Why are Patients Getting Lost in Healthcare Processes and How can we Fix it

Sandeep Gupta
Fri 01 Jun 2018
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We’re living in a fast-paced world. We juggle tens of social media accounts on our PCs, laptops, phones, tablets, and a gazillion other devices. All the digitally stored personal information- from emails to home addresses and phone numbers to bank transactions- can be tracked through a few lines of codes.

In a world where people can easily create digital identities and link themselves in a matter of minutes, it’s surprising that hospitals continue to have a hard time correctly identifying patients and matching them to the accurate record. To be honest, identifying patients has only become more complicated with the advent of multiple data systems.

Patient matching and identification can prevent losses up to $1.5M for an average hospital.

Approximately 33% of claims are denied because of inaccurate patient matching, which costs an average hospital $1.5 million and healthcare over $6 billion.

A Black Book research gave even more specific results, showing that inaccurate patient matching results in an extra $800 per emergency department visit, and $2,000 per patient per inpatient stay.

It doesn’t stop here. Poor patient matching also impacts patient safety and their lives. Being able to track utilization of services for patients is necessary to manage population health and track major cost drivers. However, these insights are of little use if they can’t be seen in the context of other services a patient may take. The value in healthcare is much more than mere dollars and cents, and if 80% providers discover these errors while they are performing treatments, the quality-oriented healthcare we aim for is a long way.

What does a typical patient matching instance look like?

Patient data, by the very nature of it, is disparate and unstructured. A person can have hyphenated names or often use their shortened name. They could toggle between using their middle name or not as they get themselves registered for a medical procedure. Babies can have different names after their christening, and people can drop their last names altogether at times. All in all, names alone can take up different forms. Not to mention how phone numbers and addresses can change over time.

Usually, when hospitals collect information from patients, they input that data into their databases. On a small scale, hospitals could create patient databases with names, dates of birth, addresses, and Social Security Numbers. Over time, however, as healthcare witnessed an expansion of EHRs and several other clinical and administrative data systems, the problem went out of hand. Although each healthcare organization had a master patient index (MPI), the addition of multiple settings and the lack of standardization for patient data collection rendered MPIs messy and full of overlapping records.

Patients receive care across multiple settings in a network and each bit in their entire health record is important for treating them. With no standardization or with broken master patient index, providers end up working off errors, and in rare instances- wondering if they are even looking after the right patient.

Concerns that need to be voiced and addressed

  • No standard data with lots of missing fields
  • Duplicacies
  • Real-time changes in patient profiles

One of the major challenges that healthcare providers have expressed is the lack of standards for data collection. This could have been overlooked when healthcare was on a relatively smaller scale, but as healthcare networks expand, patients end up creating multiple digital identities as they go.

Also, patients may change their demographic information, and the current methods of data collection do not address this challenge. Incomplete or incorrect demographic details result in limited patient data. And more often than not, some fields are missing altogether. ONC suggests there is an average patient mismatch rate of 8% within an organization- which goes even higher when data gets exchanged between different organizations.

Traditional MPIs, as they rely on demographic data to search for patient records, can easily fall victim to mismatches, and providers could be left dealing with overlapped or duplicate records. And it just burdens healthcare with additional expenses- from $60 to fix duplicate records to $50,000 per HIPAA breach.

Healthcare needs much more than a conventional Master Patient Index

Several studies have revealed that the most significant reason for duplicate or erroneous records is the lack of data standardization within the organization- which is bound to happen as the number of data systems in an organization increase.

Healthcare organizations need to employ a unified data layer which is flexible enough to capture the details of a patient as they move across the continuum. The first leg of the process should be standardizing the data coming in from multiple sources and integrating it together. Having entire patient data in a single place could be instrumental in ruling out duplicates and missing values.

Assigning each patient record a unique patient identifier is a critical part of creating an Enterprise Master Patient Index (EMPI). Also, leveraging the advancing innovation in healthcare, organizations can create smart algorithms with improved accuracy that can identify duplicate records during integration and store standardized, unique records in a master schema. It’s important that healthcare organizations run quality checks on their data from time to time to ensure that integrity is maintained. Creating an extensive, organization-wide EMPI is a challenging task and it requires greater emphasis on precise mapping and efficient integration.

Eliminating the identity crisis in healthcare with population health information

A critical thing to note is that accurate patient matching and identification stems from making systems accurate and reliable. It’s less about creating enormous databases with tons of datasets and more about ensuring that each patient record that is stored is kept unique and follows a standardized format. Initiatives such as population health management and care management are of little use if population data has its origins in inaccurate records. Patients are the essence of healthcare, and out-of-the-box innovation can only make a difference when providers are sure they are looking after the right patient.

Learn how a unified healthcare data platform can eliminate the challenges of patient matching, get a demo.

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Tags: Big Data, Health IT, PHM
Sandeep Gupta
Why are Patients Getting Lost in Healthcare Processes and How can we Fix it

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