Contact Us: (888) 783-5280 | Info@ReportingMD.com
Contact Us: (888) 783-5280 | Info@ReportingMD.com

Analytic Challenges with Mid-Year EHR Conversions

Sometimes health systems need to perform a mid-year data conversion from one system to a new system. This can make year-end quality program reporting from the legacy system(s), challenging. It can be daunting to think about how you are going to combine the data and accurately report, let alone the unrelated tasks such as training staff on the new system and managing timelines, with minimal interruption to patient care.

Successfully merging data from multiple sources may be one of the most underestimated challenges in health IT. If you are combining data from multiple sources, you want the data to be usable outside of the source system as well. No organization wants to deal with the additional cost and resource waste of keeping legacy systems maintained to be able to have access to historical medical records. Not to mention the creation of care gaps created as a result of providers missing important information because they are navigating between multiple systems for a patient’s medical information.

So, what does this mean?

This means you will need to merge the data into a harmonized database. A harmonized database allows for data to be stored from different systems in a discrete and re-usable form for extraction and importing to a report or other EHR systems. Afterall, converting to a new EHR involves taking data from a legacy system and doing the best you can to map the data into a new system.

Harmonizing data is not an easy task. There is a high level of ambiguity and complexity in the data concepts themselves. For example, think of patient demographic information. The data elements are fairly straight forward, correct? Age, gender, address, why wouldn’t those just map accordingly. Well, formatting issues are usually one of the biggest barriers. Example: A date of birth in one system listed as MMDDYYY and the new system has a date format of YYYYMMDD.  This is a common example, and can be a simple to fix, but the complexity just continues, into financial and clinical data points between the systems.

Let’s say you have a plan to merge all historical data from the legacy system into the new EHR system. Matching patients is necessary to avoid duplicate patient records and to reduce the risk of missing pertinent medical information on a patient during delivery of care. The unique patient identifiers for the patients are typically not going to be the same between the legacy system and the new system.  When it comes to quality reporting, unmatched patients cause aggregation inaccuracies and can result in low performance, leaving practices at risk for audit. Significant amounts of incentive revenue could be missed due to patient matching errors.

Patient matching helps address interoperability by determining whether records correctly refer to a unique individual. Health Care facilities fail to link records for the same unique patient as often as 50% of the time. These absences in matching patients correctly can lead to safety problems or patient harm. Failures to effectively match patients can be costly, leading to additional or duplicate tests and delays in care.  The rates in which patients are accurately matched varies widely across institutions, due to factors of technologies and processes employed and the quality of data. ONC, as part of its interoperability roadmap sets goals for duplicate records within a health care facility.

Patient Matching challenges stem from: 

  • Standardization: Each data element may not be standardized the same way across health IT systems.
  • Typos: Information can be entered incorrectly, ie: transposition of numbers in a birthdate, or inaccurate spelling of last name
  • Information not entered: data may have never been entered in the first place.
  • Default or Null Values: Key identifying fields may have present values to indicate unknown or missing information.
  • Similar Information: Patients may have similar information, for example twins date of birth, last name and demographic information might be the same, and their first names could be very similar as well.
  • Information changes: Patients move, get married, and change demographic information throughout their lives
  • Identity fraud: Patients could use someone else’s information to get treatment.
  • Ineffective for some populations: Some patient populations, such as children or lower socioeconomic statuses who don’t have certain identifiers often move and have unknown or non-static demographic information. Other demographics such as undocumented immigrants may be reluctant to provide accurate information out of fear of deportation.

Certified Health IT plays a vital role in establishing a nationwide, connected, and interoperable health information infrastructure. Certain healthcare payment programs, including the Promoting Interoperability Programs and the Quality Payment Program, require the use of certified health IT. CMS refers to the minimum set of required certification functionalities that the health IT used by eligible clinicians must have in order to qualify for the CMS incentive programs as Certified EHR Technology (CEHRT), which is certified by the Office of the National Coordinator.

Using certified health IT improves care coordination through the electronic exchange of clinical care documents. It provides a baseline assurance that the technology will perform clinical-care and data-exchange functions in accordance with the interoperability standards and user-centered design.  The benefits are enhanced patient safety, usability, privacy and security.

