State of Electronic Health Records – Challenges & Solutions

Electronic Health Record have revolutionized patient care, and emerged as a preferred solution to achieving better Patient Care. With the ubiquitous adoption of EHR over the last few years, it has become an integral part of the healthcare delivery system. Nearly all health enterprises, big and small have implemented EHRs to collect and report data. The proliferation of medical imaging, screening tests, and diagnostic have resulted in glut of data on Patient Health in several countries. The Global EHR market is steadily on rise. It is estimated to grow from $ 30 billion 2020 to $ 40 billion by 2025. Despite the global adoption of EHR, not all physicians are content with utilizing EHR. Physicians often find themselves caught up in doing data entry work, which takes away their time from Patient interaction. Besides, EHR are far from being the ‘Cure all’ solution for patient safety and efficiency that it was expected to be. However, as the EHR systems matures, EHR are expected to achieve their complete potential in the long run.

Key Challenges

Silo-ed Data Systems

Many providers utilize systems that add on silo-ed data. For example, system using different data formatting making it difficult to exchange the data between hospitals, external laboratories, and physicians. This also makes it hard to utilize the data collected via patient monitoring devices. The Fast Healthcare Interoperability Resources (FHIR) draft standard is trying to define data formats for storing and transmitting data across healthcare organizations. For achieving radical interoperability, health organizations will have to tear down the data silos and ensure data (vital patient information) rendered is accessible to those who need it most. With more and more patient data being churned, and the industry shifting to Patient Centric Care – interoperability has become a ‘must have’. The other challenges that hamper achieving interoperability include privacy and security concern, limitations within HIEs, lack of compatible technical & linguistic standard to ensure shared data is complete and meaningful. With these impediments achieving true interoperability becomes difficult.

PACS & Silos – Creating Roadblock

PACS strategy focuses on the proprietary design and code sets of the systems. Specialties including Cardiology, Radiology, gastroenterology and ophthalmology manage medical imaging in silo-ed systems such as the Picture Archiving Communication System (PACS), the cardiology PACS, or other “mini PACS”. The proprietary extension makes it difficult to achieve interoperability within and outside the enterprise and the problem becomes knotty when health enterprises try to incorporate specialty images that are not covered in traditional PACS parameters. Apart from hindering interoperability, relying on PACS strategy can also affect organization’s agility. DICOM has been widely accepted as the defacto standard. However, there are plenty of organization still using PACS integrated system which are costly and complicated. With new modalities and image types getting utilized, PACS systems are not equipped to ingest these images. Enterprises opting for PACS system should consider below questions.
1) What approach will be taken for the requirement of data usability for Population health and data analytics?
2) IS your PACS system equipped to manage large data sets that come with digital pathology and genomics?
Furthermore, multiple silos need multiple infrastructure and models. This becomes a costly affair for the enterprise when it tries to integrate these departments silos with the EHR. To make the different specialty medical imaging within the EHR multiple connecting points need to be integrated which are expensive and difficult to maintain. According to Frost & Sullivan finding, close to 75% of healthcare data is available in the form of non-DICOM medical imaging assets encompassing video, photos, oncology treatment plans and other file types which the PACS cannot manage.

Reducing Physician Burnout

A group of researchers from the University of New Mexico in 2019 in their survey found that EHRs are largely the cause for the physicians’ burnout and stress epidemic. It states the time utilized for medical recording has doubled. Physician spend two minutes on their computer for every one minute spent with the patients.
Solutions

NLP & Voice Assistance

NLP is believed to reduce the time spent on data entry work for Physicians and enables them to allot more time to patients. It can save a lot of time by making documentation search, medical transcription (speech to text) and report generation easy with specific set of terms and designations related to various tests & medical exams. These features allow physicians to easily connect with their patients. Few EHR systems integrated with voice recognition features will allow physician to search a Patient’s Medical History without the need for the manual search in the system.

