Identify a human-technology interface you encountered in clinical practice which needs improving. Provide a detailed replacement plan for the interface incorporating one of the axioms presented in the readings this week. How can this interface improve patient care?

Post your initial response by Wednesday at 11:59 PM EST. Respond to two students by Saturday at 11:59pm EST. The initial discussion post and discussion responses occur on three different calendar days of each electronic week. All responses should be a minimum of 300 words, scholarly written, APA formatted (with some exceptions due to limitations in the D2L editor), and referenced.  A minimum of 2 references are required (other than the course textbook). These are not the complete guidelines for participating in discussions. Please refer to the Grading Rubric for Online Discussion found in the Course Resource module. 

” How many times am I going to tell you people that I am allergic to Heparin”. Gazed and petrified, I walked back out of the room to recheck the name for which the Heparin was intended. The latter was intended for the other patient in the room. That was during the full blown Covid Pandemic. In order to limit frequent entry in the Covid 19 Isolation room, patient wristbands were now taped on the door outside the semi-private isolation room. A tired brain during the medication administration rush hour could easily make a disastrous error.

We live in an era where communication between machine and man is at the highest. Society is embellished and made easy by software and hardware created to make our day-to-day life easier, get accurate information and minimize errors (Goodman-Deane et al., 2021)

The interaction between humans and that hardware or software is called the human interface(Goodman-Deane et al., 2021).

Examples of these interactions are diverse and include healthcare where our practice is inundated by interactions like these daily (Goodman-Deane et al., 2021).

With the interactions though come also problems including interface exclusions due to informatic illiteracy or bypassing interfaces due to loopholes or to cut corners. This paper discusses one such bypass: Workarounds during BAMA: Barcode Assisted Medication Administration (Goodman-Deane et al., 2021).

As nurses we are at the forefront of a multistep process of medication prescription and administration and we play a pivotal role in preventing medication errors (Veen et al., 2020).

The last 20 years have seen a huge digitalization in medication administration process This digitization includes prescription, administration and storage (Craswell et al., 2021).

Electronic BAMA are a tool used daily by nurses for safely administrating drugs and prevent errors (Veen et al., 2020).

Several studies have shown a considerable drop in medication errors since its implementation in healthcare (Veen et al., 2020).

Nonetheless, nurses do frequent Walkarounds in barcode scanning during medication administration(Veen et al., 2020). 

Workarounds in hospital consist of not scanning the barcode at all (most common), scanning a barcode which is not on the patient’s wristband, scanning the same medication for different patients and ignoring computer generated scanner alerts (Veen et al., 2020)

Discussed at my setting is the problem is the walkaround during medication administration of people in isolation rooms.

Times like the Covid pandemics are periods where such walkaround are not only present but numerous.

Unreadable barcodes, limiting frequency of entry into an isolation room, fear of infection all play a role in this walkaround.

The ever-mutating Covid 19 virus, the recent addition of Influenza and RSV infections all encourage training towards a possible inundation of hospitals with patients on isolation, hence a solution to the problem should be envisioned. Also, infectious disease still poses a consistent threat on human health both nationally and globally (2020).

A possible solution to the problem is the addition of a photo ID when the wristband is scanned. This will decrease the risk of errors during workarounds as obvious racial differences can prevent some errors. Differences for instance in skin complexion, can prevent the RN from administering a medication to the wrong patient.

Improving nurse to patient ratios is also an indirect way to combat nursing workarounds in BAMA (Veen et al., 2020). Improving rations will decrease nurse stress and burnout. Retaining current full-time nurses via different incentive is one step towards a solution in the multifactorial problem.

References:

Craswell, A., Bennett, K., Hanson, J., Dalgliesh, B., & Wallis, M. (2021). Implementation of distributed automated medication dispensing units in a new hospital: Nursing and pharmacy experience. Journal of Clinical Nursing, 30(19-20), 2863–2872. https://doi.org/10.1111/jocn.15793 

Goodman-Deane, J., Bradley, M., & Clarkson, P. J. (2021). Relating age, Digital Interface Competence, and exclusion. Gerontechnology, 20(2), 1–14. https://doi.org/10.4017/gt.2021.20.2.24-468.11 

The COVID-19 pandemic and paradigm change in global scientific research. (2020). MEDICC Review, 22(2), 14. https://doi.org/10.37757/mr2020.v22.n2.4 

Veen, W., Taxis, K., Wouters, H., Vermeulen, H., Bates, D. W., Bemt, P. M., Duyvendak, M., Oude Luttikhuis, K., Ros, J. J., Vasbinder, E. C., Atrafi, M., Brasse, B., & Mangelaars, I. (2020). Factors associated with workarounds in barcode‐assisted medication administration in Hospitals. Journal of Clinical Nursing, 29(13-14), 2239–2250. https://doi.org/10.1111/jocn.15217 

The EHR system has been credited with improving the quality of patient care, making patient information more accessible, improving efficiencies, reducing costs and medication errors, and increasing safety (HealthIT.gov, 2017; Hoover, 2017). There is a downside to the EHR system. It is associated with burnout, job dissatisfaction, and intention to leave physicians and nurses (Kutney-Lee et al., 2021; Zarefsky, 2020).

