Clinical Applications of Handheld Computers

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					                Clinical Applications of Handheld Computers

                              Michael A. Grasso
      George Washington University School of Medicine, Washington, DC, USA


   As the healthcare industry continues to become more distributed, healthcare organizations
are increasing their reliance on mobile links to access patient information and to update their
master database at the point of care. Handheld computers have evolved into a viable platform
for these systems. While initial projects have shown promise, several questions remain. This
article explores the unique characteristics of handheld computers with respect to user
interface design and wireless access, and introduces a prototype development effort.

1. Introduction
   As the healthcare industry continues to become more distributed, healthcare organizations
are increasing their reliance on mobile links to access patient information and to update their
master databases at the point of care. With mobile computers, clinicians can instantly update
patient records at the bedside to ensure that data properly reflects the most current
information. In addition, data can be validated against a centralized repository as it is being
entered to help eliminate medical errors, save personnel time, and minimize the need for off-
line validation.
   Handheld computers show promise as an important platform for the development of these
new applications [1,2]. However, recent surveys report that the majority of handheld
computers are used primary as reference tools and for portable computation, but are rarely
used to interface with electronic medical records or with a wireless service [3,4]. This article
explores the unique characteristics of handheld computers with respect to user interface
design and wireless access, and introduces a prototype development effort.
2. Background
   The Palm OS platform redefined handheld computing by providing systems with a unique
balance of features. The Pocket PC is a related device based on the Windows CE operating
system. For simplicity, this paper refers to both the Palm OS and Pocket PC devices as
handheld computers. These are inexpensive, lightweight, highly portable devices that are
small enough to fit into a shirt pocket, and which can integrate seamlessly with other
computers through a direct connection or wireless network.
   The typical computer has a large display, a high-speed network connection, and ample
processing power. In contrast, a handheld computer has a small display, a limited network
connection, and minimal processing power. Careful attention must therefore be given to
software design on handheld computers in order to communicate information effectively.
2.1. User interface limitations
   Portable data entry is an important function of handheld computers. However, handwriting
recognition is often too slow and error-prone for large data entry tasks [5,6]. Most examples
in the literature report guarded, but optimistic results on pen-based input [7,8,9,10]. The
efficacy of handheld computers for point-of-care data entry may be significantly limited
unless pen-based input is restricted through gestures, macros, selection lists, application-
specific keyboards, or speech input.
   The strength of handheld computers may lie more in portable information display than in
data entry. Examples of this includes accessing patient care documentation [6], decision
support [11,12], and clinical references [13,14]. A major challenge is to make efficient use of
the limited amount of screen space. One approach is to minimize the use of buttons, menus,
and scroll bars, which can consume a considerable amount of room on small screens. Another
option is to replace these screen objects, which are traditionally used for navigation only, with
objects than can be used both for navigation and to provide context. An example of this is
focus-and-context visualization, which divides the screen into a set of objects, where a central
object provides detail, and where other objects provide thumbnail overviews that are used for
navigation [15,16].
2.2. Haptic interfaces
   Haptic interfaces may provide another way to maximize screen space on handheld
computers [17,18,19]. A haptic environment couples the human sense of touch with a
computer, and can yield interfaces with more realistic properties. The components available
for handheld computers include force resistance sensors to measure touch and pressure,
accelerometers to detect rotation, and proximity range sensors.
   The similarities between a handheld computer and a patient's medical chart are noteworthy.
Both can be picked up, carried, held in one hand, and annotated with a pen or stylus. But there
are many things you can do to a medical chart that you cannot do with a handheld computer.
Physicians not only write on a chart, but they might crease a page to mark important findings.
They flip through pages using well-developed dexterity skills. Although not recommended,
they might even turn the chart upside down and shake it to see if a missing lab slip falls out.
In reality, an elaborate array of physical interactions are possible with a patient's chart, most
of which are not brought to bear on handheld computers.
   Consider a handheld device with a haptic display that embraces all of the properties of a
patient's chart. The physician would not turn the page by clicking a button or a scroll bar.
