The
application of modern information technology to support the free flow of
patient information around the circle of care is what digital health is all
about. For patients, this implies that every health-care practitioner they meet
in different places should be able to promptly and efficiently access important
health record information.
Electronic
health records, for example, are thought to improve patient-centered treatment,
integrate care, and maintain the financial viability of our health-care system.
However, despite billions of dollars spent over the previous two decades to
facilitate speedy and safe sharing of health information, Ontarians are
nonetheless confronted with the harsh fact that their health data are still
scattered. The COVID-19 epidemic has shown even more data quality concerns.
Much of the
available data on COVID-19 is a muddle, as revealed in a recent National Post
piece. Data on infected cases and fatalities are not only delayed, but also
incomplete. According to reports, Ontario provided contradictory figures from
provincial medical professionals and municipal public health departments. It's
no surprise that the Ministry of Health recognises that "uniform standards
across sectors are absent, making it incredibly difficult to merge patient data
or local systems with provincial ones."
It's a
bitter pill to swallow after years of effort to enable quick and safe health
data transmission.
Neither
long-term nor effective
The Ontario
government is exploring two measures to improve data quality, with examples
including the accuracy and timeliness of data produced by various service
providers. The first strategy focuses on leveraging common communication
protocols to improve health data transmission across heterogeneous systems
(systems built by multiple manufacturers that require different hardware and
software configurations to operate).
This
strategy, however, is neither scalable nor sustainable as communications among
various systems become more complicated, time-consuming, and error-prone as
additional systems are added to the mix. Inconsistent numbers of COVID-19
infected cases and deaths supplied by various levels of government are an
example. Not to mention that these standards develop quickly, and older
versions of the same standard are difficult to map and migrate to current ones.
The second
method is based on the Digital Health Playbook's minimal common data set, a
resource designed to help health-care companies construct their digital
systems. To mention a few applications, the basic data set includes data
classes (such as individual patients) and their related components (such as
date of birth) for clinical notes, laboratory information, drugs, vital signs,
patient demographics, and procedures.
These data
sets, while adequate for the needs of family physicians whose primary role is
disease control and prevention, are insufficient for treating complicated
individuals with many health conditions, which need a tremendous quantity of
health data from diverse health-care providers.
These two
measures taken by the Ontario government to solve data quality concerns are not
sustainable nor successful, and hence cannot be used to guide health
digitalization policy.
As scholars
concentrating on information technology in health governance, we recommend that
a data strategy include four pillars:
1. Data
quality requirements
To begin,
data quality is an umbrella phrase that incorporates various qualities such as
accuracy, accessibility, and timeliness. There are also trade-offs between
these dimensions. For example, increasing the frequency of data reports may
have an impact on data comprehensiveness, which necessitates time to cover all
of the essential data.
While
"fit for use" (meaning the quality of data meets the demands of its
intended users) is deemed reasonable and pragmatic, it must be explicitly
stated what quality criteria must be emphasised. Given limited resources and
rising expectations to reduce health-care costs, determining which data quality
criteria should be prioritised becomes increasingly important.
2.
Long-term, scalable, patient-centered platform
Second, the
health-care industry is not alone in coping with decades-old systems and the
low-quality data provided by these systems, such as erroneous COVID-19 case
numbers. Using lessons learned from banks and other organisations, the
health-care industry might develop an open data platform that facilitates data
sharing across health-care providers and allows patients to submit data through
social media, mobile and wearable devices. Countries like the United Kingdom
and Germany have begun to apply the open data platform concept.
3.
Measurable improvement indicators
Third,
quantifiable outcomes for initiatives to enhance data quality must be
identified. Training programmes on best practises for data entry may be
implemented, as could system features that allow for data quality verification
(for example, completeness or consistency). Measurable outcomes would assure
accountability and attainment of the desired goals, as well as informing future
funding decisions.
4. The
suppliers' improvement process
Finally, a
data strategy must explicitly describe a data quality improvement and
monitoring process in which data quality is regularly checked and analysed to
ensure that data supports patient care and research. Because data quality is a
shared obligation, the quality assurance process must take place both
collectively among providers and individually inside each provider.
Meaningful
interaction with all stakeholders is essential for defining and implementing
the data strategy. Patients and providers, for example, must be involved in
identifying the data needed to treat the diseases that consume the majority of
our health-care expenditure, defining quality characteristics of the data, and
specifying roles and responsibilities for preserving data quality.
In contrast
to the Ontario government's band-aid approach, the four-pillar data plan is
long-term, concentrated, and comprehensive. It would guarantee that data
quality is prioritised in Ontario's health digitalization efforts. Following
the concept, our health-care system would provide a scalable method for
constantly improving data quality.
Without such
a data plan, Ontarians risk losing another decade and billions of dollars.
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