How to make EHR/EMR Epic Integration with Your Health App

The medical field in the United States was one of the first to be affected by universal digitalization, which had a positive effect on patient treatment. However, perfection knows no limits, and ordering medical records is the first step to a “bright future,” which is impossible without technology development.

Please dive deep into our Podcast with Jeff Fried, Director of Product Management at InterSystems. You can gather insights about EHR integration like Epic with the use of InterSystems technology.

The APP Solutions is a long-standing InterSystems partner, and we use InterSystems technology to integrate with 99% of EHR systems within the US. This is the quickest and most affordable way to solve all your interoperability challenges.

Integrate my Software with EPIC

Contact Us

What does Epic Software Mean?

Epic Software is an application created to help doctors and other healthcare professionals (insurance agents, pharmacists) effectively manage electronic medical records and track any information that is in any way related to a patient’s health. Such software helps access data much more quickly, not prescribing unnecessary tests or drugs that can cause side effects, etc. Epic software helps to significantly improve business processes.


According to statistics, in 2022, Epic named top EHR 12th straight year in nomination “Best overall software suite”. Isn’t that compelling enough reason to incorporate the system into your application?

What is EHR/EMR Integration?

EHR (electronic health records software) and EMR (electronic medical records) are medical records systems. Despite the similarity of concepts, they still have a defining difference that affects the degree of compatibility due to the amount of information. EMR contains data maintained by one doctor or group of doctors from one medical institution; as soon as you go to another hospital, you need to start all over again. 

The EHR absolutely systematizes all info from any medical personnel that has ever been entered into the system. Any doctor anywhere in the country can view the current data or send it to colleagues when needed.



EMR/EHR epic integration simplifies the life of medical staff and patients, allowing them to download the entire medical history of a person in seconds, and saves time on various burdensome but necessary formalities such as filling out personal and insurance information.


Does Epic have an API?

You can integrate your software with EHR (which we have found will give your product a huge advantage) in several ways:

Looking to seamlessly integrate your medical app with EHR/EMR like Epic? Don't let integration headaches slow you down – reach out to us

Contact Us


A White-Label Telemedicine Platform – organizing data transmission in FHIR HL7 standard for health care data exchange using FHIR HL7 infrastructure



Calmerry Online Therapy Platform

Orb Health – Сare Management As A Virtual Service

BuenoPR – 360° Approach to Health

But Epic is the most optimal because interoperability is easier to achieve using EHR solution providers’ public or open API. And the exchange of data between Epic EHR and your product takes place using the Epic API.

API is an application programming interface, a set of rules for working with data. With its help, you can understand how to retrieve info and send it back in each case. It acts as a kind of intermediary between the application and the EHR/EMR system.

Upon receiving a specific request, the API relays it to the system. It then receives, processes, and issues a response containing all the necessary information about the patient in a unified approved format USCDI (The U.S. Core Data for Interoperability). 



This standardization is part of the Affordable Care Act. It allows the creation of a homogeneous data set so that information does not get confused, and any doctor can quickly and easily figure it out.


Want to learn more about EHR integration? Watch our podcast with the expert Jeff Fried, Director of Product Management at InterSystems.


I need help to integrate my Platform or Device with Epic

Tell me more

How to Integrate Your Health App with Epic EMR/EHR Systems?

First, think about the server where your application is stored. Next, check the compatibility of the sites from which you are going to collect information. Make sure the EHR/EMR supports these sites. 


The next step is to create an account at, which we mentioned earlier. This is required to access the API key. Confirm your identity; it is important to protect data transmitted encrypted via the SSL protocol. 


Check if the API will have the necessary endpoints so that, at some point, your application does not stop receiving data from the system. Check everything from medical history to allergy markers. You will see the API key on the website, which is best tested to see if all endpoints are working as expected.

Include API calls in your application code using endpoints. After everything is implemented and tested, you can take advantage of the Epic EHR/EMR integration.

How To Make A Medical App: The Ultimate Guide

The Benefits of Integration EHR/EMR

 Among other preferences, we wanted to emphasize again:

  • Quick access to any medical institution. Doctors don’t always have the opportunity to see a patient in their office; sometimes, they have to go out and work with no computer with available data. The implementation of Epic EHR allows viewing everything from a smartphone. With Epic, healthcare providers exchange more than 200 million records each month.
  • Cost optimization. The Epic API is easier to integrate than HL7 or CCD. In the latter case, the developer must write a lot on his own; this significantly affects the project’s cost. Whereas Epic syncs with EHR/EMR for free.
  • Scalability. With Epic’s electronic health record integration, you can easily resize your database. It is helpful for medical organizations such as intensive care units, where patients are under constant supervision, resulting in many entries.


