High Data Quality in Healthcare with Melissa

Melissa offers a full spectrum of data quality solutions for hospitals, medical practices & more. These help you optimise your healthcare operations, better understand your patients, reduce risks and lower costs.

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How Melissa Helps Healthcare

For 40 years, Melissa has worked hand-in-hand with leaders in the healthcare industry. We are HIPAA / HITECH and SOC 2 compliant, and our breadth of data ensures that your patient records are always clean and accurate. Work with us to:

Both small medical practices and large hospitals can benefit from high quality data, because good patient care starts with accurate patient information. That's why we offer individual, customized solutions for every situation. Feel free to contact us via phone, e-mail or fill out the form!

Data Quality Solutions to Improve Healthcare Data

Addresses, email addresses and phone numbers are of high importance in order to communicate with patients. However, much of this contact information is subject to errors, as it changes frequently. For example, 30% of all emails are undeliverable due to incorrect data or data expiration. It's no different for addresses and phone numbers. In addition to communication, this affects the delivery of invoices and other important documents. Identity matching and duplicates can also cause problems. The same person may be filed with similar or abbreviated names, with/without initials, etc. All these little things can quickly lead to confusion. With our data quality solutions, you have the opportunity to solve these problems long term. After all, patient data is by no means static and therefore need to be checked on an ongoing basis.

Two important issues in healthcare are the well-being of patients and operational efficiency. Both depend on the integrity of data. This is where Melissa's full range of data quality solutions can help you. We offer the following solutions to help you maintain your patient data.

GENERAL VERIFICATION

Parse and verify addresses, names, phone numbers and email addresses. Check them - in the future - directly at the point of entry.

ADDRESS VERIFICATION

Analyse, verify, cleanse and standardise (physical) addresses to ensure optimal delivery and reduce mailing costs.


EMAIL VERIFICATION

Correct typos and invalid characters that cause high bounce rates. Ping emails to determine activity and ensure necessary compliance.




PHONE VERIFICATION

Ensure accuracy of mobile numbers for medical notices & alerts.

DATA DEDUPLICATION

Identify and clean duplicate patient records and then merge them into one golden record.



GEOCODING

Link patient information with geocoordinates to improve logistics and/or locate vulnerable areas and populations.





With our data quality solutions, we can support you directly when you receive contact data (point-of-entry), so that no more incorrect data enters your system. Existing data can be cleaned by batch processing - before the point-of-entry solution. We provide end-to-end support throughout the whole patient pathway.

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In particular, COVID-19 has shown the healthcare system that data quality is (still) poor in many facilities and institutions. At the same time, it also showed the need for high data quality, achievable with our data quality tools, which help institutions increase data quality by cleansing and standardising contact data within a very short time. Furthermore, this contact information is reconciled and duplicates are removed and consolidated in one place. This enables you to optimise your processes in such a way that the amount of data is processed in a timely manner, especially large backlogs of patient records that lead to inefficiencies. Together with outdated systems and manual processes, these are the three biggest barriers to poor data quality in healthcare.

Data Quality Goals in the Healthcare Industry

Access to accurate and complete data is becoming increasingly important in today's digital world. In the healthcare industry especially, this data is "vital for survival". We are talking about contact data as well as health data. On one hand, patients should be able to be reached quickly and easily—for example, to communicate a diagnosis. On the other hand, it is important to know who the patient is, and which examinations have already been conducted. Therefore, healthcare data must be factual, organised, valid, accurate and accessible. In other words, accurate data quality is critical to patient care, onboarding, safety and operational efficiency.

Contact data are created during the first appointment and small errors quickly creep in. Health data, on the other hand, is added throughout the rest of the process. Typical sources of errors that result in incorrect data are:

  • The recording of (contact) information often takes place manually in hospitals or medical practices. When data is later entered digitally, typing errors or incorrect numbers can easily occur, or incorrect information can arise due to illegible writing.
  • Data is digitally entered too late because of a shortage of specialists.
  • The patient has recently moved but has accidentally entered the old address.
  • Confusion of patients with common names (e.g. Smith as last name).
  • Different spellings such as Johnsen/Johnson.
  • Foreign names, where there is uncertainty as to what is a first or last name and how the name is spelled.



The 4 Main Goals of Data Quality Standards in Healthcare



1. Correctness & Validity

The original source data is correct (e.g. dates of birth and patient names).

2. Completeness

All data fields are complete, and no data is missing (e.g. all phone numbers have an area code).



3. Consistency

Information throughout the organization follows a fixed standard process (e.g. use of a convention: DD.MM.YYYY).

4. Up-to-date (current)

There is no outdated data, and all information is up to date (e.g. all recent observations are recorded for future use).



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minimised labor costs

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Fully-automated
contact data cleansing

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Now catches
errors in real time

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Easily integrated into
existing .NET and SQL processes

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Used GeoCoder to
find nearby physicians in network

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Case Study: CalOptima

Find out how CalOptima, the second largest health insurer in Orange County, migrated 422,000 members, 5,800 physicians, and 24 hospitals into one data warehouse with the help of Melissa’s Data Quality Components for SSIS.


View CalOptima Case Study
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Easy implementation
with SSIS

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Used GeoCoder to find
nearby physicians in network

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Better knowledge
of member locations

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Reduced returned
mail costs

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  • Osvaldo Cruz
    Data Warehouse Architect

    "Our main objective was to add value to the data produced for business users. Having conformed and integrated data is valuable, but the added value of recognising valid addresses is significant. One of the cost-driven indicators is returned mail and missing communication with members."

Contact Melissa Now

After your outreach, a Melissa representative will contact you in a timely manner. If desired, you can get access to a free trial, or our team can show you the capabilities of our data quality solutions in a live demonstration. We will also be happy to clarify any questions you may have or advise you on our solutions as well as what fits your deployment scenario and budget.



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