” may sound similar, but they have a vastly different meaning, particularly for health professionals. Data integrity means keeping your data clean, useful, and whole. Data integration refers to putting your data together. Both processes are vital for making Big Data work.

The practice of data analysis, collection, and reporting is different from theory. It’s more than just automated processes. It’s a vital piece in the puzzle of data. Any missing piece will lead to data fragmentation. Data integrity is one of the most promising solutions to that issue. In this article, let’s dig deep into the problems of data integrity, collection, and reporting in health care. And how we can help you bring your health data to the forefront.

Data Integrity, Collection, and Reporting: Explained

Imagine going to the dentist for your regular check-up and you’re asked to fill a form. Looking at this form, you see that it’s actually a photocopy of the original. You can barely read the faded printing on the sheet of paper. The lines are broken, there’s a visible cut at the top, and it’s generally in a bad shape.

This photocopy was photocopied from a copy of the original document. The original quality is lost. In short, data integrity degrades over time. Reusing, reproducing, and storing data will affect integrity, collection, and reporting.

That example isn’t different from what happens to digital data.

Imagine an original piece of data as a first-scan document. It’s identical to the original document. Next, imagine you printed this scanned version, photocopied the printed version, and scanned the photocopy again. Then, you snapped a photo of the screen with your phone, you sent the screenshot to a colleague who printed it with a printer running low on ink. At this point, the original document has changed drastically. It’ll lose its integrity. That’s also what happens to health data. And it’s near impossible to restore the document to its original state.

You ask your patients, suppliers, and colleagues to upload data on various devices on conflicting systems. You create copies of this data and store it somewhere else. In the process, you create duplicates and backups using different technologies and change their formats. What’s more, you might migrate to a new system causing more changes to the data.

As you can see, collecting data isn’t easy. And preserving its quality poses a challenge for healthcare facilities.

The Seven Deadly Sins of Health Data

Entering data without validation
Uploading different versions of the same dataset
Importing and exporting data without supervision
Human negligence
Creating several backups without updating them
Isolating data on one system without connection
Not taking data integrity, collection, and reporting seriously

All these “sins” degrade the quality of your data and make it impossible to use. When you want to ensure consistent care for patients or provide health research assistance, you’ll need high-quality data. When you get two conflicting records for the same patient, which one will you use? Even the slightest difference will make accurate tracking and reporting hard to attain.

Data Integrity, Collection, And Reporting: Solved

What to do when you’re responsible for data integrity, collecting, and reporting in your health care facility or practice? First, you should enforce strict rules and protocols.

At least, conduct regular audits for detecting corrupt data. Have routines, checklists, and processes to meet data integrity, collecting, and reporting requirements. Your system administrator should also be aware of any changes.

Furthermore, do weekly or monthly data validation to ensure your data is up to date. Validation gives you an additional pair of eyes on your data.

In ancient times, when scholars and scribes copied a book, they counted every character in every line to make sure it’s an identical copy of the original. Printing changed that. Today, computer programmers take a few seconds to validate data.

Is Data Analysis the Same as Data Analytics?

First, let’s make the distinction between data analysis and data analytics. Many data companies advertise their data analytics features with little attention to data analysis features. Some use the terms interchangeably and cause more confusion.

Data analytics is a vast discipline with many branches like data collection, storing, organizing, visualization, reporting, and more. Data analytics help in gathering business intelligence based on real situations.

Data analysis is another branch of data analytics that’s only concerned with cleaning and scrubbing the data. Unlike data analytics, data analysis mainly focusses on having clean data.

With that distinction in mind, let’s examine the challenges facing healthcare data analysis.

Challenges of Data Analysis in Healthcare

Data analysis is key to the kingdom of health data. It’s critical to the process of gaining insightful information from your health data. Data analysis is the field of cleaning, refining, and inquiring data to extract useful information. It’s the road to reveal the secrets hidden in the data.

Without data analysis, it’ll be near impossible to put health data into work. Gaining actionable insight from your health data would be like searching for a needle in a haystack.

With machine learning, data analysis is now more sophisticated than before. It’s now possible to collect, clean, and visualize a huge amount of health data. However, there is also a huge gap between data analysis systems.

The challenge of analyzing the data is huge, but understanding the data is another. Machine learning is being used to unlock secrets in health data.

Health data can yield valuable insights, but it is a road full of unique challenges. Here are just a few obstacles:

1. When Health Big Data Is Too Big

A great deal of big data is stored on the servers of healthcare facilities, which poses security, privacy, and scaling problems. Although cloud storage is a promising solution, supporting IT infrastructure is still a challenge.

