Case Study Analysis: System Science in Healthcare and Zoya's Death

This case study analyzes the tragic death of Zoya, a patient with a known drug allergy, highlighting critical failures in the Australian healthcare system. The assignment examines the overconfidence in Electronic Health Record (EHR) systems, the lack of integration of healthcare decision support systems, and the lack of system resilience, ultimately leading to medication errors. The study emphasizes the importance of resilient EHRs, integrated systems, and the need for data quality and ethical implementation of clinical decision support tools to prevent similar incidents. The case underscores the need for a balanced approach that combines technology with professional skills and a focus on patient safety.

SYSTEM SCIENCE IN HEALTHCARE
Student Name
Professor’s Name
Affiliation
Date

Discussion
i. Over confidence in Electronic Health Care systems
Most hospitals and offices of physicians have fully adopted Electronic Health Record (EHR)
systems, a trend that is steadily growing, and the traditional paper-based record keeping systems
have been taken over. Adoption of these systems has redefined the roles of various stakeholders
in the health care industry and in some cases invited new stakeholders into the medical scene.
For example, software designers in this tech-savvy age are key stakeholders in the health care
industry. In fact, technology is as a key driver of the health care services, and with some areas of
health care service delivery automated, the health sector would be greatly imprecated without
technology. However, managing patients only through the lens of health care smart systems has
been the cause of the most grievous mistakes that have been recorded in the medical industry5.
When not rightly used, health care decision support systems could be the source of problems in
the medical industry.
When practitioners put all their confidence to decision support systems, they forget that the
systems are programed rather than trained, and so they are always liable to make errors.
Furthermore, decision support systems in the health care setting could be taking away the
professionalism from the medical industry, leading to a dry and unbecoming system that is
abhorred by patients. For one reason, medical practitioners who are charged with taking care of
patients have been forced to use these systems without a proper knowledge on their usage1.
Hence, it is thus that in the given case study, Dr. Stanley made a grievous error in diagnosis of
Zoya, which transcended to a wrong drug prescription.
For some practitioners, Electronic Medical Records have become the equivalent of drive-texting
in the medical profession, as they spend a lot of time behind computer screens at the expense of
attending to the patient personally. In so saying, Electronic Medical Records (EMRs) tend to pull
away health care professionals from patients, and the desired personal interaction with the
patients is replaced with a thousand clicks of the mouse2. Every health care practitioner well
knows that giving the patients an attentive ear is the foundation of proper diagnosis and
treatment. Heath practitioners have spent years and years learning to parse the clues they get

from patients, only for Electronic Medical Records to suck the professionalism out of them.
Overdependence and overconfidence in Electronic Medical Records is the issue here which
needs to be addressed1.
To remedy this situation, clinicians and hospital administration should work harmoniously to
demand ameliorated products from software designers and implore the government to formulate
tolerable laws to govern the use of Electronic Medical Records. Social Network Analyses of
complex system3.
ii. Lack of Integration of Health care Decision Support Systems
It is agreeable to everybody that proper health care of patients fundamentally relies on proper
coordination between clinicians and the administration of the hospital. This coordination is also
necessary for the effective working of electronic medical system in information retrieval and
giving of diagnosis reports. Lack of an integrated Electronic Medical Record system to
harmonize patient information from various hospitals is a serious shortcoming. Many errors that
have been made by practitioners in the medical field could have been avoided had the Electronic
Medical Record systems been standardized in Australia. Since one EHR system cannot
communicate with another, serious errors are made that would otherwise have been prevented
with the integration of all Health Electronic Records (HER) systems in the country5. A key
benefit of integrating HER systems would be improvisation of the quality of communication
with other health care providers which will lead to enhancement of prescription refilling
capacities and improvement of online interfaces to more pharmacists. In so doing, errors in the
system would be easily noticed, and failure in one system will be easily captured in another
system. The interdependence that will be created in due process will do away with all system
redundancies and promote effective patient report processing. Furthermore, decision support
would be enhanced with more participants giving additional recommendations for analysis of the
patient condition at hand. This will enhance a greater participation of more health care
participants into the EHRs interoperable systems as opposed to the standalone EMRs6.
iii. Health Care Systems are not Resilient
Resilience in health care systems is at the foundation of the full recovery of the health systems’
capacity to absorb shocks of mis-handlings and negligence’s in the medical practice. Health care

systems often fail in presenting a consistent and seamless continuum to ensure effective patient
transitions, which has led to gaps in consistent patient care and thereby threatening the wellbeing
of patients. These gaps in the continuity of patient care is an evidence that the current health care
systems are practically unable to sufficiently meet the demand of the disturbances they receive.
A lack of system resilience in many EMRs and EHRs has seen the medical industry expend
financially in unnecessary compensations to families in the case of patient deaths as a result of
misdiagnosis or wrong drug prescription4. Therefore, clinicians and the hospital management
should demand resilient EMRs and EHRs from the software designers. Resilience is viewed as
the ability of health care systems to absorb disturbances and yet remain stable. However, in the
medical practice, system resilience incorporates health system reengineering to anticipate
absorption of future failures, while taking into consideration the growing complexity in the
emergence of new diseases and complications that require agility and intensive responses7.
iv. Poor quality of Data
Issues regarding the quality of data as outputted by EMRs have become relevant in the recent
years as adoption of these electronic databases has been ever increasing. Data quality can be
viewed as the ‘appropriateness of data,’ that is the fitness of data for use in executing a certain
task. When it comes to electronic medical databases, care should be taken in evaluating the
appropriateness of a database to a particular situation. For example, some databases perform
better in analyzing of patient data as opposed to dispensing clinical information. Negligence in
patient care can be reduced by re-evaluating the medical databases that are put in use in medical
health centers7.
For data to be described as quality, it should have accurate, relevant, and timely details that are
appropriate and fit for each and every situation at hand. It is important to note that an integrated
clinical coding system can only be implemented by designing a uniform clinical database.
Hence, to solve this issue, maintaining a standard clinical database is the only solution. This is
the case because health centers in different hospitals use different disease labelling criteria
depending on the setting2. This often poses a problem when a patient transfers to a new hospital
and their report conflicts with the hospitals labeling criterion, thereby creating confusion which
may end up in creating complications in drug prescription, in case the patient had some reactions
to some drugs.