Instead, the physician would turn pages by brushing a finger across the top corner of the
display, similar to turning a page in a book. Another common task is to fan through a chart by
bending it slightly and rapidly skimming its pages. Using tilt and pressure sensors, a handheld
device might implement this by measuring the angle of the device along with finger pressure
along the edge, similar to fanning through a book.
2.3. Location-dependent user interface
   Before entering patient data on a handheld computer, the clinician must open a specific
software package, search for the correct patient, and possibly download the most recent data
from a central repository. All of this takes time that normally would not occur in a paper-
based system, where the clinician would simply pick up the patient’s chart that is hanging on
the exam room door. The need for additional navigation on handheld computers can hinder
data entry and contribute to decreased efficiency of the user interface. These experiences have
led to the development of user interface principles for mobile devices that require minimal
user attention and employ context awareness [20,21]. For example, when a central server
determines that a clinician has entered a particular patient’s room, it can automatically
instruct the handheld device to run the appropriate software, download the patient’s current
medical record, and display a summary of pertinent findings. Another example is to find the
closest printer when downloading lab results, find the closest workstation to display radiology
images, or locate the nearest surgeon for a patient consultation. Location-dependency can also
be used for passive security, for example, to prohibit access to patient data when the handheld
computer is not inside a particular area of the hospital.
2.4. Wireless protocols
   Mobile wireless solutions would allow clinicians to instantly update patient records at the
point of care to ensure that the central database properly reflects the most current information.
In addition, the data can be validated against a centralized repository as it is being entered to
help eliminate medical errors, save personnel time, and minimize the need for off-line data
validation. Several examples of point-of-care data collection have been reported for surgery
[22,23], radiology [24], psychiatry [25], clinical trials [26], home health visits [27], and
electronic prescriptions [28]. Other important areas of research for the healthcare industry
include security [29,30], signal interference [31,32], mobile middleware architectures, mobile
agents, and mobile transactions [33]. A broader survey can be found elsewhere [34].
   Two important wireless standards to emerge in recent years are Wi-Fi (802.11) [35] and
Bluetooth [36]. Both provide high bandwidth over short distances. They operate over the 2.4
GHz unlicensed band and can connect to the existing internet infrastructure through wireless
access points. The Wi-Fi specification is a wireless extension of Ethernet that supports
TCP/IP and other forms of network traffic. It allows for wireless transmission speeds up to 54
megabits per second (Mbps) at distances up to 1,000 feet. The Bluetooth specification is
complementary to Wi-Fi. It allows electronic devices to identify services and communicate
through ad hoc networks called piconets. It operates at distances up 300 feet and at
transmission speeds up to 2 Mbps. Based on initial studies, signal interference with sensitive
medical equipment does not seem to be a problem [22,37].
   Another option is to transmit two-way data over long distances using the cellular network.
Examples of this are CDPD (cellular digital packet data) [38] and GPRS (general packet radio
service) [39]. These protocols allow mobile users to connect to the Internet at transmission
speeds of around 100 kilobits per second (Kbps) or less. This is three orders or magnitude
slower than the Wi-Fi standard. However, because they use existing cellular networks, they
can be deployed over long distances, similar to a pager or cell phone.
2.5. Weakly connected environments
   Traditionally, clinical databases have been stored in central computing facilities using fixed
computers with high bandwidth, hard-wired network connections, and powerful processors. In
contrast, wireless-enabled handheld computers operate in weakly connected environments
[40]. They are subject to frequent disconnections due to power constraints of the handheld
computer or limited network availability.
   During those times of network disconnection, the handheld computer may still be
operating. The clinician may perform updates on data that resides locally on the handheld
device, or queue up operations for a central server once the network is available again. This
situation can result in a number of problems with respect to recoverability, consistency, and
durability. For example, the updates to a patient record that are entered on a disconnected
device may be lost if the handheld computer experiences a catastrophic failure, resulting in
recovery problems due to this single point of failure. Patient information stored locally on a
handheld computer may become out of date, resulting in consistency problems until the
device can reconnect to download updates. If two people are updating the same patient record
simultaneously, the changes made on one disconnected device may conflict with changes
made on another, resulting in database changes that are not durable.