Who Needs Epic EMR/EHR Integration?

In addition to the above cases, the Epic USCDI API is the best fit for applications focused on remote patient treatment. For example, telemedicine. The patient does not need to collect all of his data from different clinics to forward it to the appropriate specialist. The doctor will find where to get them.

Epic EHR/EMR integration is also suitable for patient management, which tracks and controls treatment plans. When a patient’s medical history is reviewed, the app retrieves baseline and current clinical data (drug status and test results) and tracks changes.




Which Healthcare Organizations don’t Need Epic EHR/EMR Integration?

Note: if your software requires more than just retrieving data, you need to consider other APIs, because Epic only allows you to view the data, not make edits! Thus, if the attending physician is going to add new or correct existing information, remember about HL7, FHIR API, etc.

Epic is also not suitable if you need data synchronization or extensive analytical data (as a rule, there will be no overload with data on one patient). Also, Epic is not suitable for patient-oriented applications, as the information stored in the EHR/EMR is primarily for physicians.

How To Create a Healthcare Mobile Application

Download Our eBook

What is EHR (electronic health record), and how does it work?

Healthcare and data science are something of a perfect pair. Healthcare operations require insights into patient data to function at a practical level. At the same time, data science is all about getting deep into data and finding all sorts of interesting things. 

The combination of these two resulted in the adoption of Electronic Health Records (EHR) that use a data science toolkit for the benefit of medical procedures.

In addition to this, healthcare is the perfect material for various machine learning algorithms to streamline workflows, modernize database maintenance, and increase the accuracy of results.

In this article, we will explain what EHR is and how machine learning makes it more effective.


What is EHR?

Electronic Health Record (aka EHR) is a digital compendium of all available patient data gathered into one database. 

The information in EHR includes medical history, treatment record data such as diagnoses, medications, treatment plans, immunization dates, allergies, radiology images, laboratory and test results.

  • The adoption of EHR in the industry kickstarted in the late 90s after the enacting and signing of HIPAA (Health Insurance Portability and Accountability Act) in 1996. 
  • However, due to technological limitations, things proceeded slowly. 
  • The technology received a significant boost after the passing of the HITECH (Health Information Technology for Economic and Clinical Health) Act in 2014 which specified the whats, whys, and hows of EHR implementation.

The main goal of implementing EHR is to expand the view of patient care and increase the efficiency of treatment.


In essence, EHR is like a good old patient’s paper chart which expands into a full-blown, interactive, data science dashboard, with real-time updates where you can examine the information and also perform various analytical operations. 

  • Think about it as a sort of Google Account type of thing, where your data is gathered into one place and you can use it for multiple purposes with tools like Office 365 or the likes.

The critical characteristics of Electronic Health Records are:

  1. Availability – EHR data is organized and updated in real-time for further data science operations, such as diagnostics, descriptive analytics, predictive analytics, and, in some cases, even prescriptive analytics. It is available at all times and shared with all required parties involved in a patient’s care – such as laboratories, specialists, medical imaging labs, pharmacies, emergency facilities, etc. 
  2. Security – the information is accessed and transformed by authorized users. All patient data is stored securely by extensive access management protocols, encryption, anonymization, and data loss protection routines.
  3. Workflow optimization – EHR features can automate such routine procedures as recurrent Automate and streamline provider workflow. In addition to this, EHR automation can handle healthcare data processing regulations such as HITECH, HIPAA (USA), and PIPEDA (Canada) by implementing required protocols during data processing.

Electronic Health Records vs. Electronic Medical Record – What’s the Difference?

There is also another type of electronic record system used in healthcare operations – Electronic Medical Records AKA EMR. 

The main difference between EHR and EMR is the focus on different persons involved in medical procedures. 

  • EMR is a digital version of the dataflow in the clinician’s office. It revolves around a specific medical professional and contains treatment data of numerous patients within the specialist’s practice.
  • In contrast, EHR data revolves around the specific patient and his medical history. 

In one way or another, EHR intertwines with numerous Electronic Medical Records within its workflow. There is a turnaround of data going back and forth – medical histories, examination data, test results, time-based data comparison, and so on.

Read a more detailed overview of EHR/EMR differences in the article EHR, EMR and PHR Differences

Considering Developing a Healthcare Mobile App?

Download Free Ebook

How AI/ML fits into Electronic Health Record?

As was previously mentioned, the availability of data is one of the primary benefits of implementing Electronic Health Records into medical proceedings. 

Aside from data being available for medical professionals at all times, the way medical data features in EHR makes it perfectly fitting for various machine learning-fueled data science operations.