Many practices are changing EHR systems to upgrade the certification to then required 2015 CEHRT, others may have already had a 2015 CEHRT product and are switching EHRs for various reasons. There are a couple of things to keep in mind when shopping for a new EHR to help with easing transition and maintaining certification needed for Promoting Interoperability programs.

Testing and certification under the Temporary Certification Program does not examine whether two randomly combined EHR modules will be compatible to work together. Office of the National Coordinator for Health Information technology Authorized Testing and Certification Bodies (ONC-ATCBs) are not required to examine the compatibility of two or more EHR modules with each other. Certification also doesn’t require that an EHR technology designed by one EHR developer makes its data accessible or “Portable” to another EHR technology designed by a different developer.

Gaps can threaten performance of healthcare organizations and closing care gaps has proven to have a positive impact and produce a high return on investment. Addressing gaps in care continues to grow in importance as value-based care efforts mature. When transitioning EHRs, the previously mentioned challenges can play a significant role in not understanding the number of care gaps. Patient Matching difficulties can lead to skewed numbers of gaps. Additionally, with a list of gaps to close, practices are left with uncertainty of the benefit versus the time needed to close a gap. Some care gaps may have a much higher return on investment than others.

Some takeaways for closing care gaps:

  • Education and communication are essential to making providers aware of the value of identifying and closing gaps in care. Challenges include the lack of sufficient resources or education about how to maximize workflow changes and effectively close gaps in care.  Different EHR technologies may provide different levels of reports. Not all will provide down to the patient level in performance to be able to easily identify in a report which gaps need to be closed.
  • Gaps in care can adversely affect provider performance. Providers are significantly more concerned than health plans that gaps in care pose a threat to their practice by affecting clinical performance, financial performance, and the ability to retain patients.
  • Programs to address gaps in care offer a higher return on investment.
  • Fixing care gaps will only grow in importance as value-based models continue to evolve.

In addition to the data conversion and patient matching, the practice also needs to identify and close care gaps so that their current reporting year performance is not negatively affected, all without disrupting the daily delivery of care.

ReportingMD can help

ReportingMD can partner with you to make the Mid-Year Data Conversion as painless of a transition as possible. ReportingMD has solutions for Patient Matching, Data Validation, Aggregating data from multiple disparate systems, and consultative services that help to identify care gaps and so much more. ReportingMD has a robust Patient Matching tool to match patients between disparate systems correctly. ReportingMD uses algorithms, unique identifiers, and manual review to accomplish accurate patient matching. Additional data validation is completed to work with the practice to confirm any patient matching that doesn’t fit with 100% certainty. This additional validation also allows for practices to complete data clean-up on their end, merging charts correctly that did not get merged and reducing the risk of medical errors due to duplicate charts.

ReportingMD tools and consultative services provide insight on closing care gaps. Services that we provide include:

  • Showing missed opportunities: where documentation was lacking
  • Revenue associated with each gap: we provide a complete analysis on how much revenue is left on the table due to care gaps, and consultative services on how to close those gaps
  • Patient level detail: we identify the patient(s) the care gaps are related to, so that you can follow-up and close the care gap
  • Upcoming Appointments and Day Sheets: Our Total Outcomes Management tool has reports to easily identify when patients with care gaps will be returning to the office. It is easy to manage and incorporate into existing workflows so that care gaps can be closed.

Value-based care analytics extends far beyond data aggregation and reporting. Success in value-based healthcare depends on strong clinical data expertise, deep programmatic knowledge and performance analytic solutions that are flexible and transparent.

We partner within each level of an organization to help you make the transition to value-based risk smoothly, with less administrative burden and no disruption to the delivery of care.

ReportingMD has more then 18-years’ experience in this category and is uniquely positioned to create a value-based care analytic management program that allows:

  • Optimized quality scores
  • Reduce physician, IT and administrative burden
  • Enhance performance incentives and reimbursement
  • Improve patient outcomes through care-gap management

Think of our team as your “plug-and-play” analytics department.

We can help with data conversion

Learn how we can help you combine data from disparate sources