How to Overcome Healthcare Data Management Challenges

1. Data fragmentation and information silos

Data fragmentation is a common challenge in healthcare. Patient data often doesn't stay with a single team or department. Pharmacists, lab technicians, general practitioners, nurses, and other healthcare staff use patient data. Each updates the patient data with additional information; pharmacists update medical bills, and nurses may add monitored vital signs.

If anyone fails to update patient data properly, data fragmentation can occur.

Data fragmentation can create an information silo when;

• A nurse fails to update a patient's vitals, which can then create a silo for a general practitioner.
• A pharmacist fails to update the medical bill; it can create a silo for the insurance claim examiner.

These are some of the common problems that are created due to information silo and can create a gap in the healthcare organization's workflow.

2. Duplicate and inconsistent patient data

According to the National Library of Medicine, duplicate patient records were associated with 44% higher odds of missing an abnormal laboratory result. Duplicate and inconsistent patient data is a major problem in healthcare organizations.

These problems can lead to misdiagnosis, treatment errors, and other issues.

For example, two patients may share the same name, gender, and even age; if their test reports and medication data aren't properly documented, treatment can be confused.

In such cases, duplicate data is likely, and if the healthcare organization fails to keep test reports and medication consistently up to date, patient data may become inconsistent.

3. Difficulty matching patient records across systems

Patient records are often stored and used across multiple systems, like billing software, hospital information systems, and pharmacy management software. If healthcare admin staff record a patient's visit date as 16th of May in the hospital management system due to a medical emergency, and the same patient revisits the next day but the admin fails to update the date to 17th May in the hospital management system.

Still, the pharmacist updates the date to 17th May in the pharmacy management software, which can create confusion when handling patient records.

4. Keeping up with changing regulations and compliance requirements

Healthcare organizations must stay up to date on the latest government regulations. For example, if the government passes a law to digitize patient medical records, the compliance team must find an efficient way to digitize them. If a hospital has been running for 30 years, digitizing medical records can be challenging because of the large volume of data. In such cases, staying up to date with changing regulations and acts can be difficult.

5. Protecting sensitive healthcare data and patient privacy

The patient's data must be protected and kept secure because it is sensitive health information. If a patient is identified with a life-threatening infectious disease, the patient is classified as a biohazard patient; this is sensitive information. If this information leaks outside the healthcare organization, it can frustrate the patient.

However, if the organization does not maintain proper access controls, there is a high risk that information will leave the organization. To address this, the U.S. Congress introduced the Health Insurance Portability and Accountability Act (HIPAA) in 1996.

Under HIPAA, patients' Protected Health Information (PHI) must be protected using role-based access control (RBAC), ensuring that only authorized users can access sensitive health data.

6. Data integration and interoperability across healthcare systems

Healthcare systems often store patient data in different formats, such as PDF, CSV, XML, and other file types. Because of different file formats, it is difficult to export data across multiple healthcare systems.

Data integration can also be difficult if a hospital information system stores the patient's blood pressure as a single "120/80" value while a laboratory information system stores systolic and diastolic readings as separate fields. These differences require data transformation and mapping before healthcare systems can exchange and correctly interpret the information.

Image source: https://www.reddit.com/r/medicine/comments/pjtkoa/an_in_depth_discussion_of_problems_when_deploying/

The Reddit discussion above clearly shows that training machine learning models on a sample dataset is not enough to achieve the desired results and support clinical decisions. Real-world healthcare data is essential for training machine learning models. But inconsistencies in healthcare data across multiple systems can create gaps when using it to train ML models to make correct decisions.

Delayed access to critical patient information

Poor healthcare data management can delay access to patient information. Consider a patient with chronic kidney disease admitted to a critical care unit. Based on the patient's condition, the nephrologist and urologist advise starting hemodialysis immediately in an emergency ward. Because lab reports were scattered across multiple files, the healthcare staff couldn't find the serum creatinine level. This delays the treatment and can even become a life-threatening problem.

Higher operational costs and resource waste

Poor healthcare data management can increase administrative expenses. Healthcare staff spend more time verifying, retrieving, and correcting patients' information. This reduces staff productivity and can lower the efficiency of resources allocated.

Increased risk of errors in healthcare decisions

If patient healthcare data is not maintained properly, there is a risk of incorrect clinical decisions and incorrect medication. Consider a scenario where a patient's blood test report is misplaced with another patient's; this confuses the hematologist and increases the risk of misdiagnosis and treatment errors.

Disrupted workflows across healthcare teams

Poor healthcare data management can disrupt workflows across healthcare teams. In healthcare, each piece of data is evidence or proof of a patient's underlying medical condition. If any medical data is missed, the entire workflow gets disrupted. If a pharmacist misses a patient billing record, then it can create a gap in the revenue cycle management system.

Reduced quality of patient care and outcomes

Improper healthcare data management can reduce the quality of patient care and desired outcomes. Misdiagnosis or incorrect treatment can delay patients' recovery. This can prolong the patient's hospital stay and delay discharge. Over time, this can erode patients' trust in the healthcare organization, reducing the organization's overall outcomes.

Brings scattered healthcare knowledge into one centralized environment

Knowledge management platforms help you centralize patient healthcare documents from multiple sources across multiple teams. This makes knowledge management a central hub of healthcare information. For example, the laboratory team, the X-ray team, the HR team, and other teams can store and access healthcare data in a single knowledge base.

Connects healthcare knowledge across teams and systems

The knowledge base connects the healthcare team easily. For example, when a hospital introduces a policy for handling medical test samples, the HR team can easily find it in the knowledge base to educate a new lab technician during onboarding. Even when the laboratory team updates the policy, the knowledge base connects the information across teams.

Makes healthcare information easier to discover and use

The knowledge base makes healthcare information easy to retrieve and use when needed. Because all information is in one place, it's easier to find treatment protocols, SOPs, administration policies, and other healthcare-related data. The knowledge base can also store patient data. This saves healthcare staff time searching for information.

Balances information accessibility with data protection

The knowledge base provides role-based access levels, allowing healthcare staff to retrieve the information they need while protecting sensitive data from unauthorized access. For example, a lab technician may access laboratory procedures and testing guidelines, while a doctor may access relevant clinical information. This ensures employees can quickly find the knowledge they need for their roles while maintaining the confidentiality and security of sensitive healthcare information.