Feasibility Testing of the Quality-monitoring Tool, Qdact, for the Palliative Care Research Cooperative

Healthcare processes are measured, evaluated, and characterized through the use of
healthcare quality measures. Healthcare quality measures are tools that quantify the
consistency of care delivery within a population eligible for the process. Containing a
numerator (those with successful delivery of a care process) and a denominator (eligible
patients for that care process), quality measures produce a frequency or adherence rate to
which a care process was performed. Adherence rates can then be compared to evaluate quality
of care across clinicians, organizations, or collaborations to compare data, establish
benchmarks, and spur quality improvement projects.

In palliative care, for example, it is usually considered best practice to prescribe opioids
for moderate/severe pain. Imagine that a palliative care program's calculated adherence rate
reveals that their clinicians prescribe opioids for moderate-to-severe pain only 50% of the
time. Armed with this information, the program can now develop a directed quality
improvement project with the intention of improving this performance towards a more ideal
goal (e.g. 75%). Further, it can provide feedback to clinicians in real-time regarding how
they are performing against the quality measures of interest. In this example, clinicians
may receive an electronic alert reminding them to prescribe an opioid when directed by an
accepted best practice. This real-time approach, combined with a system that promotes
culture of data collection, sharing, benchmarking, and reporting, are effective methods to
improve healthcare quality. Lastly, and the focus of this proposal, is to build and test
such an infrastructure that performs such real-time quality monitoring of healthcare
measures in the Palliative Care Research Cooperative group (PCRC).

The investigators have previously identified the three major components needed for an
effective and usable quality-monitoring infrastructure. Together, these three components
answer the "what", "how", and "for why" questions that must be addressed within a quality
assessment and improvement system.

First, is the ability to perform collaborative and integrated data collection across several
sites. Successful multi-site data collection requires a centrally governed set of data
collection processes, which are guided by a data dictionary. A data dictionary is a set of
agreed-upon data elements, answer choices, rules, and branching logic. The data dictionary
informs the development of a data collection platform for use by clinicians. Together, the
data dictionary and software for use by clinicians guide "what" data is collected, and
ensure that the intended collaborative analyses can be performed with the data set created.

Second, is the process for data collection - the "how" characteristic within the system.
Data is collected, transmitted and recorded through the use of a data collection platform,
transmission processes, and registry, respectively. The data collection platform is the
interface in which real-time data is captured and recorded. This can involve paper-based or
electronic forms using patient, caregiver, or clinician reporters. Data is then transmitted
to the registry, either through electronic or manual means. Lastly, data is collected and
securely stored in a prospective registry, so quality reports can be generated and research
analyses completed. These steps are recommended standards for development of health
information technology by the Agency for Healthcare Research and Quality (AHRQ).

Third, is the component of the infrastructure that answers the question, "for why?" Several
reports have highlighted the need to translate raw data from quality monitoring efforts into
continuous feedback on quality to clinicians and other end-users to motivate the delivery of
best practices. This allows for changes in clinician performance during usual clinical care
delivery, thus meeting the Institute of Medicine's aim for a rapid learning healthcare
system. Generally, feedback is provided through system-generated reports that target
specific end-users (e.g. clinicians, administrators) delivered during pre-specified time
periods (e.g. weekly, quarterly).

At Duke University, investigators recently built the information technology infrastructure
needed for prospective quality measure adherence and outcomes monitoring in palliative care.
This system was developed and deployed in the Carolinas Consortium for Palliative Care, a
four-site collaboration between Duke University and three community palliative care
organizations. Recently, this Consortium has expanded to include organizations outside the
Carolinas; eleven sites now comprise the Global Palliative Care Quality Alliance (GPCQA).
The rapid expansion of qdact users and subsequent data collected have supported several
research-level analysis published in the literature.

In using qdact.pc, clinicians record data on processes of care and patient-reported outcomes
on personal iPads® during face-to-face clinical encounters with patients. During patient
interviews, clinicians record patient-reported areas of distress, clinical management
decisions, and patient-reported outcomes using validated instruments. These instruments
include those common to the field, including the Edmonton Symptom Assessment Scale (ESAS),
Palliative Performance Scale, and FACT-G. Longitudinal changes in these scales are captured
through repeat use of qdact.pc during subsequent encounters. Further, qdact.pc calculates
length of stay from admission and discharge dates, changes in symptom severity by
calculating the difference between two dates, and readmission rates by analyzing whether
patients in the registry had previously been admitted. Then, care processes and outcomes can
be linked using these data.

Inclusion Criteria:

- Palliative care clinicians employed by Palliative Care Research Cooperative sites.

Exclusion Criteria: