The process of migrating from one EMR to another is among the most difficult technical and functional projects a healthcare organization can tackle. EMR data migration requires a thorough understanding of the underlying data structure as well as a solid foundation in interoperability standards such as LOINC, HL7, SNOMED, and CDA. Beyond the technical considerations, bringing together all of the stakeholders in your practice or practices and getting them to agree on which data will be migrated can be a significant challenge. If the right questions are not asked at the start of a migration project, it may be executed in a way that does not meet the needs of all stakeholders – resulting in time-consuming and often expensive rework or project cost overruns.
Below are the 5 most frequently asked questions (with answers) our clients pose when tackling EMR data migration. Download the full EMR data migration whitepaper for additional considerations, tips, tricks and best practices.
A: We can migrate all of the data or a selected range of the data based upon your requirements. Typically, the data elements & amount/duration of data is driven by organizational requirements related to continuity of care, patient safety and even population-based reporting requirements. The Galen team is able to assist your organization in evaluating these requirements and industry best practices regarding these considerations. Any data not migrated can be archived using Galen’s VitalCenter Online Archival EMR data archival solution.
Electronic Medical Records (emr/ehr)
A: All migrations are based on elements and the amount of data to be migrated. For example: if Medications, Allergies, Problems, and Scanned Documents are to be migrated and there are 10 years worth of data, then the process could take up to a week to migrate into live. It is recommended to do these types of migrations in stages and spread it over several weekends.
Q: What about any new items that were added in the legacy system after the initial extract was taken? How do we include those items in the migration?
A: There are a couple different ways this can be handled. It depends a bit on the exact source system you are migrating from. If the source system allows free text items (medications, problems etc.) it can be helpful to instruct users to not enter free text after the initial extract and to only use dictionary items. Then to capture any data that was added, a second extract can be taken a week or so prior to the go live and a catch-up mapping exercise can be performed. This should capture any items that were added.
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A: Most EMR systems store each time a clinical item was updated separately. For example, if a diagnosis was assessed three different times during three separate visits, there would be three records of that diagnosis. If we were to migrate each instance of that diagnosis, you can imagine we would end up with duplicates in the patient’s chart. We typically extract the most recent example of the item so we get the most up-to-date comments/edits to that item.
A: There are a few different options you have when patient matching fails. During the emr data migration process, we can provide a report of those patients that failed. The easiest method to correct the errors is to update the legacy system to fix the reason for the error. This might be updating the patient’s last name or some other piece of information so it matches the other system. If patients are missing from the target system, they can also be added. If the patients do exist in both systems and the first method is not an option, a one-to-one mapping exercise can be completed that will map the patient in EMR to the unmatched patient record from the legacy system. This mapping can then be added to the emr data migration logic so data is able to be migrated. Another option is to manually abstract the information for those patients that cannot be mapped. This can be a preferred option if there aren’t many patient matching errors or if the project is on a tight timeline.These records can be shared across differt health care settings. Records are shared through network-connected, terprise-wide information systems or other information networks and exchanges. EHRs may include a range of data, including demographics, medical history, medication and allergies, immunization status, laboratory test results, radiology images, vital signs, personal statistics like age and weight, and billing information.
Today, providers are using data from patit records to improve quality outcomes through their care managemt programs. EHR combines all patits demographics into a large pool, and uses this information to assist with the creation of new treatmts or innovation in healthcare delivery which overall improves the goals in healthcare.
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Combining multiple types of clinical data from the system's health records has helped clinicians idtify and stratify chronically ill patits. EHR can improve quality care by using the data and analytics to prevt hospitalizations among high-risk patits.
EHR systems are designed to store data accurately and to capture the state of a patit across time. It eliminates the need to track down a patit's previous paper medical records and assists in suring data is up-to-date,
It can reduce risk of data replication as there is only one modifiable file, which means the file is more likely up to date and decreases risk of lost paperwork and is cost efficit.
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Due to the digital information being searchable and in a single file, EMRs (electronic medical records) are more effective wh extracting medical data for the examination of possible trds and long term changes in a patit. Population-based studies of medical records may also be facilitated by the widespread adoption of EHRs and EMRs.
The terms EHR, electronic patit record (EPR) and EMR have oft be used interchangeably, but differces betwe the models are now being defined. The electronic health record (EHR) is a more longitudinal collection of the electronic health information of individual patits or populations. The EMR, in contrast, is the patit record created by providers for specific counters in hospitals and ambulatory vironmts and can serve as a data source for an EHR.
In contrast, a personal health record (PHR) is an electronic application for recording personal medical data that the individual patit controls and may make available to health providers.
Electronic Health Record
While there is still a considerable amount of debate around the superiority of electronic health records over paper records, the research literature paints a more realistic picture of the befits and downsides.
The increased transparcy, portability, and accessibility acquired by the adoption of electronic medical records may increase the ease with which they can be accessed by healthcare professionals, but also can increase the amount of stol information by unauthorized persons or unscrupulous users versus paper medical records, as acknowledged by the increased security requiremts for electronic medical records included in the Health Information and Accessibility Act and by large-scale breaches in confidtial records reported by EMR users.
Wh users log in into the electronic health records, it is their responsibility to make sure the information stays confidtial and this is done by keeping their passwords unknown to others and logging off before leaving the station.
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Pre-printed forms, standardization of abbreviations and standards for pmanship were couraged to improve the reliability of paper medical records. An example of possible medical errors is the administration of medication. Medication is an intervtion that can turn a person's status from stable to unstable very quickly. With paper documtation it is very easy to not properly documt the administration of medication, the time giv, or errors such as giving the wrong drug, dose, form, or not checking for allergies and could affect the patit negatively. It has be reported that these errors have be reduced by 55-83% because records are now online and require certain steps to avoid these errors.
Overall, those with EMRs that have automated notes and records, order try, and clinical decision support had fewer complications, lower mortality rates, and lower costs.
EMRs can be continuously updated (within certain legal limitations: see below). If the ability to exchange records betwe differt EMR systems were perfected (interoperability
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), it would facilitate the coordination of health care delivery in nonaffiliated health care facilities. In addition, data from an electronic system can be used anonymously for statistical reporting in matters such as quality improvemt, resource managemt, and public health communicable disease surveillance.
Sharing their electronic health records with people who have type 2 diabetes helps them to reduce their blood sugar levels. It is a way of helping people understand their own health condition and involving them actively in its managemt.
Ev if data breaches occur. There are concerns about the efficacy of some currtly applied pseudonymization and data protection techniques, including the applied cryption.
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Documtation burds for medical facility personnel can be a further issue with EHRs. This burd could be reduced via voice recognition, optical character recognition, other technologies, involvemt of physicians in changes to software, and other means
Theoretically, free software such as GNU Health and other op source health software could be used or modified for various purposes that use electronic medical records i.a. via securely sharing anonymized patit treatmts, medical history and individual outcomes (including by
