Using HL7 and DICOM to Improve Operational Workflow Efficiency

in Radiology

Thusitha Mabotuwana and Christopher S. Hall

Radiology Solutions, Philips Healthcare, Seattle, WA, U.S.A.

Keywords: DICOM, HL7, Medical Informatics Applications, PACS, Radiology Analytics, Radiology Informatics,

Radiology Workflow.

Abstract: Radiology departments are increasingly asked to do more with less annual budget and to remain competitive

while managing bottom lines. Identifying opportunities to improve workflow efficiency is an important aspect

of managing a department and reducing associated costs. Workflow enhancement tools can be built by making

use of HL7 and DICOM messages that are directly related to various workflow steps. In this paper, we discuss

the importance of using both HL7 and DICOM to determine more accurate metrics related to granular

workflow operations, such as distinguishing between billing and operational exam volumes. Using a

production dataset, we also demonstrate how visualization can be used to provide better visibility into routine

radiology operations.

1 INTRODUCTION

For many years, a hospital’s radiology department

has functioned as a key profit center. In 2007,

radiology accounted for 37% of outpatient profit,

defined as revenue less direct costs, making imaging

the most valuable hospital outpatient service line (The

Advisory Board Company 2008). However, with

significant increases to healthcare related spending in

recent years, projected to be close to 20 percent of the

US GDP by 2024 (Centers for Medicare & Medicaid

Services), there has been a strong emphasis towards

moving away from the traditional fee-for-service

model to alternative reimbursement models.

In the traditional fee-for-service payment model,

providers are reimbursed by insurers for each service

provided. Unnecessary imaging alone is reported to

waste at least $7 billion annually in the US (peer60).

Since each service gets reimbursed, there is no major

incentive for hospitals to minimize costs associated

with these tests while the insurer has an open-ended

economic risk. On the other hand, with capitated

payment models, the economic risk shifts to the

hospital since the hospital only gets reimbursed a

fixed amount to treat a specific condition (Centers for

Medicare & Medicaid Services). With specific

healthcare reforms currently underway in the US,

there has been a strong focus toward integrated care

delivery while reducing costs – for instance, under the

new Accountable Care Organization payment model,

starting from 1

April 2016, hip and knee replacement

payments will be based not only on the procedures

performed, but on the quality of care delivered as well

(Centers for Medicare & Medicaid Services).

Similarly, starting from around 2011, various

radiology procedures have been getting paid under

‘bundled codes’ when two or more related studies are

performed together.

The American College of Radiology routinely

monitors changes to radiology-related payments and

recently reported that the bundled code payments are

falling short of the payment levels of the predecessor

codes and values; for instance, computed tomography

(CT) abdomen-pelvis without contract exams were

paid at $418.43 prior to using bundled codes; in 2013,

under the bundled payment model this was reduced to

$306.05 and in 2014, this was further reduced to

$241.79. With such changes to reimbursements, and

in an attempt to reduce costs associated with

unnecessary imaging, radiology has gradually been

shifting from one of the primary profit-centers for a

hospital to a cost-center. Radiology departments are

increasingly being asked to do more with less annual

budget and to remain competitive and manage bottom

lines. Radiology departments need to optimize quality

of care, patient experience, outcomes, efficiency and

throughput while reducing costs.

Mabotuwana T. and Hall C.

Using HL7 and DICOM to Improve Operational Workﬂow Efﬁciency in Radiology.

DOI: 10.5220/0006138900570065

In Proceedings of the 10th International Joint Conference on Biomedical Engineering Systems and Technologies (BIOSTEC 2017), pages 57-65

ISBN: 978-989-758-213-4

An important aspect of managing a radiology

department is to have meaningful insights into the

routine operations. This could include fairly

straightforward metrics such as the total number of

billable exams and exams by modality over a

particular time period. However, to identify workflow

improvement opportunities it is important to gain

visibility into the more granular metrics, such as the

difference between billing and operational volume,

total patient encounter duration, imaging systems

utilization and number of scans by hour of day and/or

day of week.

