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
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.
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)
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)
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.
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
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 –
Reduction in wastage/obsolescence –
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
ISO/IEC standards such 16022 Data Matrix and 18004
ISO/IEC 29161 Unique Identification for IoT
ISO 17367 Supply chain applications of RFID –
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