Hybrid storage systems appear to have filled that gap. However, more storage space has always been required to deal with all that data. It’s crucial to have enough storage space to meet the needs of healthcare facilities.

2. Data Cleaning Is Time Consuming

You can automate the cleaning process to save time. Unfortunately, the data isn’t entirely reliable since you cannot always be sure it’s clean. Storing a huge amount of data without ensuring accuracy is a waste of resources. Uncleaned data is bad data. When you use bad data, it’s impossible to make decisions.

Manual data cleaning isn’t any more error-free. And it’s time-consuming. Luckily, machine learning techniques are evolving fast to meet this challenge.

3. Data Comes in All Shapes, Sizes, And Forms

In the current medical recording system, patients’ data is collected differently. That makes categorizing health records difficult. Using different systems to collect medical records create conflicts that are hard to reconcile. Even though coding systems differ, it can still be difficult to see the big picture.

Universal systems will bridge the gap between clinical analytics, billing, and claims. It’s important to remember that health data is not a universal system.

4. High-Quality Data Is Hard to Collect

Due to the inadequate data collection methods, accurate data is hard to organize. Some patients fail to report their symptoms promptly. They neglect to update their health records as well. As a result, the data quality suffers.

Without accurate reporting, clinical data cannot improve. Your data collection method becomes outdated when it cannot keep up with the real world.

5. Capturing Faulty Data

A technical problem with one of the imaging machines may lead to faulty data, resulting in inaccurate data.

When medical images don’t capture the correct problem, it becomes impossible to use. For example, a distorted x-ray scan will have too much noise. Thus, it’s difficult to identify the real symptoms of the patient. It is impossible to analyze data when small mistakes accumulate.

6. Security and Privacy Threats

In the hacker world, health data is always in demand so it can be sold at the highest price. Medical data is a valuable asset on the black market. Security is a big challenge for data analysis because unethical persons view it as the holy grail of personal data.

Patients don’t feel privacy laws are doing enough to protect their data. A vital part of data analysis is the collection and sharing of health information between facilities. Paradoxically, patients are afraid to share their data because of that fear of getting their data shared with the world.

Conclusion

There’s untapped potential for data analysis in health care. But first, it’ll need to evolve past the obstacles. Healthcare professionals will also need to adapt to these changes. There’s no use in analyzing data if you’re not going to use it effectively.

There are still bigger challenges ahead, but data analysis has come a long way already. Right now, it’s impossible to predict the future, but healthcare will surely undergo a dramatic change in the coming years.

The solutions to these challenges will open the possibility of expanding healthcare.

Are you facing some data analysis challenges? Let’s talk and see if we can come up with a solution.

Sep 04

Love0

Importance of Data Analysis in Healthcare

By Mery Ramirez Data Analysis

By Mery Ramirez

Healthcare is one of the most important sectors in any place around the world. These great institutions are regularly used to save human lives and serve humanity in one way or the other. Besides saving lives and helping with one’s wellbeing, we all know hospitals also generate a lot of data from the services offered to the many patients admitted daily. As the increasing amount of information grows over time, there is the chance for data inaccuracies and corruption.

As a result, data corruption needs to be dealt with competently. To assist with this, there is one way, known as Data Analysis. It is an important tool that can help in easing pressure placed on medical care units. This is a great management utility. One might question how we can all manage this with Data Analysis, so let us take a closer look.

Importance of Data Collection in Healthcare

Raw facts and figures are called data. It can be any relevant information about the patient from their background to medical history. This can be purposed to great effect by the technique of Data Analysis. We can do both qualitative and quantitative analysis. This is important and beneficial for the institution. Let us look at some of the amazing ways using Data Analysis in hospitals can help.

Knowing the Patient Better

The first thing that comes as a significant benefit is having knowledge about a patient’s wellness based on previous family and medical history. This is one of the best things that has happened in healthcare. This is also the reason that patient data is being aggregated in so many hospitals. The doctor can speak to the patient without hesitation, giving the patient ease, as they can now communicate directly regarding their conditions. This can significantly improve the course of treatment. By knowing a patient and their historical background, the doctor can easily manage how to deal with everything regarding the patient.

Advanced Patient Care

“Health is better than wealth,” is a famous proverb, and it is a true blessing indeed. These days, healthcare is easy to get, however, getting the wrong type of care can be problematic. There is no telling if the wrong treatment can be fatal or not, but it does have severe harmful effects on the body. What was the reason for the wrong decision? Insufficient information was provided to the medical operator. They were given a diagnosis and started a familiar treatment, but they were not the ones responsible. It was the lack of data specs that made it difficult for them to give the right treatment. Having preexisting knowledge of the patient’s medical background can help the doctor in managing everything from medicine to giving the correct treatment.