In this paper we discuss a generic approach using

two established healthcare information exchange

standards, Health Level Seven (HL7) and Digital

Imaging and Communications in Medicine

(DICOM), to determine metrics important to the

operations in a radiology department. The main

contribution is the linking of HL7 and DICOM to

determine granular workflow steps and the discussion

around specific radiology workflow nuances.

2 METHODS

2.1 Background

Healthcare vendors have embraced the rapid uptake

of technology in healthcare and as a result, most

hospitals have clinical systems from different vendors

to accommodate the needs of various departments –

for instance, a computerized physician order entry

system (CPOE) may be used for order entry, a

hospital information system (HIS) for patient

registration, a radiology information system (RIS) for

radiology specific functions, an EMR for medical

records, a scheduler for scheduling appointments, a

billing system for accounting purposes, dictation

systems for creating reports and a picture archiving

and communication system (PACS) for imaging

related tasks. To provide integrated patient care, these

different clinical systems need to communicate with

each other. HL7 messaging standard is arguably the

most widely implemented standard for

interoperability in healthcare across the world and

allows for the exchange of clinical data between

disparate systems (HL7 2016). Similarly, DICOM

(The DICOM Standard) is the de facto standard for

exchanging medical images. Although system-to-

system direct communication may be possible,

hospitals often use an HL7 interface engine (HL7

2016) to facilitate information exchange. Figure 1

shows a typical hospital configuration, with a focus

on radiology – often, mammography requires

dedicated workstations compared to other modalities,

such as X-ray (XR) and CT, and as such, is shown

separately. In-house systems would typically provide

some form of aggregated patient view that combines

information from RIS, HIS and laboratory

information system.

Figure 1: Overview of communication between various

clinical systems.

With recent incentives towards increased system

interoperability, facilitated by healthcare reforms

(e.g., Meaningful Use Stage 2 (HealthIT.gov 2015)),

hospitals have been moving towards enterprise

electronic health record systems (EHRs) to improve

patient care by facilitating sharing of patient data that

is typically distributed across multiple clinical

systems, and also improve workflow efficiency (e.g.,

EHRs have a single sign-on where disparate systems

will require multiple sign-ons for the same user).

However, most clinical systems are ‘closed-systems’

where the data is not directly accessible to external

parties, and often, even to hospital IT administrators.

As such, developing tools based directly on HL7 and

DICOM can have widespread applicability

irrespective of the individual hospital setting.

2.2 Reporting for Operational

Excellence

There are various systems already in place to provide

routine operational reports to radiology department

managers, often at a cost center level to which

budgets are allocated – definition of a cost center can

vary depending on the hospital, but for radiology, it is

usually one or more modalities. For instance, high

volume modalities such as CT would be a standalone

cost center whereas ultrasound (US), nuclear

medicine and vascular imaging could be combined

into a single cost center. Routine reports may not

always be sufficient for operational purposes; for

instance, it may be useful to know the machine

utilization of a shared US machine and using a cost

HEALTHINF 2017 - 10th International Conference on Health Informatics

center based approach will not capture all exams

performed on this resource. Additionally, there are

often exams which are split into two or more billing

codes although they occupy one scheduled slot.

Karami discusses a comprehensive list of metrics

important for radiology across seven main categories

(Karami 2014) while other investigators (Morgan,

Branstetter et al. 2008, Cook and Nagy 2014) have

discussed the importance of analytics and other

business intelligence software for radiology. The

underlying data source for systems that provide such

capabilities can be broadly categorized as:

1. Systems used directly in workflow – these

systems are used during routine operations and

would include systems such as the EHR, RIS,

HIS and PACS. Data is entered directly into

these systems.

2. Third-party software that subscribe to HL7

messages – these systems are often setup as a

‘listener node’ where a copy of all, or a

selected subset, of HL7 messages will be sent

to, often via the HL7 interface engine. Having

an interface engine is not so common for

DICOM since PACS is often the only

destination for images.

3. Third-party software that integrate with

systems used in workflow – these systems

often have closely-coupled integration with

systems used directly in workflow. For

instance, a new CT dose monitoring software

application may be installed in a hospital as a

new DICOM node and all CT machines can be

configured to forward a copy of DICOM

structured report (which is a way to analyze

dose-related data for CT) to this node.

Due to the specialized nature of clinical software,

most of the systems often consume only HL7 or

DICOM. However, as discussed later in the paper,

there are significant benefits to linking data from

these two sources for more accurate metric

calculation.

2.3 Overview of HL7

An HL7 message is composed of a series of segments

with each segment identifying the type of information

the message contains (e.g., patient demographics,

lab/observation result, diagnosis, insurance and next

of kin). In turn, each segment includes one or more

composites (also referred to as “fields”) that contain

the actual information (such as names and result

values). Composites can contain sub-composites (or

sub-fields) – for instance, patient name is a composite

within the ‘PID’ segment and can contain over six

sub-composites (such as family name, given name,

middle name and suffix). Composites are typically

separated by a “|” character, while sub- composites

are usually separated using “^”.

Each HL7 message starts with a message header,

corresponding to segment MSH, and defines the

message’s source, purpose, destination, and other

syntax specifics like composite delimiters. MSH field

9, denoted by MSH-9, is particularly important since

this specifies the type of message that is being

transmitted (such as ADT, ORM, ORU, ACK and so

on (HL7 2016)). The segments present in a given

message vary depending on the type of message that

is being transmitted. For instance, Figure 2 shows the

composition of an ADT message (used to convey

information related to patient admission, discharge

Figure 2: Components of an HL7 ADT message for a fictitious patient (Altova 2016).

Using HL7 and DICOM to Improve Operational Workﬂow Efﬁciency in Radiology

and transfers) containing seven segments (MSH,

EVN, PID and so on).

Similar to the number of segments within a

message type, the number of fields present within a

segment can vary as well. For instance, the PID

segment can contain over 30 different fields, although

it is common for the segments to terminate after the

last non-empty field (corresponding to value “C” in

Figure 2).

2.4 Overview of DICOM

DICOM is a specification for creation, transmission,

and storage of medical images and report data (The

DICOM Standard). In addition to the binary pixel

data, all DICOM files contain metadata related to

patient (e.g., name, gender and date of birth),

acquisition setup (e.g., type of equipment used and

settings such as source IP address and machine

name), and study (such as study

description). Metadata is contained in the DICOM

header which is essentially a list of key-value pairs –

the keys are standardized values in hexadecimal. As

an example, tag (0008,1030) corresponds to the study

description.

2.5 Typical Radiology Workflow and

Information extraction from HL7

and DICOM

At a high level, once a referring physician has ordered

an imaging exam, the exam gets scheduled (after

necessary pre-procedure steps are completed, such as

pre-authorization from insurance). Each imaging

exam will be associated with one imaging order.

When the patient arrives at the radiology department,

the front desk staff would typically ‘arrive’ the patient

in the EHR (could be the RIS or some other system

depending on the hospital configuration). At this

point, the technologist knows that the patient has

arrived for the scan (this could be by looking at a

‘technologist view’ in the EHR/‘modality worklist’,

or some other means, such as the front desk staff

printing out a ‘patient requisition form’ and handing

over to a technologist). When the technologist is

ready for the patient, he/she will go to the patient

waiting area and call for the patient. After explaining

the process, the technologist will start preparing the

patient for the scan, for instance, by giving oral

contrast if needed. Once ready, the patient will move

into the scanning room and around the same time, the

technologist will ‘start exam’ in the EHR. The

DICOM images get acquired at this point and sent to

a modality workstation. The RIS/EHR/PACS systems

typically work independent of the modality

workstation. At the end of the scan, the technologist

will review and push the images from workstation to

the PACS and then ‘end exam’ in the EHR. At this

point, the images are ready to be reviewed by a

radiologist. All these workflow steps trigger HL7

messages. The end-to-end radiology workflow from

order-to-report is more extensive as discussed by

McEnery (McEnery 2013), but the image acquisition

process is where combining data from HL7 and

DICOM is most relevant. As such, we focus only on

this part of the workflow.

Figure 3: Status messages that get triggered during radiology workflow.