Overall, machine learning is a viable option in the following aspects of Electronic Health Record:

  • Data Mining
  • Natural Language Processing 
  • Medical Transcription
  • Document Search
  • Data Analytics
  • Data Visualization
  • Predictive Analytics
  • Privacy and regulatory compliance

Let’s look at them one by one.

Data mining 

Gathering valuable insights is one of the essential requirements for providing efficient medical treatment. One of the challenges that come with gaining insights is that, in order to do that, you need to go through a lot of data. This process takes a lot of time.

With the increasing scope of data generated by medical facilities and its growing complexity – the use of machine learning algorithms to process and analyze information during data mining becomes a necessity. 

Overall, the use cases for Data mining in Electronic Health Record revolve around two approaches with different scopes:

  • Finding data about the patient and his treatment. In this case, ML is used to round up relevant information in the medical history and treatment record to assist further in the decision-making process. 
  • On the other hand, patient-centered data mining is used to assess different types of treatment and outcomes by studying similar cases from the broader EHR database.
  • Data extraction for medical research across multiple EHR/EMR, and also public health datasets. In this case, a machine learning application is used to gather relevant data based on specific terms and outcomes across the EHR database. For example, to determine which types of medication for particular ailments were proven to be active and under what circumstances.
  • On the other hand, the same tools apply for exploratory research that reshapes available data according to specific requirements — for example, examining test result patterns of annual lipid profiles.



Predictive Analytics

EHR is all about data analytics and making it more efficient. One of the most important innovations brought by Electronic Health Record is streamlining the data pipeline for further transformations.

The thing is – EHR machine learning-fueled data processing provides a foundation to identify patterns and detect certain tendencies occurring throughout numerous tests and examinations of a specific patient across multiple health records. 

  • With all patient data and respective reference databases intertwined into a single sprawling system – one can leverage the available data to predict possible outcomes based on existing data. 
  • Predictive analytics assist the doctor’s decision-making process by providing more options while considering possible courses of action.
  • On the other hand, machine learning predictive analytics reduces the time required to pro.  

Predictive analytics models are trained case-by-case on the EHR databases. The accumulation of diverse data allows them to identify common patterns and outliers regarding certain aspects of disease development or a patient’s reaction to different treatment methods.

Let’s take DNA Nanopore Sequencing as an example. 

  • The system combines input data (coming from the patient) with data about the illness and ways of treating it. 
  • The predictive algorithm determines whether a particular match of treatment will result in a positive outcome and to which extent. (you can read more about Nanostream in our case study).

Natural Language Processing

In one way or another, natural language processing is involved in the majority of EHR-related operations. The reason for that is simple: most medical record documentation is in a textual form combined with different graphs and charts to illustrate points.

  • Why not use a simple text search instead? Well, while the structure of the document is more or less uniform across the field, the manner of presentation may vary from specialist to specialist. NLP solution provides more flexibility in that regard.

The main NLP use cases for Electronic Health Record are the following:

  • Document Search – both as part of the broader data mining operation and simply as an internal navigation tool. In this case, the system uses a named-entity recognition model trained on a set of specific terms and designations related to different types of tests and examinations. As a result, doctors can save time on finding relevant information in the vast scopes of data. Depending on the purpose, the search results form via the following methods:
  • By context – locating information within the document – vanilla document search. For example, you can perform a comparison of physical examination reports criteria by criteria.
  • Terms/Topics/Phrases – extracting instances of specific terms used or topics mentioned. For example, a doctor can obtain all blood test results and put them into perspective.
  • Search across multiple documents;
  • One of the most prominent current applications is the Linguamatics I2E platform which also provides data visualization features.
  • Medical transcription – in this case, NLP is used to recognize speech, and subsequently, format it in an appropriate way (for example, break down into segments by context).
  • The speech-to-text component operates with a set of commands like “new line” or “new paragraph.”
  • Nuance Communications make one of the most prominent products of this category. Their tools, Nuance Dragon, augments EHR with a conversational interface that assists with filling data into the record.
  • Report generation – in this case, NLP functions as a form of data visualization in a textual form. These models are trained on existing reports and operate on specific templates (for example, for blood test results). Due to the highly formalized language of the reports, it is relatively easy to train a generative model based on term and phrase collocation and correlation. 
  • In this case, the correct verbiage is analyzed out of the habitual juxtaposition of a particular word with another word or words with a frequency higher than chance (collocation) and the extent to which two or more variables fluctuate together (correlation). 


What solutions can we offer?

Find Out More


Data Visualization

Data visualization is another important aspect of data analytics brought to its full extent with the implementation of Electronic Health Records. 