   Research into mobile transactions that attempt to guarantee durability and consistency
include version vectors and anti-entropy. Version vectors store a version number with each
item, which is then used to resolve updates when data is exchanged [41]. The anti-entropy
approach is based on the propagation of write operations following a set of ordering and
closure constraints [42]. With both techniques, there is still the possibility of an update
conflict when the database is fragmented. Several vendors provide database tools for
handheld computers, including Oracle, Sybase, and Microsoft. However, none of these can
fully support real-time transactions in weakly connected environments, and thus cannot
guarantee data consistency or durability.
3. Materials and methods
   We developed a prototype Clinical Trials Information System on an iPAQ 3800 series
Pocket PC with 802.11b wireless network capabilities. We deployed the server on a Pentium-
based desktop running Windows XP with a Lynksys 802.11b wireless access point. The
Pocket PC software was developed with Microsoft eMbedded Visual Tools 3.0. The server
software was developed with Microsoft Visual Basic 6.0 and Microsoft Access 2002.
   To optimize the user interface, we restricted user input to numeric data and the selection of
text strings from previously-defined lists. We organized the data into a hierarchy of
independent display objects, and used focus-and-context visualization to display thumbnail
views of objects not currently in focus.
   The database scheme followed an event-oriented model, with a list of observations for each
patient. Other tables in the scheme contained metadata that supported the definition of
observation types and data validation rules. A more detailed description can be found
elsewhere [43]. We adopted a data sharing protocol based on optimistic fragmentation. We
used read-only and exclusive locks to create database fragments for the handheld clients.
Each lock had a configurable expiration time. All database fragments were monitored through
shadow processes on a fixed server. Our approach optimistically assumed the database was
consistent unless there was an expired lock that a client failed to release. Inconsistencies were
resolved by a quorum algorithm.
4. Results
   We developed a system that clinicians can use to collect data, and that incorporated a
number of ideas to enhance the usability of handheld computers. The prototype was evaluated
by 5 participants, who were asked to collect history and physical data on simulated patients.
   We used an exit questionnaire to collect feedback from the participants. It included
questions based on a Likert scale of 1 to 7 to gauge overall reactions to the system as
accurate, dependable, efficient, intuitive, and useful. Each participant was instructed that a
higher value was indicative of a more positive response, and that a value of 4 was considered
neutral. We computed an acceptability index (AI) for each participant as the average ranking
for each question. The average AI for all participants was 5.2, with a standard deviation of
   The initial feedback was generally positive. However, there were some concerns about the
amount of free-form handwriting input that might be required if enumerated lists could not be
defined to anticipate all possible responses. Validation rules ensured the completeness and
accuracy of the data following a simple “if-then” format based on Boolean criteria. While this
format worked well for identifying missing data among sets of observations and for verifying
normal ranges, the participants found it difficult to specify rules to identify trends in
longitudinal data.
5. Conclusions
   Through the Palm OS and Pocket PC platforms, handheld computing has evolved into a
viable tool for ubiquitous clinical applications that are tightly coupled to the essential
processes of patient care. There remain, however, several open questions. Addressing these
issues will help provide the technical advancements and empirical guidelines needed to build
principled applications that respond to the information technology demands of the next
generation of clinical systems.
   We have presented a prototype system that incorporates several design principles that were
optimized for handheld computers. We plan to conduct a formal evaluation of the prototype
later this year to more thoroughly assess its usability. We also plan to refine our approach to
validation rules, and expand the user interface to support haptic gestures and location-
depended operations.
   Michael A. Grasso ( is a medical student at the George
Washington University Medical Center in Washington, DC. He also holds a PhD in computer
science from the University of Maryland Baltimore County. This research was supported by
grant 1R43CA84797 from the National Cancer Institute.
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