Types of Waste in Healthcare, Case Study Example

Introduction

In the article “Types of Waste in Healthcare,” author Molly Gamble addresses the issue of waste in the industry, and notes that the term encompasses much more than simply misusing inventory items or other similar forms of waste. In the context of the healthcare industry, “waste” includes a variety of issues, from procedures that are done incorrectly and must be redone to time wasted in moving patients unnecessarily to employees who do not maximize their own potential. Among the notable forms of waste identified by Gamble is waiting time for patients. This waiting time is often a reflection of waiting time for health care workers as well, as poor planning and time management can result from workloads that are not level (Gamble). Innovations in the health care industry related to improving wait times are being built around new technologies and approaches to streamlining and time management that are designed to minimize the amount of time patients are kept waiting for appointments, procedures, tests, and other routine and emergency activities.  In the research study “Improving Wait Times in Primary Care Using the Dartmouth Microsystems Improvement Curriculum,” the researchers examined how wait times could be minimized through the effective application of technology and planning.

The Research Study

According to Malloy et al (2013), long patient wait times in the health care industry have far-ranging implications and potential consequences. While patients may be inconvenienced and frustrated by long wait times, there are a variety of other factors at stake. In recent years the trend in healthcare has been to focus on patient-centered care, a model which is intended to ensure that patients are engaged and involved in the ongoing process of managing their own health care, and are given appropriate information needed to make well-informed decisions in tandem with their health are providers. The move towards patient-centered care has been shown to lead to improved health outcomes, and has also been shown to lead to increased patient satisfaction. While the improvement in outcomes would certainly be reason enough to adopt a patient-centered approach to health care, there are also practical and economic reasons to take such an approach.

Among the practical reasons to provide patient-centered care is that it can lead to improvements in efficiency for health care providers; as procedures and workflows are revamped and restructured to focus on patient-centered care, these changes often lead to a top-down shift towards overall efficiency and streamlining of workloads (). Beyond these practical implications are the economic incentives and mandates that are driving the shift towards patient-centered care. Under the Affordable Care Act (ACA) and other public and private systems, reimbursements and payments for services to providers are increasingly ties to indicators of patient satisfaction and positive outcomes. Rather than tying payments to procedures, tests, or other activities, payments are being tied to the outcomes they produce. This approach is intended to ensure that providers are delivering health care based on what is best for the patient, as opposed to what reimbursements or payment s are doled out for particular procedures.

With payments also being tied to patient satisfaction, it is clear that any improvements that can be made in patient wait times are likely to have at least some positive effect on patient satisfaction ratings. To the degree that such improvements also serve to drive efficiency in health care organizations, they may also lead to improved outcomes for patients. With this in mind, the prospect of improving patient wait times may offer a number of benefits to patients, health care workers, and health care organizations. In the study by Malloy et al, the researchers focused on the Dartmouth Microsystem Improvement Curriculum (DMIC) in an effort to determine whether it could help reduce wait times in a primary care setting.

At the core of the factors used to assess patient wait times in the primary care setting is the “office cycle time,” which is described by Malloy et al as “the amount of time in minutes that a patient spends during the entire course of an office visit beginning with the time a patient enters the facility to the time the patient leaves.” This office cycle time has been shown in studies to be directly associated with patient satisfaction, and the study by the authors examines a system designed to reduce this office cycle time. The study was conducted at a primary care facility that had recently been formed out of a merger of two clinics, and was experiencing longer wait times than was desired.

In an effort to reduce the office cycle time, the clinic implemented the DMIC framework as an improvement process. According to Malloy et al, “the DMIC framework engages frontline staff in continuous examination of processes and performance at the microsystem level.” The authors describe Microsystems as “the building blocks of larger systems” (Malloy et al, 2013). The DMIC is designed to help health care workers identity all of the components of any system or process utilized in the workplace, and to further identify and implement improvements to those systems.  In order to assess the efficacy of the DMIC, the researchers waited for three months before they began to collect data; this allowed time for the workers at the clinic to become fully trained on DMIC procedures and to begin to utilize them in their practice. During this time the staff and management identified a series of goals and benchmarks that, if met, would be considered evidence that the DMIC was effective. Several key areas were identified in which patients might have to wait, from their initial arrival in the waiting room to time spent waiting in exam rooms to post-exam wait times for referrals and discharge instructions.

Once this period was over, the researchers began to collect data by using a patient survey. Ever patient was offered the survey, and the researchers reported a positive response rate in statistical terms. The survey was structured to gather wait time data as assessed by patients, and was correlated against a wait-time collection instrument designed for the study that was attached to patient charts and filled out by staff members at different stages of the patient cycle. The patient survey included sections for patients to remark on their perceptions of wait times, and on their overall satisfaction.  The surveys and collection instruments were implemented in a pre0intervetion phase and a post-intervention phase to determine whether the DMIC intervention had a positive effect on wait times.

The researchers’ collected 406 surveys in the pre-intervention phase and 397 in the post-intervention phase. The response rates for the two phases were 21% and 13% respectively. According to the analysis of the surveys, the intervention produced “statistically significant improvements” in wait times, and also led to improvements in patient satisfaction ratings. The data gathered by the researchers was included in the report in the form of two tables which included the pertinent figures included in the pre-intervention and post-intervention phases of the study.

Assessment

The study conducted by Malloy et al appears to have been constructed in an effective manner. It accounted for a number of significant factors, including gathering data both before and after the DMIC intervention.  Moreover, it included collection instruments deigned to gather responses from patients along with data on wait times provided by staff members during the study times. This made it possible to cross-check the information offered by patients with the hard data from staffers, a structure that allowed patient perceptions to be weighed against changes in wait times. The only improvements to the data collection tools that might have made a notable difference would be a collection tool that standardized responses from patients about wait times. The following graph demonstrates how this value could be assessed and put in bar chart form.

The bar chart breaks wait times into three categories, standard care, patient centered care, and DMIC, and the y-axis represents the average amount of minutes each patient had to wait in the survey group. Compatible with the data retrieved, it clearly shows that while patient centered care cut down patient wait times significantly, DMIC had the most impact.

In sum, as structured, the study left it to patients to make determinations about their wait times. A data collection instrument that utilized a simple electronic stopwatch to be used by patients during their office cycle time would have been useful. This would allow patients to become more fully engaged in the study process, and would have made it possible to check wait times as recorded by patients against wait times recorded by staff. In total, however, this study appears to have been both well-conceived and well-executed, and it demonstrated that the DMIC had a positive effect on both patient wait times and on patient satisfaction.

References

Ernst, and Young, (2013). Health Care Industry Report. [online] Available at: http://www.ey.com/Publication/vwLUAssets/New-horizons-2013-August-23-2013/$FILE/New-horizons-2013-August-23-2013.pdf [Accessed 15 Sep. 2014].

Gamble, M. (2013). 8 Types of Waste in Healthcare. [online] Beckershospitalreview.com. Available at: http://www.beckershospitalreview.com/hospital-management-administration/8-types-of-waste-in-healthcare.html [Accessed 15 Sep. 2014].

Malloy, C., Little, B. and Michael, M. (2013). Improving Wait Times in Primary Care Using the Dartmouth Microsystem Improvement Curriculum. Journal of nursing care quality, 28(3), pp.250–256.

Patient wait times for appointments cut by more than 80%. (2013). Annals of Psychotherapy and Integrative Health, 16(2).