Key Information Access

Nowadays, the internet is available in almost every part of the country. This allows easier access to the proper facilities. Having a healthcare database and using Data Analysis in the institution can give faraway patients real help. Having an online healthcare database system allows for a centralized place where every individual patient can sign up and access their account via their own personal username and password to check the status and results of tests and lab reports. Nothing matters more than the safety and health of the patient. This can give them ease with the fact that if they live faraway, they do not have to spend additional time and money to return just for the results when they can eventually check them online. This can simplify things for them, as well as the healthcare management staff.

Records Maintenance and Safety

Having updated patient care records is important so that if a patient is out of reach, the doctor can contact them and continue the treatment from the same point rather than starting from point zero, which can be an issue for both the doctor and the patient. Moreover, doing manual entry details is difficult. There are thousands of forms and locations that need to be checked and with so much work it will take time. Having a database management system will let everyone store their details online and the healthcare facility can store the entire data in the cloud, making it safe for their customers. This can also help in accessing files online like urgent cases and blood donation-based patients.

Having an efficient database system is very important in the healthcare field. However, various factors need to be considered. There is no doubt that healthcare is the backbone of our society.

Sep 04

Love0

The Struggle of Data Scrubbing

By Mery Ramirez Data

By Mery Ramirez

Data Scrubbing is one of the most common techniques used since the introduction of data sciences and has been used for various purposes for quite some time. Also known as Data Cleansing, this process is primarily defined as the cleaning and removing of various types of completely inconsistent data that does not have any meaning or needed usage. Industries such as insurance companies, banking systems, telecommunications, and many others use data for various purposes. For this reason, having completely refined data that does not require many changes affecting system performance is important.

Normally, data cleaning processes are hectic and require a lot of effort. This is made much easier by using various software solutions that can completely clean and scrub the data in no time. Though this is one of the best ways to get work done, however, there are also many other struggles that need to be looked at. While both data scrubbing and data cleansing are used simultaneously, there is still a minor difference between these two. Deciding on taking the challenges one may have to face? Let us have a look at the process in more detail.

Steps to Perform Data Scrubbing

There are several steps to perform while doing data scrubbing. Here, we have confined them to a few that are important to give a rough idea of what is going to happen and how one can sort out all the essential things easier.

Inspection and Audit

The very first thing is to identify all the irregularities and inconsistencies that are present in the data. This can be done in two ways. Either by reading and pointing out the whole errors scheme manually or by using a data-scrubbing tool. By doing a complete audit, one can easily get an idea of what is wrong with the given data and what needs to be fixed. With this, one can now move on to the next step as the parts that need change have been identified.

Data Cleaning

Here comes the real deal. This is where one’s skills will start counting. In data cleaning, begin by removing the general errors that can be seen. This includes all inconsistencies and all irregularities in the data that affect the flow. This also involves irregular words that do not appear to be needed. In short, this is the general error removal of typo errors and more.

Verification of Data Cleanliness

After removing all the general errors, one needs to make sure that all the necessary data issues that were in the data have been removed. One can also do this by using the same data-scrubbing tool that has been used to first identify the errors. This can also be done by reviewing the whole thing manually and getting others to give feedback on what needs to be rectified.

Report

Typing a report is also valuable, as this will document the effort on things that have changed. Writing a report is important and good practice, especially in a great firm. This is needed to get the whole thing under analysis and noting the parts changes were made. All the outcomes are then converted into a written format and submitted.

Common Errors

Speaking of things to rectify, let us get an idea of what kind of errors may need to be fixed. This is important so one can easily identify errors and make sure things do not go south. Following are some of the common data errors one can find while rectifying working data.

Duplicate Data Errors

This typo error involves the repetition of words in your data. It can be a single word or an entire block. Duplicate data increases the size of the data in no time and makes things difficult for many people.

Inconsistent Data Errors

This is an irregular flow of the whole data and can make a poor impression. The data flow is corrected and neatly set to ensure that it does not affect the natural look and elegance of the whole data. It is removed either using a scrubbing tool or manually.

General Errors

These include all the typing errors such as typo errors, punctuation, smaller letters, and much more. This can be rectified by simply using document editing software. However, it is important to take care of all errors so there are no issues left behind.

Data Scrubbing, as easy as it seems, can become a real hectic task if not done correctly. Just make use of the proper tools and knowledge to cleanse data from all kinds of errors. Choose from the options one can avail, and we are good to go.