Statistical Thinking in Healthcare: How to Use Data to Improve Patient Care

Prescription errors in pharmacy result from mistakes during the prescription writing process or a prescribing decision (Gursanscky et al. 2018, p. 29). It includes errors made by a physician like incorrect frequency of administration, wrong drug selection, and wrong route. Also, wrong instructions of using the drug, dosage form, wrong drug, or allergic drugs cause prescription mistakes. They also include incorrect following of (CDS), Drug-Drug Interactions (DDI), wrong patient errors, and bad Controlled Drug Substances (CDS). Therefore, statistical thinking is crucial in the pharmacy industry, especially when prescribing drugs. When reading about HMO’s Pharmacy problems, it is evident that they have challenges that only critical statistical thinking can solve.

Process Map and SIPOC Model

For effective comprehension of the problem, we should focus on the critical aspects of statistical thinking; process, variation, and data (Britz et al. 1997, p. 77). Therefore, understanding the whole process helps to identify the possible problems and improve HMO’s pharmacy system.

The critical problem defined was “ inaccurate prescriptions ” that led to numerous complaints as well as a few lawsuits faced by the pharmacy. Using the SIPOC model, we identify the main components of the prescription process.

Supplier	Input	Process	Output	Customer
Patient	Medications	As described above in the Process Map	Medicine	Patient
Physicians	Prescriptions
Pharmaceutical Distributors

Causes of Inaccurate Prescriptions

Typing wrong info 

Contaminated drugs due to wrong conditions 

Misunderstand prescriptions 

Unorganized 

Wrong prescription amount 

Wrong procedure 

Improper info 

Illegible info 

TW 

After the SIPOC model, it becomes easier to analyze these components while brainstorming possible causes of inaccurate prescriptions. Using the cause-and-effect diagram is vital at this stage because it displays a variety of possible causes to the problem as well as the required data for collection. 

The cause-and-effect diagram elaborates how some causes may be related to the prescription itself, considering a large number of doctors that have illegible handwriting leading to misconstrued information while prescribing the drugs. Other possible causes of inaccurate prescription result from the pharmacy assistants errors like typing the wrong data or the pharmacy’s environment that is propitious for mistakes. According to Gursanscky et al. (2018), prescription writing errors are among the most common avoidable mistakes which make it a vital area for improvement among doctors (p. 27). All processes in various institutions face variations that require minimization that effectively improves the system by correcting these errors (Lawson et al. 2018, p. 147). These variations can either be the expected variation (common cause) or the unexpected variation (special cause). By plotting the data described above, the wrong prescription is widely considered as an expected variation because it is always prevalent in the stable prescription process among different healthcare settings.

Main Tools

When dealing with such a problem, the first step toward analytical solutions requires forming a team that can correct the problem. Therefore, forming a team comprising of doctors, pharmacists, and pharmacy assistants would be the first tool. Such a team helps in identifying and implementing process changes that reduce inaccurate prescriptions ((Britz et al. 1997, p. 77). By using the DMAIC Framework, the team can make the required improvements to the HMO business process. This approach contains five stages:

Define : This stage contains activities whereby the team flowcharts the processes used in creating prescriptions and keep a daily count of the number of incidents with inaccurate prescriptions

Measure : This involves collecting the data of inaccurate prescriptions and plotting in the respective charts.

Analyze : The data is then monitored using a control chart, and investigating sources of the expected or unexpected variations with proper adjustments made.

Improve : The common causes and particular causes of inaccurate prescriptions should be determined and eliminated. In this case, particular causes are illegible handwritings, inadequate training, and rush jobs.

Control : After identifying the causes and problems, the prescribing process needs improvement or redesigning depending on the errors found. After eliminating the causes to the problem, HMO Pharmacy continues monitoring the process to avoid a repeat of the mistake.

Solution and Measurement Strategy

Systems-View Solution

For a practical solution in HMO Pharmacy, the working team must view the prescription process in the form of a system context. By maintaining a focus on improving the whole system, then the improvement ideas avoid the results of becoming turf battles (Britz et al. 1997, p. 78). Additionally, the systems view avoids sub-optimization whereby they solve the wrong prescriptions only to create another like ineffective communication. Therefore, by ensuring that all entities in the prescribing system acknowledge the aim of the HMO Pharmacy, it reduces inaccurate prescriptions. It is whereby management creates corporation between variously involved parties-doctors, pharmacists, and pharmacy assistants (Britz et al. 1997, p. 78). By working together, it reduces assigned blames and finger pointing whereby the doctors acknowledge that they need to write legible prescriptions, the pharmacists know that they should accurately understand the prescription and the assistants know to ask for help if they do not understand the doctor’s writing.

Measurement Strategy

To measure the solution above, the HMO Pharmacy should continue creating process charts and control charts that help them understand the changes that aid in improvement and the ones that do not. Similarly, analyzing and comparing customer complaints should be continuously counted and monitored (Britz et al. 1997, p. 77). By monitoring the causes of the complaints, they reveal commonalities that identify necessary process changes.

References 

Britz, G., Emerling, D., Hare, L., Hoerl, R., & Shade, J. (1997). How to teach others to apply statistical thinking.  Quality Progress ,  30 (6), 67-79.

Gursanscky, J., Young, J., Griffett, K., Liew, D., & Smallwood, D. (2018). Benefit of targeted, pharmacist‐led education for junior doctors in reducing prescription writing errors–a controlled trial.  Journal of Pharmacy Practice and Research ,  48 (1), 26-35.

Lawson, T., Weekes, L., & Hill, M. (2018). Ensuring success and sustainability of a quality improvement project.  BJA Education, 18 (5), 147-152.