HEALTHINF 2017 - 10th International Conference on Health Informatics

Table 1: Events required to determine workflow metrics for a CT Abdomen-Pelvis exam.

Metric

Data Source

Event(s) / Segment

Comments

Patient wait time

HL7

ORM^001: ORC-5 (order status=ARRIVED);

ORM^001: ORC-5 (order status=BEGIN)

Value is difference between the two

events; e.g., 25 minutes

Total scan time

DICOM

Acquisition times from 1

and last image in

PACS using (0008,0032)

Value is difference between the two

timestamps of images; e.g., 18 minutes

Sequence time

DICOM

Acquisition times from 1

and last image in

PACS using (0008,0032) for each Series

grouped by series UID (0020,000E)

Value is difference between the two

timestamps of images for each series;

e.g., 4 minutes for Series 1; 7 minutes

for Series 2

Begin-to-End time

HL7

ORM^001: ORC-5 (order status=BEGIN);

ORM^001: ORC-5 (order status=END)

Value is difference between the two

events; e.g., 23 minutes

Arrive-to-End time

HL7

ORM^001: ORC-5 (order status=ARRIVED);

ORM^001: ORC-5 (order status=END)

Value is difference between the two

events; e.g., 48 minutes

Billing exam volume

HL7

Accession number count based on ORM^001:

OBR-3

For a CT Abdomen-Pelvis exam, 2

orders will be placed; exam volume is 2

Operational volume

DICOM

Accession number count using (0008,0050)

For a CT Abdomen-Pelvis exam, only 1

physical scan is performed.

Machine utilization

DICOM

Performed machine is identified using AE

Title tag (0073,1003)

Calculated using some interval (e.g.,

1hr) minus sum of total scan times per

AE Title

Technologist

productivity

HL7 + DICOM

ORM, OBR-34 – operator name; accession

from DICOM and HL7

Calculated using operational volume per

some interval (e.g., 1hr) per

technologist

The various status messages that get triggered during

the different steps of the radiology workflow are

shown in Figure 3. Table 1 shows a few important

metrics most radiology department track along with

the HL7/DICOM field(s) that can be used to calculate

the value.

A radiology exam is identified by a unique

accession number. This can be determined using the

value in HL7 ORM^001 OBR-3 segment or DICOM

(0008,0050) tag. Accession number is then used to

join between HL7 and DICOM data to determine the

accurate value using one or both data sources.

2.6 Dataset

Through a product co-creation agreement with an

integrated care delivery network, we had access to a

database that stored all HL7 and DICOM traffic that

was sent from the radiology department to the PACS

since June-2015. The database was within the

hospital premises in a secure data center with

restricted user access. All metrics computed were at

an aggregate level with no PHI exposed, and no data

left the hospital environment. As of 31-May-2016, the

database contained over 13 million HL7 messages

over 120 million DICOM records.

3 RESULTS

3.1 Workflow Considerations

Here we discuss seven important aspects that need to

be considered when specific metrics are calculated for

operational purposes, with a focus on the power of

combining data from HL7 and DICOM.

3.1.1 Billed vs Performed Exams

Study volume is essentially the number of unique

accession numbers. This is the fundamental

chargeable unit for a radiology department, and as

such, many clinical systems will produce this volume

report on a scheduled basis (typically weekly or

monthly). These reports are often driven by financial

reporting requirements, and as such, will contain only

the billing exam volume. As illustrated in Table 1,

this means that a CT abdomen-pelvis study where the

images are acquired in a single scan will get reported

as two billable exams since there will be two orders

associated with the scan. However, it is important to

know the operational study volume as well since this

can have a significant impact on metrics such as

number of exams performed on a machine and the

number of scans a given technologist has performed

Using HL7 and DICOM to Improve Operational Workﬂow Efﬁciency in Radiology

– it takes significantly longer, in fact nearly twice as

long, to perform two CT abdomen exams on two

patients (due to various changeover and

documentation times) than to perform two scans on

one patient. As a result, from an operations point of

view, it may not be accurate to say that one

technologist who has performed two billable exams

on the same patient has been as efficient as another

technologist who has performed two exams on two

different patients (assuming everything else is

comparable).