Improving Workflows

An HBR article stated that there is a room to improve workflows by 20% on average by eliminating steps that don’t add any value. All providers can agree to improve workflows in the EHR without any regulatory changes or technology innovation.
Artificial Intelligence & Machine Learning – Next Leap forward
AI is touted to be one of the biggest disruptions to look forward to in Healthcare. It embodies blending of Machine Learning algorithms and petabytes of data that enables approximation of human decision making. AI in Healthcare is very much dependent on the supervision of the physicians. AI powered EHR systems take medical voice recording & utilize Natural Language Processing (NLP) to form concise notes. It can be utilized to examine Patients EHRs and predict future outcomes. A successful example of AI’s disruption is Radiology. Any Radiologist must have a reference data set of few thousand images. It can be utilized to look into these radiology images; advances in images and pattern identification allows AI to spot the grey shades and identify a lesion which can be missed by human eyes. Testing platforms are utilizing AI to improve accuracy, flexibility and simplicity. Furthermore, it is also driving a massive patient centric revolution in healthcare by nudging patients to take charge of their health with ‘do it yourself’ approach. Diabetes is a successful example of use of AI by patients. An empowered Patient is able to use glucometer to manage his/her glycemic control. It can act as a source to improve physicians’ knowledge by providing them new insights. AI integrated EHR combined with home testing can give out insights on cancer screening, blood pressure, blood glucose control and weight loss. With AI integrated systems, physicians can get automated alerts about their patients and can provide immediate guidance. Besides, features like Voice Recognition, digital scribes and connected devices are already saving physician’s time taken in data entry by automating the process. It is also effective in correlating data from wearable devices, sleep trackers, exercise-diet logs along with a patient’s chief complaint, examination findings & lab results. EHR platforms of the future will be AI powered but they will be effective if they are patient centric.

Predictive Analytics

Predictive analytics can help the physicians in forming more accurate diagnosis and in arriving at better decision making. For example, a patient having chest pain presents at the hospital and the doctor suggests EKG test. At the time of admission there is no sign of heart attack but in past, the patient seems to have some heart condition. If the physician can access Patient’s Health Record through EHR and plug the information into a predictive analytics software for health diagnosis, the physician can arrive at better decision on patient’s treatment. For example – if the patient needs observation and admission, etc. By utilizing the practical predictive analytics algorithm, a physician can receive the latest data regarding any potential progression in any disease, latest information on treatment protocols, along with patient’s complete medical history. Besides Predictive analytics can also play a huge role in providing early intervention in preventing and reducing the severity of disease, thereby contributing effectively to improve Public Health.

Data Liquidity

Health Information Technology allows providers and health enterprises to collect and analyze data virtually from all end points of the healthcare ecosystem. It improves clinical outcomes and engages patients effectively. Data liquidity refers to the ability of data to flow throughout the end points of the healthcare system, easily and securely. In simple words, information is provided to various stakeholders of the ecosystem whenever and wherever they need it. Many health enterprises adopting emerging health technologies are inundated with data from multiple sources that they find it difficult to manage the data. The next-gen EHR systems will focus on data liquidity and work towards making patient data more accessible, consumable and portable.

EHR – Telehealth Integration

Pandemic changed the way people receive care. More and more people are opting for virtual healthcare. A few providers, before the Pandemic were wary to adopt Telehealth and telemedicine tools but Pandemic forced physicians and patients to use telemedicine and virtual appointments. As per the growing demand for telemedicine consultations, EHR systems will focus on Telehealth integration to provide telemedicine consultation to the patients. Mobile App and patient portal will further facilitate virtual consultations.

Mobile & Cloud-based Infrastructure

With EHR systems in place, the patient data is easily accessible. However, with each passing day, this data will get more and more complex with the rise in population. Accessing patient data in secure environment becomes non-negotiable. Having a cloud and mobile infrastructure gives physicians flexibility to access data on mobile, on the go, 24 x 7 anytime, anywhere! Furthermore, with patient security and privacy issues most providers will prefer cloud based infrastructure. mhealth sector will also utilize gadgets and smartphones to track patient information automatically and remote monitoring of the patients.

Summary 

EHR has become an integral part of the digital healthcare ecosystem. However, to create value-based healthcare, EHR systems should be able to keep up with the emerging trends and allow providers the flexibility of integrating new modules and technologies to improve patient care. At HOPS Healthcare we enable providers and health enterprises to create a value-based healthcare ecosystem with customizable and scalable EHR platforms that are designed to withstand disruptions in the industry.