Technology is used more and more to replace hospital processes that were manual. An example of this happening is the process of updating medical records. Before the advent of the electronic health record (EHR), updating medical records occurred by writing notes into patient charts. Notes were entered during the visit and afterward. EHR systems have changed the manner in which updating medical records occurs. The EHR system is designed to have physicians update medical records during patient visits. Updating this way requires physicians to maintain eye focus on the computer screen, not the patient (Eberts & Capurro, 2019). Although this has not had a negative effect on the patient/physician relationship (Eberts & Capurro, 2019), the excessive amount of data entry has caused burnout for some physicians (Zarefsky, 2020). The amount of time needed to document EHRs has also caused nurses job dissatisfaction, burnout, and intention to leave (Kutney-Lee, 2021). This situation was particularly for nurses who spent less time in direct patient care because of EHR systems (Khairat et al., 2020).

Developing effective human-computer interaction needs a formal evaluation using rigorous experimental or qualitative methods. While there are studies on how health professionals perceive EHR systems, there are no studies on decreasing stress, burnout, and dissatisfaction concerning health professionals’ use of EHR systems. One reason is EHR systems are not that old. They have been in most hospitals for a little over ten years.  The idea that EHR systems can cause burnout in health professionals is also recent. It can only benefit healthcare if this human-computer outcome is rigorously studied and solutions found.

References

Eberts, M. & Capurro, D. (20198). Patient and physician perceptions of the impact of electronic health records on the patient-physician relationship. Applied Clinical Informatics, 10(4), 729-734.

Kutney-Lee, A., Brooks Carthon, M., Sloane, D. M., Bowles, K. H., McHugh, M. D., & Aiken,  L. H. (2021). Electronic health record usability: Associations with nurse and patient  outcomes in hospitals. Medical Care, 59(7), 625-631.

Zarefsky, M. (2020, October 21). 7 things about EHRs that stress out doctors. American Medical Association. https://www.ama-assn.org/practice-management/digital/7-things-about-ehrs- stress-out-doctors

As technology continues to advance in medicine, practitioners are becoming more comfortable with the human-technology interface. Embedded within electronic health records (EHRs), is the Clinical Decision Support System (CDSS) which is meant to enhance patient safety, increase provider efficiency, and optimize patient care (Fant & Adelman, 2018). CDSS is designed to combine evidence based medicine with patient data to produce specific recommendations for patient care (Fant & Adelman, 2018). 

     Efforts to improve medication related decision support would  help reduce alert fatigue. Admittedly, many medication alerts are inappropriately overridden as a result of alert fatigue. Studies have shown that practitioners override 52% of outpatient medication alerts, and 73% of inpatient medication alerts (Fant & Adelman, 2018). Common override alert types include duplicate drug, formulary substitution, and patient allergy (Justinia et al., 2021). Improving the medication alert system needs a redesign for more efficient provider workflow. This is best achieved using  an iterative design process, allowing for evaluation and correction of identified problems (Mastrian & McGonigle, 2021). 

     Clinicians should partner with Pharmacy staff and Information Technology staff to create a customized medication alert program for the EHR. Hospital surveys reveal that providers are overwhelmed with immense quantity of medication alerts, but could not identify which alerts were actually working and which were most beneficial (Lam & Ng, 2017). For this reason, clinicians from all services and specialties should be present in the discussion for optimal multidisciplinary end-user input. Considering other provider perspectives regarding overriding medication alerts will increase the knowledge needed to accomplish the goal of creating an  customized medication alert system (Mastrian & McGonigle, 2021). A twelve month plan will be developed and implemented to monitor the number and category of medication alerts that are overridden by providers. Once this data is collected and analyzed, drug-drug alerts can be reclassified. Next, duplicate therapy alerts should be analyzed and alert use by providers, which will also be reclassified. A study using a committee-led systematic approach to optimizing drug-drug interactions facilitated a decrease in the number of medication alerts and an increase in both medication acknowledgement alert and modification rates (Bhakta, et al., 2019). Since most of the modification work will be done by Information Technology staff, there should be minimal visual changes to the medication order page, minimizing stress for the providers by maintaining consistency and simplicity. Efficiency and effectiveness should be measured by reviewing the number of alerts that are acknowledged and those that are overridden to evaluate the benefit of the customization tool. Electronic surveys can be sent to confirm provider satisfaction. Because new medications are added to the formulary, monthly assessments should be done as part of ongoing optimization efforts to improve CDSS use. Updates should be made in order to comply with changing standards. Implementation of a successful customization of the medication alert CDSS will increase patient safety, thereby improving quality of care. 

References

Bhakta, S.B., Colavecchia, A.C., Haines, L., & Garey, K.W. (2019). A systematic approach to optimize electronic health record medication alerts in a health system. American Journal of Health-System Pharmacy, 76(15), 530-536. https://doi.org/10.1093/ajhp/zxz012

Fant, C. & Adelman, D. (2018). Too  many medication alerts; how alarm frequency affects providers. The Nurse Practitioner, 43(11), 48-52. Doi: 10.1097/01.NPR.0000544279.20257.4b

Justinia, T.,Qattan, W., Almenhali, A., Abo-Khatwa, A., Alharbi, O., & Alharbi, T. (2021). Medication errors and patient safety: evaluation of physicians’ responses to medication-related alert overrides in clinical decision support systems. Acta Informatica Medica, 29(4), 248-252. doi: 10.5455.aim.2021.29.248-252

Lam, J.H. & Ng, O. (2017). Monitoring clinical decision support in the electronic health record. American Journal of Health-System Pharmacy, 74(15), 1130-1133. doi: 10.2146/ajhp160819

Mastrian, K.G. & McGonigle, D. (2021). Informatics for the health professionals (2^nd ed.). Jones & Bartlett.