Key Blocker in usage of EHR's
Clinicians are always under pressure, in any given visit be it as a part of an outpatient clinic visit or a bed side round. They are expected to make eye contact, listen empathetically, process nonverbal cues, keep lab tests, allergies, and medication lists in mind, and formulate differential diagnoses. The same clinician is also required to document clinical notes granularly enough to support clinical coding and enter data in a structured way to comply with data quality and regulatory requirements.
With widespread usage of EHR's it has become common to see clinicians and other allied professionals staying after hours to do just data entry into EHR's to comply with mandated data entry. This is reducing physicians and other providers into data entry clerks and is detracting them from being productive to provide quality care. This ended up making data entry as the largest potential obstacle to the effective use of EHR within clinical settings. Physicians at Yale and the University of California recently argued that EHRs are becoming detrimental to the care of patients as physicians spend twice as much time in front of a computer compared to face time with a patient.
However there are other multiple studies which proved that real time entry of patient data will make the current data available to clinicians and help them target the right patients to provide care. But real time entry of data adds additional burden to the clinicians taking their valuable and critical time away from providing care to patients and often leading to missing and erroneous data impacting patient outcomes and creating administrative overheads.
Currently there are some tools and work arounds within clinical settings to lessen the burden of data entry.
Intuitive User Interface: Clinicians have been complaining about quality of the interface they were forced to use since the inception of EHR's. The user interfaces were often found to be not intuitive and required countless keystrokes and they found it difficult to align their work process with what was needed to be entered into the EHR. Even today the best of the EHR's in the market have sloppy user interfaces, but vendors are making effort to create user interfaces by designing based on human behaviour principles and by working, observing and consulting with end users. The intuitive user interfaces need to be accompanied by proper education, training and support to aid in easier use of EHR by users.
EHRs with intuitive user interfaces aggregate information pertinent to the problem at hand and are designed using data visualization techniques for optimal display. But intuitive user interfaces has its own limitations in terms of space on screen, expectations of varied users and affordability and human senses.
Digital Dictation: To reduce clinician’s workload related to data entry in EHR’s lot of organisations use digital dictation tools with voice-to-text capability to speed up data entry. EHR providers are increasingly incorporating and integrating voice-recognition software into their products to allow clinicians to directly narrate into the system. There is an overhead of narrated notes need to be reviewed for accuracy and then approved, but clinicians are found approving their entries without reviewing them. This increases the risk of inaccurate data and mistakes. The art of converting narrative into structured data and incorporating semantic workflow to support the narrative is quite complex.
Natural Language Processing: Natural language processing (NLP) is increasingly considered as a viable technical solution for improving clinical outcomes and simplifying data entry. NLP deciphers doctors’ notes and other unstructured information generated during patient visit into structured, standardized formats. However NLP suffers from the similar issues as digital dictation and text is often ungrammatical, consists of “bullet point” telegraphic phrases with no semantic context.
What the future holds?
Internet of Things (IoT) is a concept that basically connects any device with an on and off switch to the Internet. IoT’s offer Automatic identification and data capture (AIDC) technologies, AIDC refers to the process of automatically identifying and collecting information about patients and logging this information in an application such as EHR. AIDC refers to a range of different types of data capture devices such as barcodes, biometrics, RFID (Radio Frequency Identification), magnetic stripes, smart cards, OCR (Optical Character Recognition) and sensors.
What AIDC can do for EHR's is briefly discussed below.
Digitising Written Notes: Many clinicians are still more comfortable working with paper and asking them to enter data electronically can lead to resistance against an EHR implementation. Automated document and data capture technology can be leveraged to enable doctors to continue to utilize paper encounter forms with a new EHR system. AIDC allows setting up bar codes (2-D or 3-D) automatically and assign them to patient’s record by scanning and digitizing their back files and integrating them with the EHR system to have a single view of an entire patient history. OCR/ICR technology is typically incorporated in document and data capture software applications that also have features like image processing for improving the quality of scanned documents; forms recognition, to identify the type of form being scanned; and forms processing, which enables the software to identify and capture specific pieces of data.
Improving Efficiency: One of the most important aspects of AIDC is to improve efficiency of an organisation by assisting in equipment tracking, inventory management and patient tracking. This is done using solutions involving RFID and mobile scanners which will help organisation track assets, providing real-time information about assets (drugs and consumables) ensuring hospitals have what they need, where they need it, when they need it.
