systems theory to solve issues in a healthcare provider’s erratic patient identification system

systems theory to solve issues in a healthcare provider’s erratic patient identification system

                                                                 Introduction
            Healthcare management has been the focus of intense management and technological improvement over the last three decades for reasons associated with patient welfare. Part of these developments have been improving the system through which patients are received, treated, and managed afterwards to ensure cost effectiveness, patient satisfaction, high quality services, and the lowest number of errors possible. One methods used in this process is utilizing the systems theory approach to investigate the possibility of healthcare management systems to have errors. This paper uses the systems theory to solve issues in a healthcare provider’s erratic patient identification system while offering viable solutions aligned with the institution’s vision, mission, and values and ensuring patient satisfaction.

Systems Theory

The patient identification department is part of the larger electronic patient health records management department. This system is responsible for assigning each patient under treatment at the healthcare institution a unique identity that professionals can easily identify while being safe from errors or tampering. As with all systems, this one contains several parts that form a conjoined structure that derives its efficacy from the contributions of each component (Meyer & O’Brien-Pallas, 2010). These components include; input, output, throughput, cycle of events, and negative feedback.

The input in such a system comprises of information and personnel that arrive at the hospital’s electronic patient health records database in their raw state. Usually, this entails the patient himself or herself presenting to the hospital and having their names, gender, age, physical characteristics, and initial parameters such as blood pressure, heart rate, and blood type recorded (Kohli, & Tan, 2016). Additionally, their symptoms go down on record as accurately as possible to enable treatment to commence.

The output in this open system is any state of mind or body that the patient emerges from this institution with. In this particular case, the patient should have benefited from treatment in an ideal situation. However, from a purely systems theory perspective, the output is satisfied customers whose health records have been updated with accurate information while their identity remains intact.

Throughput is the activity of processing input using the hospital’s information management system in tandem with medical personnel to derive a suitable output (Delcea & Ioana-Alexandra, 2016). Essentially, this entails capturing the patient’s pertinent information, recording them in the information management system for use by different professional during treatment up to recovery.

The cycle of events in a hospital environment must retain the entire system’s efficiency and objectives. Considering that hospitals are essentially open systems, then the cycle of events is such that the management must measure the extent of success in terms of patient records management success (Malvey, 2013). Such metric could define the entire system’s renewal process and define its sustainability.

Negative feedback in the systems theory perspective of healthcare entails identification of errors in the system to correct any leakages, bottlenecks or lags. Such information in the hospital includes patient complaints, legal suits, and even employee misconduct.

Open Systems Approach to Healthcare Problem

One of the most common problems in modern healthcare practice is errors in patient identification. Such mistakes can cause significant legal issues in case of misdiagnosis or treatment for the wrong medical problems. Therefore, hospitals are usually on the lookout for this issue, more so in the currently automated patient healthcare information management systems.

Using the open system theory or approach, this problem of errors in patient identification lies at the input and throughput levels. The input phase of a healthcare institution is concerned with the collection, verification, and recording of all relevant patient data (Meyer & O’Brien-Pallas, 2010). Therefore, the healthcare information management system must be in tandem with the national identification registry to verify the patient’s identity. It should also be in tandem with previous healthcare institutions where the patient has been to collect and verify any medical and personal identification data.

A large part of this process is also attached to the throughput phase of the open system. These abilities to collect, verify, record, and assign a unique identifier are key in preventing errors in patient identification. The medical personnel should also have access to the hospital’s healthcare management system to verify each patient’s identity to prevent mix-ups and confusion. All other parts of the open system in this healthcare scenario are dependent on the performance of the first two components. Mistakes done during the collection and processing of information results in flaw information management practices (Meyer & O’Brien-Pallas, 2010). Such practices result in errors such as those involving patient identities.

Goals, Outcomes, and Standards

Part of the process of rectifying flawed management practices involving patient information and identities includes seeking professional assistance from IT personnel. Such personnel would quickly analyze the entire open system and identify any gaps in the input and throughput components (Delcea & Ioana-Alexandra, 2016). Additionally, they could also include capabilities in the output to detect errors before presentation of the same information to medical personnel. In such a scenario, the desired outcome is a reduction of errors in patient identification at the medical center.

Part of the goals and objectives that could be translated into policies include the need for wearable identification tags on each and every patient regardless of whether they are in-patients or out-patients. Such tags would have written information about the patient’s name, gender, age, blood group, and medical codes defining their treatment. Additionally, these tags should have RFID technology that medical personnel can read and verify using handheld tablets (Almajali, Masa’deh, & Tarhini, 2016). Combined with the patient’s physical picture or characteristics, these initiatives could be made into institutional policies. Then hospital’s executive management would have to liaise with its financial management team to assess the financial commitment needed.

Alignment with Mission, Vision, and Values

The proposed solution to a problem involving patient identification errors is perfectly aligned with the hospital’s mission of providing satisfactory medical assistance and advice to all patients in an error-free treatment environment. It also aligns perfectly with its vision of being the best medical center in terms of customer welfare, professionalism, and quality of service. The solutions and its attached policy are similar to the medical center’s values of customer safety, professional integrity, and accountability.

Summary

Modern medicine has taken on a fair amount of technological innovation for the sake of customer safety, cost management, and quality service. However, issues still do come up as the partnership between medicine and technology continues to grow. One such problem is errors during customer identification, which is a serious issue.

Systems theory defines the treatment process in an open system environment that is affected by external factors. The problem of errors in patient identification can be solved by correcting the system’s input and throughput components abilities to collaborate with other medical center healthcare systems and interactive patient tags. These initiatives must be able to align perfectly with the medical center’s mission, vision, and core values.

References

Almajali, D. A., Masa’deh, R., & Tarhini, A. (2016). Antecedents of ERP systems implementation success: a study on Jordanian healthcare sector. Journal of Enterprise Information Management, 29(4), 549-565. doi:10.1108/jeim-03-2015-0024

Delcea, C., & Ioana-Alexandra, B. (2016). Fostering risk management in healthcare units using grey systems theory. Grey Systems: Theory and Application, 6(2), 216-232. doi:10.1108/gs-12-2015-0078

Kohli, R., & Tan, S. S. (2016). Electronic Health Records: How Can IS Researchers Contribute to Transforming Healthcare? MIS Quarterly, 40(3), 553-573. doi:10.25300/misq/2016/40.3.02

Malvey, D. (2013). The Fear Factor in Healthcare: Employee Information Sharing. Journal of Healthcare Management, 58(3), 225-237. doi:10.1097/00115514-201305000-00011

Meyer, R. M., & O’Brien-Pallas, L. L. (2010). Nursing Services Delivery Theory: an open system approach. Journal of Advanced Nursing, 66(12), 2828-2838. doi:10.1111/j.1365-2648.2010.05449.x