Distinguishing between billable and performed

exams may or may not have a significant impact

depending on the study mix performed at a given

institute. For instance, in our dataset, for a certain day,

there were 891 total billable exams based on HL7

messages whereas there were only 829 exams based

on DICOM. In general, the difference was between 5-

10%.

It should be noted that the ability to use the

accession count from DICOM to determine

operational volume depends on the particular

hospital’s workflow. Some hospitals, including the

one in our study, typically scan all images under a

single accession number, push them to the PACS, and

then either split, or link the images to the accession

numbers associated with the different orders.

Alternatively, the splitting can happen at the modality

workstation itself, in which case two accession

numbers (in the CT abdomen-pelvis example) will be

seen in DICOM. In this case, the reporting engine will

need to perform some logic, such as ‘same patient,

same acquisition times for different accession

numbers’ to determine which studies should be

merged for operational reporting purposes.

3.1.2 Exams with Multiple Modalities

Studies where multiple modalities are involved are

identified using the same accession number. A few

examples of such studies are PET-CT, PET-MR and

interventional radiology exams (which may often

involve XR and/or ultrasound and/or CT). In each

instance, the complete exam will often be billed under

a single accession number, although from an

operations point of view, two (or more) resources

were utilized to perform the exam. Images acquired

from different modalities can be determined using

DICOM Source AE Title tag. These exams need to be

correctly accounted for when determining relevant

metrics (such as operational volume, technologist

productivity and machine utilization).

3.1.3 Shared Resources

It is common practices for different departments

within radiology to share resources. For instance, a

PET/CT machine may be used mainly for PET scans,

but due to low PET volumes, the CT department may

often make use of this resource to perform certain CT

exams during busy periods. If PET and CT are

different cost centers, PET and CT volumes will be

shown separately for each departments, but for

machine utilization, both volumes need to be

accounted for.

3.1.4 Manual vs Automated Timestamps

Care must be taken when calculating various

turnaround times using timestamps. For instance, per

Figure 3, scan duration is calculated using times from

the DICOM header. These times will often be reliable

since these are machine generated timestamps. On the

other hand, depending on the clinical system, exam

start and end HL7 messages may be trigged manually.

This flexibility is provided often for valid practical

reasons, for instance, after acquiring all images for a

CT exam, a technologist may have time to ‘end exam’

in the system only after scanning a new emergency

patient (i.e., back-time the value for the previous

exam). Similarly, ‘start exam’ time may be entered

manually and may depend on the individual

technologist – some technologists may consider the

start of exam to be when they call the patient from the

waiting room, some may consider the start to be when

the patient walks into the scanning room, while others

may consider start of the exam when the patient is on

the scanner itself. As such, it is important to

standardize the terminology associated with granular

workflow steps. If the workflow can be standardized

so that all technologists start the exam when they go

to get the patient from the waiting room, then the time

difference between ‘patient arrived’ and ‘exam start’

HL7 messages will accurately reflect patient wait

time while the difference between ‘exam start’ HL7

message and ‘first DICOM image’ timestamp will

show the overhead associated with getting the patient

on the scanner (which could be significant for obese

and/or immobile patients) and adjusting the scanner

settings prior to image acquisition.

3.1.5 Same Information in HL7 and

DICOM

Some data can be available in both HL7 and DICOM.

Either source can be used if the value in both sources

is the same (such as the accession number), but there

could be instances where same data is entered slightly

HEALTHINF 2017 - 10th International Conference on Health Informatics

differently depending on the clinical system in use.

For instance, when a technologist completes an exam

in the EHR/RIS, the resulting HL7 ‘end exam’

message will contain the complete operator name. On

the other hand, the technologist also needs to enter the

name into the modality workstation; however, if all

reporting is EHR/RIS driven, technologists will often

enter only their initials into DICOM since this

information is not used anywhere. Therefore, it is

important to identify the right data source and merge

data from either HL7 or DICOM after identifying the

study based on accession number.