Visualization is one of the critical components that make Electronic Health Record more effective in terms of accessibility and availability of data for various data science operations. 

  • The thing is – as an electronic health record is basically a giant graph with lots of raw data regarding different aspects of the patient’s state, as such, it is not practical to use it in this state. The role of visualization, in this case, is to make data more accessible and understandable for everyday purposes. That has to be obvious, right?

However, you can’t use the same data visualization template for every EHR. While the framework remains the same, it requires room for customization to visualize patient data on the EHR dashboard adequately. 

The role of machine learning in this operation parallels its role in data mining. However, in the case of data visualization, it is about interpreting data in an accessible form. 

At the current moment, one of the most frequently used visualization libraries in Electronic Health Record is d3. For example, we have used its sunburst and pie charts in the Nanostream project. 


Regulatory compliance, privacy, and patient data confidentiality

Healthcare is an industry that mostly operates with sensitive data through and through. Pretty much every element of healthcare operation, in one way or another, touches certain aspects of privacy and confidentiality. 

The fact is that integrated systems like EHR are vulnerable to breaches, data loss, and other unfortunate things that may happen to data in the digital realm. 

In addition to that, healthcare proceedings are bound by government regulations that detail the ins and outs of personal data gathering, processing, and storing in general, and specifically in the context of healthcare.

Such regulations as the European Union’s GDPR, Canada’s PIPEDA, and United States’ HIPAA describe how to handle sensitive personal data and what the consequences are of its mishandling.

The implementation of EHR makes compliance with these regulations much more convenient as it allows us to automate much of the compliance workflow. Here’s how:

  • Anonymization during data processing – in this case, patient data is prepared for testing, but non-crucial identifiable elements, such as names, are concealed.
  • Access management – EHR structure allows limiting access to patient data only for those involved in a patient’s treatment. 
  • A combination of encryption for data-at-rest and data-in-transit – the goal is to avoid any outside interference into data processing.


In Conclusion

The adoption of electronic health records and the implementation of machine learning elevates healthcare operations to a new level.

On the one hand, it expands the view on patient data and puts it into the broader context of healthcare proceedings.

On the other hand, machine learning-fueled EHR provides doctors with a much more efficient and transparent framework for data science that results in more accurate data and deeper insights into it.

Ready to develop your electronic health records system?

Estimate the project cost

What our clients say 


Doogood – An App For Doing Good

Calmerry Online Therapy Platform

Orb Health – Сare Management As A Virtual Service

BuenoPR – 360° Approach to Health

How to leverage a mobile device management system to ensure EMR security in mobile healthcare apps

Mobile applications improve medical professionals’ productivity. One can use an app for communicating with the client, sharing health records with colleagues, and even calculate medication doses. The main issue of all medical software is that it contains health sensitive data that could be stolen by hackers. Even if you make your app HIPAA compliant, you need to consider additional security measures such as mobile device management.  

Unless you want your clinic name to appear on the “Wall of Shame” of the U.S. Department of Health and Human Services, due to data breaches, you need to be aware of the main mobile device management services to integrate into your app.  

But first, let us take a closer look at healthcare mobile apps adoption across the medical industry. 

Use of mobile devices in healthcare

To meet modern healthcare standards, care facilities and hospitals implement a ‘bright your own device’ (BYOD) policy toward medical personnel. The policy may concern mobile devices, tablets, and laptops for accessing EHR and EMR, communication with care staff, record care data, and lookup prescription information, which also have a positive impact on medical treatment results.  

In particular, the need for mHealth app adoption concerns the U.S. health organizations, where the Health Information Technology for Economic and Clinical Health Act, devoted to health records digitization, was agreed on at the governmental level in 2009. Here are some stats:

  • Global mHealth Apps Market is expected to reach $111.1 billion by 2025
  • 93% of physicians believe mobile medical apps have a positive impact on the treatment outcome. 
  • Over 70% of medical personnel use mobile devices to communicate with parents and access Electronic Medical Record (EMR). 
  • The majority (84%) of patients think that their medical records are safe from unauthorized viewing.

However, patient sensitive data and medical mobile app security are not as optimistic as they seem. Why? Let’s find out. 

What solutions can we offer?

Find Out More

What is wrong with EMR

The adoption of mobile healthcare apps among medical professionals has resulted in an increased number of potential threat vectors and sensitive health data exposure, such as medical history and treatment plans.

  •  In the U.S. alone, 1,512 data breaches were affecting 154,415,257 patient records from 2013 to 2017, while 128 violations were related to EMR and affected 4,867,920 patient records. 
  • In total, data breaches on healthcare cause annual damage of $6.2 billion, which results in patient mistrust, loss of potential revenue, and penalties by the government.

