What Is The Importance Of Data Quality In Healthcare?

Overview: Definition of Data Quality in Healthcare

Technological advancement has completely changed the shape of the healthcare industry. The introduction of new software such as Electronic Health Records has made it easier for organizations to access patients’ medical data and histories, thanks to hl7 data integration. Several benefits are associated with the seamless transfer of patient data. 

From optimizing patient care with accurate data to getting a precise patient overview, acquiring patient information takes patient care further. However, to make the most of this technological advancement, organizations must be extra cautious with operational and administrative issues that may arise because not all organizations can manage quality data. 

Data quality in healthcare is associated with the importance of understanding the core ideologies of data, their values, and how common issues related to data can be tackled.

But what is data quality management? 

Sharing patient information has its benefits, but the sensitive nature of this kind of data requires protection by stringent regulations such as the ones that top healthcare IT companies adhere to. The concept of data quality management is similar to that of quality control. 

With data quality, technology, tools, and procedures are employed to verify the sources of the data obtained, which maintains its quality. The primary focus is to protect customers, or in this case, patients, by verifying information, integrating reliable sources, and transmitting data to dependable recipients. 

Characteristics Of Data Quality in Healthcare

One of the most critical parts of the healthcare industry is trust, and without accurate data, there is no room for trust or establishing accurate analytics. Overcoming the challenges associated with data quality translated to delivering accurate data throughout your organization. But how do you ensure data stays compliant over time? Several solutions can get your data onboarded. These solutions identify quality issues in data within your organization to help you deliver accurate and consistent information. Better patient care and effective patient care are just some of the benefits of constant data drift detection.  

Define Benefits of Data Quality in Healthcare

Like any other organization, healthcare providers also have to keep up with their competitors by benefitting from the opportunities they come across. Data quality can provide several benefits to healthcare organizations. This includes:

More Informed Decision-Making

The more accurate information an organization has access to, the better decisions it can make. Plus, the organization is guaranteed to have confidence in the decision that they make. Minimal risks and consistent enhancement of outcomes are just some of the additional benefits of high-quality data. 

Better Patient Targeting

Accurate and precise data means marketers know exactly what they are looking for instead of targeting a broad range of people. Organizations are sometimes forced to guess who their target audience might be because of inaccurate or misleading data. 

Your current patients are the best resource to find new potential patients. Collecting quality data from current patients means you can find similar potential customers and tweak your marketing campaigns and healthcare procedures accordingly. 

Improved Patient-Physician Relations

Better relations with your customers (in this case, your patients) is the key to success for healthcare facilities. You can get to know your audience better by gathering data from them which can then be used to provide them with the facilities they demand according to their preferences. This, in turn, strengthens the relations between patients and healthcare providers. 

Several healthcare providers use telemedicine to strengthen relations with their patients. But is telemedicine worth it? 

Telemedicine protects the patient while providing detailed medical information to the physician. 

Easier Data Implementation

Compared with poor quality data, it is much easier to use high-quality data, increasing efficiency throughout your organization. Time is an invaluable resource, and you will need to spend a lot of time fixing inconsistent or incomplete data to make it useable. This means it takes longer for an organization to implement insights that they gathered from data analysis. 

Higher Profitability

Improved patient relations, more informed decision-making, and better advertisement campaigns all lead to one outcome: higher profitability. Effective data collection leads to greater efficiency levels by preventing the wastage of resources.  This means your operations become seamless and cost-effective, which also contributes to higher profits. 

What Are Some Healthcare Data Quality Metrics?

Poor data quality leads to increased bottlenecks, decreased efficiency, and patient mistreatment. That is where a well-maintained data quality management system steps in and saves the day. 

Such systems are based on the following metrics:

  • Accuracy and Precision: 

One of the main characteristics of data quality is that it must not be misleading and must not consist of any faulty components. Data precision and accuracy are related to how the data is intended to be used. In other words, to avoid wasting additional resources and prevent data from being off-target, it is crucial to understand how the data is to be consumed. Misleading information, especially in healthcare, leads to severe complications. This explains why investment in a superior data quality management system is essential. 

  • Validity:

The validity of the data is concerned with the requirements set for a particular type of data. Take the example of surveys where items such as ethnicity, gender, or nationality have a set number of options rather than open answers. 

Any answers that do not match with the options given are considered invalid. This is a crucial aspect of determining the quality of data acquired. Every department in a hospital has its own set of data rules that allow the organization to determine what data is relevant and what data is not. 

  • Reliability:

The same data gathered by numerous systems could have originated from a different source. No matter what source data has been collected from or where it is stored, the steady mechanism must not contradict the various systems that are gathering the data. The unwarranted variance must be prevented by this steady mechanism which collects and stores data without inconsistencies. 

