Data Flow and Workplace Design in Hospitals: Uncovering Relationships Hidden in Complex Multiple Systems (Essay Sample)

Data Flow and Workplace Design in Hospitals: Uncovering Relationships Hidden in Complex Multiple Systems
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Data Flow and Workplace Design in Hospitals: Uncovering Relationships Hidden in Complex Multiple Systems.
"Data is the next gold," quip commentators in multiple forums. One area that has resisted this mantra is the healthcare field; computers are however, breaking the Walls of Jericho each and every day. Many records are generated in healthcare, flowing from the admonishing in the industry that if it was not recorded, then it was not done. The creation and preservation of these records is mainly for legal reasons and secondly for continuity of care. Apart from the random use by students undertaking operational research and Clinical Quality Improvement teams establishing baseline data, this massive data has been making its way into archives to begin a countdown for safe and legally sound shredding, with no analysis to generate information. Data science offers a method to mine this treasure trove for insights into product and process improvements.
Data science has been embraced in the business fields where it now rules in targeted advertising, supply chain efficiency programs, and even in political messaging. Healthcare, however, presents unique challenges to the data scientist: health data is plagued with inconsistency, incompleteness, and inaccuracies; health processes are poorly formulated and uncertain; clinical pathways seldom cover all diseases and are especially insufficient in rare disease coverage. These peculiarities present a formidable barrier to analyzing hospital data (Kolvachuk et al., 2018). Thus, the stage is set to examine data mining briefly.
Data mining is the search for useful information in a forest of data. Kantardazic (2020) describes data mining as a repetitive process where success is defined by the discovery of new insights to reject or fail to reject a hypothesis. Data mining fits well in healthcare as it advocates exploration, with no predetermined notions of the ideal. We have also seen above that healthcare data and processes are highly variable due to the diverse inputs (patients and health workers), so an attempt to fit the data into a preconceived model would not work.
Yang and Su (2014) reviewed reports of the application of process data mining in designing clinical pathways. Clinical Pathways (CP) are structured multidisciplinary patient care plans. They have been used to standardize care, improve patient outcomes, improve communication and collaboration in care teams, and improve outcomes. However, the traditional method of preparing care plans through committees is subjective and yields static plans, posing problems in allocation and optimization of equipment and workforce. Therefore, designing CP is one area where the predictive and descriptive power of data mining finds use.
Kolvachuk et al. (2018) report a successful application of data mining to simulate a clinical pathway for acute coronary syndrome patients in a tertiary hospital in Russia. Acute coronary syndrome responses include emergency pharmacological treatments and delayed surgical and rehabilitative treatments. Due to the urgency and criticality of the condition, several parts of the care process are required to perform optimally: availability of medicines, theatre, ward and ICU beds, and various cadres of the healthcare team. Data analysts create simulations that organize inputs in a way to reduce or eliminate waiting times while at the same time ensuring that excess capacity is maintained at levels required to meet demand surges. Kolvachuk et al. (2018) managed to demonstrate that surgical capacity was the main bottleneck; increasing the capacity of parallel surgeries from three to four greatly reduced waiting time in all points of the healthcare journey for Acute Coronary Syndrome patients. Therefore, such simulations from data mining are crucial in designing rugged processes, improving resource utilization, and predicting hospital workload. They can also be employed in training and supporting scientific decision-making (Kolvachuk et al., 2018).
Explain what relationships would be helpful to the nurse administrator in making quality improvement decisions
Warshawsky, Havens and Knafl (2012) found that nurse managers excelled in organizations that fostered quality interpersonal relationships. Quality improvement requires everyone to participate; it would serve little purpose for the wards to be delightful for patients while administrators complicate the discharge processes with sloth and uncaring attitudes. Therefore, the nurse administrator is called upon to develop, grow and maintain quality interpersonal relationships with all health care team members when undertaking quality improvement programs. Healthy interpersonal relationships help build an organization—the employees are energetic, dedicated, and proactive and can be relied upon to offer suggestions for improvement of processes and monitor the effectiveness of interventions. In addition, proactive and engaged employees report failures promptly. The nurse administrator should break the three main barriers to healthy relationship building: social identity, communities of practice, and socialization to a professional identity to build interpersonal relationships in a hospital (Bartunek, 2010).
The workplace will be divided into social groups depending on race, tribe, state of origin, or spurious grouping like a speaker’s accent. Members derive security from such groupings and may be hostile to other co-workers who they categorized as outsiders. The nurse administrator is called upon to build interactions between social identities and, if successful, create an identity based on the organization.
The second barrier is communities of practice. These build in workplace due to "initiations," that may happen to employees in a unit, for example, group punishments, which cause members of a team to gel into an inter-dependent group with a constructed identity. At the extreme, this community of practice may exist as a break-away organization with its own norms, thus presenting resistance to the hospital administration. To build interpersonal relationships within communities of practice, the nurse administrator should organize cross-functional interactions at the workplace and in social gatherings.
Lastly, members of a profession may form groups based on professional identity. Like a community of practice, socialization into a professional identity requires the nurse administrator to organize cross-functional interactions across the various professions.
To conclude, the nurse administrator is called upon to build trusting and caring interpersonal relationships across any cleavages that may arise in the organization based on social group, a community of work, or professions.
Why is it important for the nurse executive to work closely with the advanced analytics professional?
Data analytics provide objective information for designing effective clinical pathways, optimization of equipment and workforce, prediction of hospital workload, and reliable decision support (Kantardzic et al. 2020). To achieve these ends, the nurse administrator should work closely with the advanced analytics professionals. Unfortunately, healthcare data presents unique problems to data analysts as it is regularly incomplete, inconsistent, and may have errors. To resolve these deficiencies, the nurse administrator, the clinical team, and support services need to work together with the data analysts to design manual and electronic forms that capture all the needed information, are easy to use and deploy, and for electronic records, that prompt the user to a