According to the U.S. Department of Health and Human Services, which keeps an archive of health data breaches, the high percentage of breaches were traced to “other portable electronic device[s]”. What does this mean? Let us explain. 

Even if you develop EMR that competes with HIPAA security requirements, you are also responsible for information kept on mobile devices that access your EMR app. While 31% of decision-makers in healthcare avoid implementing EMR in mobile apps due to security reasons, around 49 consider implementing mobile device management tools to improve the security of their systems. 

In a nutshell, it is easier to keep all patient-related data in one place, but on the other hand, this can be quite risky. Therefore, to make your EMR mobile app secure from breaches, you need to integrate mobile device management software that will regulate user access to medical records.

“What is mobile device management software

Mobile device management strategies for healthcare organizations

As we said, consumer mobile devices are not secure by default. Moreover, healthcare organizations from the U.S. must meet recommendations by the Office for Civil Rights (OCR) which includes established policies for mobile device data security and staff training.  Otherwise, you will violate the Health Insurance Portability and Accountability Act or HIPPA. 

The solution is to apply mobile device management software. “What is mobile device management software”, you may ask. Well, MDM software enables you to control and monitor the mobile devices of users that installed your app. 

Before developing an EMR mobile app with MDM, you need to create a BYOD policy that will regulate: 

  • EMR app usage cases
  • Privacy and data ownership 
  • Types of approved devices and device provisioning
  • Security policies 
  • Evaluation of risks and liabilities
mobile device management use cases

[How does MDM work]

With this in mind, let’s find out more about mobile device management use cases and how to integrate them into your app.  


Geofencing is the type of geolocation app technology that detects user location via GPS and allows or prohibits using your app, or accessing particular data. In terms of the healthcare industry, MDM with geofencing will create boundaries around your hospital. If the app user crosses those boundaries by leaving your facility, the app triggers a response by restricting or allowing access to your EMR app.

mobile device management features

To integrate geofencing mobile device management features to your app, your mobile app development team will use MapKit or Google Maps SDK for iOS installed via CocoaPods for Apple devices and Geofencing API to add this feature to Android apps. 

App wrapping

You can enable mobility management and content delivery using existing mobile device management technology for data encryption or “wrapping,” such as XenMobile, which adds additional security to app data. Moreover, this MDM healthcare solution will automatically interrogate incoming users to know who they are, where they’re coming in from, using which device, and what data they’re trying to access. Besides this, XenMobileis includes other benefits of mobile device management:

  • Controlling native mobile apps and associated data 
  • Proving secure funnel file sharing solutions into management architecture
  • Enabling role-based access to different users. 
  • Tracking, locking, and wiping mobile devices that use your app
  • Enabling micro-app VPN for over-the-air data transmissions

To add this solution into an in-house EMR app, your development team needs to add just one line of code, since XenMobile is extremely easy to integrate. 

Considering Developing a Healthcare Mobile App?

Download Free Ebook

Remote management

In terms of the BYOD policy, the staff’s mobile devices could be stolen or lost. In this case, to protect your healthcare organization’s data from breaches, integrate remote user management software. By means of such software, you can remotely lock the device down, encrypt particular data, or erase it from the device while keeping their personal information. 

Remote mobile device management is available in the following MDM solutions: 

Application control

If an outside app is tainted by malicious code, it can siphon data from other apps on the device, which jeopardizes patient data. Application control lets you decide which apps to permit, and which to blacklist or disable. Also, you can use “containerization” to partition an area of each device for dedicated work-use; that way, distrusted third-party apps are kept separate from the EHR app on a mobile app.

The best MDM products with an application control feature are the following: 


To control all the app users that receive access to your EMR, consider a session management feature that will automatically generate reports with the following data:

  • List of sessions 
  • Search for session 
  • View session info with user’s email, duration, session ID

Thanks to this feature, you can track, not only what is happening in your system, but also, identify suspicious patterns and threads. Moreover, the MDM reporting feature will provide you with insights into your organization’s mobile environment, including device status, user information, log-in attempts, and compliance with password policies in real-time. 

To ensure your patients’ health data security is to create a formal device policy that will educate your medical staff about security risks and best practices. Next, consider the integration of mobile device management into your app. By using software built by reliable MDM solution providers, you will receive control over all mobile devices in your corporate network, data stored, third-party and native apps, and data transmission. 

Ready to start a mobile device management project?

Drop us several lines

What our clients say 

Related reading: 

A Guide on How to Create a Telemedince App 

Calmerry Telemedicine Platform Case Study 

Nioxin Consultation App for Coty-owned Brand Case Study