  • Cohesiveness:

There is no difference between inaccurate and incomplete data. Missing data can lead to a big distorted picture that contradicts reality. Without a complete overall picture of operations, uninformed actions are highly inevitable. To prevent inefficiencies in the organization, it is crucial to understand the requirements that define a cohesive data set. This also helps organizations determine whether these requirements are being fulfilled. 

  • Uniqueness:

Paying attention to detail is crucial when it comes to gathering data. Lack of detail leads to an incomplete big picture, resulting in wrong decisions and uncertainty throughout the organization.  On the other hand, an organized data set that has been summarized and manipulated can lead to a different interpretation of the same data provided with inconsistencies at the lower level. 

Sufficient uniqueness and a proper level of granularity are crucial for operations to run seamlessly. 

What Are the Healthcare Data Quality Standards?

Healthcare data quality standards are all about HL7 integration, which provides a route for exchanging and storing data. HL7 standards set regulations on how data is to be transmitted. HL7 regulations specify even the structure of data, language, and data type. Practices, management, and insights, there is nothing that HL7 does not provide support for. HL7 standards are designed to ensure seamless integration between two systems. 

HL7 ensures interoperability between systems, more specifically health information systems. Through HL7, all data and documentation consistency is maintained across all systems regardless of what organization they are part of. 

HL7 standards include the following:

  • Structured project labelling
  • Messaging standards
  • Clinical document structures
  • Care document continuation

Why Is Data Quality Important in Healthcare?

Data quality management is crucial in all organizations that work with that, whether their operations involve sharing data, analyzing, or managing data. It is significant for healthcare providers who deal with patients’ medical records daily. This type of data is sensitive and requires regulations and strict protective measures. 

To understand why healthcare data quality is of utmost importance, healthcare providers must first understand the implications of accessing the data and its impact on the individual. Healthcare providers who wish to access this data must comply with strict rules and regulations. But that is not the only aspect they must be aware of. 

Organizations must understand that acquiring medical information of a patient, including reports and records, affects both the individual and the treatment that they are provided.  Thanks to data quality, and compliance with HIPAA, healthcare providers can protect sensitive information while improving patient outcomes. 

How To Improve Data Quality in Healthcare?

Data quality in healthcare can be ensured by integrated data analytics. Tools that quantify and qualify data can also be used to improve data quality in healthcare. It is crucial to focus on healthcare regulations through health care compliance organizations and ensuring data is in the correct format.

Here are some of the methods that can be used to improve data quality in healthcare:

Integrated Data Analytics

An integrated data analytics system is the best solution to minimize errors, enhance data governance, and automate data workflow. The healthcare industry divides this process into three phases:

  • Capture phase: In this phase, EHRs receive the data.
  • Structure phase: This phase involves formatting and sorting the data that has been received.
  • Transfer phase: The data is delivered from storage to a back-end database.

 

Using Tools to Quantify and Qualify Data

The evaluation of data sets involves choosing the correct tools and metrics. Selecting the tools to evaluate the data means deciding on the overall structure of how you will utilize the data.

Without making an informed decision on the tools to use, you will not be taking advantage of your data to its fullest.

Having Data in the Correct Format 

Data quality is all about consistency, accuracy, precision, and timeliness. While accurate data is crucial to healthcare organizations, delivering data on time and in a suitable format. This makes it easier to use and enables an organization to process it seamlessly.

Other methods:

There are several other methods of improving data quality in healthcare, including:

  • Ensure that data entries are made in compliance with data protective policies, but at the same time, the needs of them aren’t overlooked.

  • Data must be categorized comprehensively, ensuring patients’ needs are met on time and without obstacles.

  • The correct data must be entered in the right field to ensure data stays organized at all times and is easier to sift through when needed.

 

How To Ensure Data Quality in Healthcare?

Since every organization is unique, each healthcare provider has its measures of ensuring data quality in healthcare. The structures and methods that they implement vary widely according to the organization’s needs, but some of the essential tips and tricks apply to all.

Checking health records during non-busy periods is an excellent approach, and so is the allocation of a dedicated resource that possesses the medical expertise to maintain quality health records. Data acquired from the patient must be verified, and sources should be evaluated. For example, any demographic data collected from the patient must be verified before and after consultation.

For this purpose, handing the patient a printout of the given information for verification is an excellent approach to find out if the data is accurate or not.  For further verification, it is also a good idea to conduct audits of health records from time to time.

Keeping the data updated is also crucial for maintaining data quality. One way to do so is to archive the records of patients who are either inactive or deceased.

How Does Data Quality Affect Patient Safety?