Recommendations and Conclusion
Negligence in patient care and medication errors can be prevented by implementation of the
solutions for the issues already identified. The issues that have been identified in the medical
practice, are worthy of attention, for negligences in the medical practice has always been
expensive. To enhance the prevention of medication errors and the reactions from adverse drugs,
the clinical decision support systems can be optimized in a manner as to make them resilient, not
just smart8.
While implementation of decision support systems in the health care system has an evidence of
improving patient care, it should not be entirely substituted for personal attendance to patients.
There is a tendency to have overconfidence in the decision support systems that always comes
with a gross presumption that the EHR and EMR systems can work independently. Most
practitioners seldom make use of their professional skills and end up relying on these smart
systems for almost the entire process of attending to patients. This issue can be addressed by
encouraging an ethical and balanced implementation of the medical decision support systems to
encourage professionalism while attending to patients3.
Integration of all electronic medical records will also help in preventing common errors that are
often made during diagnosis and drug prescriptions. Integrating electronic medical records and
will help in creating a standard database for common diseases and complications that are
neglected or labelled differently in different settings9. For example in the case of Zoya, had the
EMR systems been integrated, there would have been a system hard stop on the basis of the
inference that cephalosporin should not be prescribed to a patient who has a history of an allergic
reaction to penicillin. While the standalone system in the hospital may not have captured the
error, an integrated information system using a standardized database would have prevented the
error through the network2.
To reduce drug prescription errors and misdiagnosis as in the case of Zoya, the decision support
system in the hospital, had it been resilient, should have prevented Dr. Stanley from making such
a gross error. The electronic record clearly indicated that Zoya had an allergic reaction to
cephalexin but the doctor to observe the entry. This warranted the death of the patient, despite of
the system’s knowledge of the condition. This is an evidence that the decision support systems in

the medical industry need not only be smart, but also resilient. Above all other industries, the
decision support systems in the health care industry should be most optimized for resilience10.
Quality of data that is inputted to or outputted from a decision support system is key in
determining safety of the patient. This is true both in hospitals and in pharmacies. For example in
the case study, the pharmacist who dispensed the ‘killer drug’ to Zoya, had he/she consulted an
EHR system instead of relying on Zoya’s explanation would have saved her life. Furthermore, in
the case whereby a hybrid electronic system is used like in the given case study, care should be
taken such that patient information is clearly recorded so as to be properly transferred into the
system without error9. Although patient results are confirmed by general practitioners before
being entered into the health care system database, it is often the case that the clinical conditions
and allergies of the patients are not always updated in a systematic way into the system, which
encourages gaps that may lead to negligence as in the case of Zoya7.
In conclusion, it is important to note that a lack of resilience in the decision support systems is
the largest contributor of practitioner-related patient deaths. It is worthy to note that the decision
support systems in the health care setting can greatly prevent potential diagnosis errors and
prescription of adverse drug events. However, these decision support systems can only be
effective as they are rightly optimized to be resilient. As in the given case study, Dr. Stanley’s
error could have been prevented had the electronic system been re-engineered for resilience6.
This is evidenced by the fact that the system failed to come to a hard stop, since the feature was
not implemented. Hence, I recommend that all decision support systems in the health care system
be re-engineered for resilience optimization.
Reference List
1. Abul-Husn NS, Manickam K, Jones LK, Wright EA, Hartzel DN, Gonzaga-Jauregui C,
O’Dushlaine C, Leader JB, Kirchner HL, D’Andra ML, Barr ML. Genetic identification
of familial hypercholesterolemia within a single US health care system. Science. 2016
Dec 23;354(6319):aaf7000.

2. Betancourt JR, Green AR, Carrillo JE, Owusu Ananeh-Firempong II. Defining cultural
competence: a practical framework for addressing racial/ethnic disparities in health and
health care. Public health reports. 2016 Nov 15.
3. Boulware LE, Cooper LA, Ratner LE, LaVeist TA, Powe NR. Race and trust in the
health care system. Public health reports. 2016 Nov 15.
4. Chase MW. The Journal Of Physical and Chemical Reference Data. 2016 Aug 26.
5. Chambers DA, Feero WG, Khoury MJ. Convergence of implementation science,
precision medicine, and the learning health care system: a new model for biomedical
research. Jama. 2016 May 10;315(18):1941-2.
6. Dumais S, Cutrell E, Cadiz JJ, Jancke G, Sarin R, Robbins DC. Stuff I've seen: a system
for personal information retrieval and re-use. InACM SIGIR Forum 2016 Jan 29 (Vol.
49, No. 2, pp. 28-35). ACM.
7. Fisher ES, Shortell SM, Savitz LA. Implementation science: a potential catalyst for
delivery system reform. Jama. 2016 Jan 26;315(4):339-40.
8. Frerichs L, Lich KH, Dave G, Corbie-Smith G. Integrating systems science and
community-based participatory research to achieve health equity. American Journal of
Public Health. 2016 Feb;106(2):215-22.
9. Makary MA, Daniel M. Medical error—the third leading cause of death in the US. Bmj.
2016 May 3;353:i2139.
10. Nelson R, Staggers N. Health Informatics-E-Book: An Interprofessional Approach.
Elsevier Health Sciences; 2016 Dec 8.