3.1.6 Site-specific Business Logic

It is important to give priority to any site-specific

business logic since these are used in routine

operations. For instance, at the DICOM level, the

modality for X-ray may be CR or DR (indicating

analog vs digital X-ray respectively) whereas

operational managers may consider all of them to be

XR. Similarly, cancelled exams and historical data

imports should not count towards exam volume,

although HL7/DICOM traffic related to such exams

may be visible on the network. It is important to

accurately capture and implement such site specific

business logic when making inferences from raw

data.

3.1.7 Workflow Related Nuances

Given the diversity and complexity of various

radiology exams, there could be various workflow

specific nuances. For instance, certain MR exams

may require post-processing of images, which can

take up to an hour (post-processing usually happens

on a separate machine while the technologist is start

scanning the next patient). Radiologists can typically

start reading exams as soon as a technologist has

ended an exam. If a technologist ends the exam after

post-processing is complete, and uses the current time

as the end exam time, then it would appear as if the

exam took a long time to complete. On the other hand,

if the technologists back-times the study end time to

when the exam truly ended (ignoring all the post-

processing time), it would appear as if the exam has

been waiting in the reading queue for a long time

which affects the report-turnaround time. As such, it

is important to agree upon how to interpret the

turnaround times in context.

3.2 Identifying Workflow

Improvement Opportunities

Using HL7 end exam messages, we determined the

monthly study volumes (Figure 4) as well as the

volume by day of week and hour of day (Figure 5) for

MR, CT and XR.

For CT and XR, the heatmap representation

indicates the times when most scans are completed –

as expected, this is during normal business hours –

Monday to Friday between 8am to 6pm. On the other

hand, the MR heatmap suggests that there is some

unusually high activity happening later in the day,

between 10 and 11 pm for the months of March and

April.

Upon investigation, the MR technologists

confirmed that they routinely end exams only towards

the end of the day, typically during the shift change.

Figure 4: Monthly exam volume by hour of day.

Using HL7 and DICOM to Improve Operational Workﬂow Efﬁciency in Radiology

Figure 5: Exam volume by day of week and hour of day.

4 DISCUSSION

In this paper we have discussed the importance of

using both HL7 and DICOM to determine various

metrics related to the operations of a radiology

department. While using a single data source may be

a good approximation, it is important to take an

integrated approach in order to get better visibility

into more granular operations as well as determine

more accurate values for the metrics of interest.

A radiology department needs to create clear

definitions of metrics; even the seemingly obvious

terms such as “start of exam” need to be explicitly tied

to workflow steps and the electronic measurements

using HL7 and DICOM. This “data governance” is an

important aspect of the data analytics and process

improvement approach. Data governance should

define clearly the metrics, agree on the measurement

methodology, understand the exceptions cases where

the methodology might be imperfect, and serve as a

governing body to increase the acceptance of the

process improvement initiatives.

In the context of the MR workflow, we have

discussed a specific example where technologists

were routinely ending exams towards the end of the

shift. This may be acceptable for practical reasons,

but at the same time, this affects the data quality,

which in turn affect the various metrics that are based

on this data. As such, it is important for radiology

administrators and Department Chairs to proactively

set forth suitable guidelines and educate the

technologists on the importance of adhering to such

guideline. Providing visible feedback to the

technologists on a regular basis on the performance

of the department may help improve compliance to

such requests.

Despite having access to a large dataset, the

current study has one main limitation – the dataset is

from a single institution, and as such, the DICOM

tags we have used may not always be generalizable.

Although vendors are expected to follow the

standard, they often use private tags (to exchange

vendor-specific information that is not covered by the

DICOM standard) instead of public tags, and

sometimes populate different public tags instead of

the commonly used tags; as such, the mapping may

need to be modified depending on the site.

Having access to tools to provide visibility into

granular workflow operations is crucial for the

success of radiology departments. However, as

discussed, developers of such tools need to keep in

mind the various nuances associated with hospital

workflows in order for such tools to be meaningful

and widely adopted by all stakeholders.

HEALTHINF 2017 - 10th International Conference on Health Informatics

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Using HL7 and DICOM to Improve Operational Workﬂow Efﬁciency in Radiology