Over the past decades, healthcare organizations have taken several approaches to protect their patients’ data, but even with these advanced measures, data is still being misused widely.

Take the example of the Office of the National Coordinator for Health Information Technology report, which discovered that out of 100 medical records, at least 7 are mismatched or duplicates. This number is just an average of mismatched health records, indicating that some organizations could exceed these statistics.

It all comes down to data quality and poorly integrated IT systems that produce below-average documentation. In some cases, EHRs are misused, which propagates misinformation. When A provider has filled out EHRs, it is often challenging to correct them, which affects all other systems.

These issues severely affect patient safety since patients and doctors now have incomplete or inaccurate medical information. This could lead to a misdiagnosis, which adversely affects the health of patients.

The negative impact on a patient’s safety results in medical liability issues. Lawsuits due to malpractice are now quite common in the healthcare industry, and these cases are predicted to increase in the years to come.

Such cases damage the reputation of a healthcare provider severely.

Which Data Quality Tools in Healthcare Are Widely Used?

Quality tools in healthcare consist of processes and plans that allow healthcare organizations to comprehend, analyze their efforts to maintain data quality. Quality improving tools include:

  • Focusing on the client: The primary purpose of healthcare facilities is to meet the needs of their clients. This means the steps that do not lead to client satisfaction must be eliminated, and efficiency must be enhanced to its full potential.

  • Focusing on the systems processes: Quality management depends on systems that arrange people, materials, and procedures.

  • Focusing on teamwork: To drive the organization to success, every organization’s team must work together. The interdependent systems that maintain data quality can be better operated when there is uninterrupted communication between teams within the organization.

Examples Of Poor Data Quality in Healthcare

The US government found that the third-leading cause of death in their country is patient misidentification. Because the number of duplicate records in hospitals is increasing. This results in the misidentification of 10% of incoming patients, sharing patient data from disparate sources further increases the probability of duplicate records generation. Apart from misidentifying patients, another example of poor data quality is the inability of organizations to analyze information.

Ohio health stored the data it collected in disparate silos, which led to the inability of organizations to analyze the data. Hence, decision-makers were unable to make the right decisions for their organization. Prescription errors are also a vital example of poor data quality in healthcare.

According to the Network for Excellence in Health Innovation, prescription errors resulted in a cost of $21 billion per year. These widely affected patients all over the US. They were the cause of more than 7000 deaths annually.

Consequences Of Poor Data Quality in Healthcare

Data quality management is one of the most important considerations that must be taken seriously by all stakeholders. Poor data quality adversely affects all departments within a healthcare organization. Some of the consequences of poor data quality in healthcare:

Patient mistreatment

Once data has been entered into an electronic health record, making edits is quite complicated and a long process. In other words, to fix an inaccurate record, humans have to intervene and resolve these discrepancies manually. With inaccurate information, mistreatment and misdiagnoses are inevitable, leading to patient mistrust because of their poor experience.

Increased bottlenecks

Manual interventions made to correct these medical records increase the chances of further errors. Plus, manual interventions are time-consuming, which slows down operations and leads to inefficiencies. With more systematic approaches, these inconsistent operations could have been prevented.

Poor decisions

Inaccuracies in data lead to poor decision-making. This is because healthcare providers are becoming increasingly reliant on electronic medical records, which means any data inconsistencies or inaccuracies could prevent the organization from making informed decisions.

Employee frustration 

Employees get more and more frustrated because of the increasing requirement of human intervention to correct inaccurate data. This leads to employees mistrusting the information that they receive through the IT system. It also results in employees falling back to manual reporting, which increases the room for errors leading to further inefficiencies.

FAQs: 

What are some of the data quality issues in healthcare?

Several issues are related to data quality in healthcare, leading to the mistreatment of patients and lack of trust between the patient and the organization.

This is because inaccurate data leads to misinterpretation of the patient information and misdiagnoses, which can even lead to death. In addition to this, it also leads to inefficiency within the organization since manual interventions to correct inaccurate medical records lead to further data entry errors, affecting the organization.

How good is the quality of data in healthcare?

The US government found that the third-leading cause of death in their country is patient misidentification. Because the number of duplicate records in hospitals is increasing, which results in the misidentification of 10% of incoming patients, sharing patient data from disparate sources further increases the probability of duplicate records generation.

This shows that the current quality of data in healthcare is questionable.

Conclusion:

A crucial part of medical device app development is making sure data quality in healthcare is maintained. The fact that inaccurate diagnoses are due to duplicate EHR is a prime example of the importance of data quality. Healthcare is all about establishing trust between the patient and the physician, and poor data quality affects this adversely while damaging the healthcare provider’s reputation completely.

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