One of the good examples of improving efficiency using AIDC technologies apart from the usual inventory management and equipment tracking is bed management where bed occupancy sensors provides an early warning by alerting that the user has left their bed and not returned within a pre-set time period, indicating a possible fall. These sensors can also be programmed to switch on lights, helping as digital signage devices.
Managing Patient Care: To provide better patient care, clinicians need access to medical equipment and access clinical data which is increasingly being collected using mobile devices and other wearable technologies. Mobile devices and wearable sensors are a part of IoT solutions and they allow clinicians to gain access to the information in real-time to improve patient experiences and outcomes. The wearables and other wireless devices and sensors allow monitoring patient temperature, Parkinson’s disease, post-surgery awakening, etc. These applications require asset tagging and patient tracking. The proliferation of IoT devices allow data being automatically collected and fed into EHR systems with vendors of IoT devices proving interfaces to integrate them with EHR's thereby helping organizations gather more data and deliver better care.
How widespread is usage of AIDC?
Automatic identification & data capture (AIDC) technology in healthcare has greatly promoted the error-free data collection and improved patient safety. It is helping reduce medication errors and related healthcare expenditure. Growing focus on patient safety, technological revolution, and rising government legislations on the use of barcode & RFID technology are further expected to boost the growth of the global healthcare automatic identification & data capture (AIDC) market.
Automatic Identification & Data capture (AIDC) market within healthcare industry is mainly segmented into clinical and non-clinical applications. The non-clinical application segment holds the largest share, owing to the higher adoption of barcode & RFID technology in the non-clinical applications such as supply chain management and medical staff & asset tracking, though the clinical application segment is also growing fast. Global Healthcare Automatic Identification & Data Capture (AIDC) Market is expected to reach USD 3,122.7 million by 2022 supported by a CAGR of 15.4% during the forecast period of 2017 to 2022.
Use Cases within NHS
The Wythenshawe Hospital used a traditional paper-based process to manually enter patient information into patient records. This process is known to be less reliable than automated entry and can cause major health concerns for patients as a result of the opportunity for human error. For the medical facility, error that leads to the injury or even death of a patient opens the door for major legal complications. The Wythenshawe Hospital staff also found the process to be time-consuming, as doctors and nurses had to take time away from patients to enter data, recall patient records or refill prescriptions manually.
Wythenshawe Hospital decided to implement a system based on bar codes and bar code scanning devices to support staff in scanning codes on patient records. By automating this activity, the staff is able to automatically retrieve a patient’s Electronic Medical Record (EMR).
Six trusts which include Salisbury NHS Foundation Trust, Plymouth Hospital NHS Trust and Leeds Teaching Hospital have been selected as a part of Scan4Safety project which used barcoding to better identify and match patients, products and locations. Across the six demonstrator sites, early signs of benefits are extremely encouraging, with over £700,000 of savings already being identified:
- Stock reduction/one off stock holiday – £233,000
- Reduction in wastage/obsolescence – £462,000
- Non-clinical pay efficiencies – £46,000
Based on these initial findings, it is estimated that for a typical NHS Hospital trust, the benefits could be:
- Time release to patient care – equivalent to 16 band 5 nurses per trusts, that’s 2,400 band 5 nurses across the NHS.
- A reduction of inventory averaging £1.5 million per trust, £216 million across the NHS.
- Ongoing operational efficiencies of £2.4 million per trust annually, that’s £365 million across the NHS.
What are the challenges with AIDC?
· AIDC devices and technologies interfaces with EHR are not ‘plug-and-play’. The devices will need some kind of interface to be installed that will translate the measurement data from the device into a format that the EHR database can understand and use
· AIDC devices provide high volume of data which is required to be captured and processed at high speed
· Unlike manual data capture the data coming from AIDC devices need to be categorised into wanted and unwanted data categories
Standards for AIDC
· ISO/IEC standards such 16022 Data Matrix and 18004 QR Code
· ISO/IEC 29161 Unique Identification for IoT
· ISO 17367 Supply chain applications of RFID – Product tagging
· ISO/IEC WD 30101 Sensor Network and its Interfaces for Smart Grid System
· GS1 Global Specifications, www.gs1.com
· HIBC Health Industry Bar Code, www.hibc.de