The Statistical Process Control Charts

Statistical Process Control Charts (Media) – Special Cause vs. Normal Variation

Control charts are used to monitor the stability and control of the process being improved over time. A control chart contains three main elements. There must be a time series graph with a central line depicting shifts. Having upper control limits and a lower control limit must be included as well. Control charts show historic trends over time and how well the new process is performing. Common cause variation is “fluctuation caused by unknown factors resulting in a steady but random distribution of output around the average of the data and a measure of the process potential, or how well the process can perform when special cause variation [is] removed” (“Common Cause Variation,” n.d., para. 1). The variation may be caused by unknown factors, or it can be a measure of the potential of a process.

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Quality/Safety – Relationship Between Adverse Events and Hospital Deaths

Quality and patient safety are of the utmost importance to health care organizations. Everything done by the organization is done to assure quality and safety. In 1999, the Institute of Medicine (IOM) published To Err Is Human: Building a Safer Health System. This paper discussed errors occurring at health care institutions that were causing patients to die. According to estimates from two major studies, “at least 44,000 people, and perhaps as many as 98,000 people, die in hospitals each year as a result of medical errors that could have been prevented” (IOM, 1999, para. 1). Since then, health care institutions have been working toward improving the quality of care delivered and preventing unnecessary patient deaths. One way to prevent death is to begin collecting information about adverse events and perform an investigation as to why the event occurred.

Study Examples

The following are examples of studies conducted using different research designs. Included in the examples are a retrospective chart review, a quasi-experimental study, a randomized control study, a qualitative study, and a QI study.

Example: Retrospective Chart Review

A retrospective chart review, titled Is Researching Adverse Events in Hospital Deaths a Good Way to Describe Patient Safety in Hospitals: A Retrospective Patient Record Review Study was performed to assess patient safety (Baines, Langelaan, Bruijne, & Wagner, 2015). The investigators wanted to understand if the adverse events of the living (those who were discharged home) had a relationship to those patients who died in the hospital. Could an adverse event predict patient safety?

A total of 11,949 charts were reviewed. Of that total, 50% were patients who had died, and the other 50% were patients who were discharged alive. The main outcome measures were adverse events in inpatient deaths and in patients discharged alive. The researchers looked at size, preventability, clinical process, and type of adverse event (Baines et al., 2015).

The retrospective chart study found that more information regarding adverse events was learned from the patients who died than from patients who were discharged alive. Many of the events were similar but were not representative of the number of adverse events. Patients who had died were older, had longer inpatient stays, and were more urgently admitted, but they were not generally admitted to a surgical unit. The researchers also found that the patients who died had more preventable adverse events than those who were discharged alive. It was also found that the patients discharged alive had more preventable adverse events related to a surgical process.

Example: Quasi-Experimental Study

In May of 2016, a large study was conducted to evaluate Medicare fee-for-service (FFS) readmissions after an intervention was applied to high-risk discharge patients. The study was funded by the CMS to reduce readmissions among all discharged Medicare FFS patients. The study, “Quasi-Experimental Evaluation of the Effectiveness of a Large-Scale Readmission Reduction Program” (Jenq, Doyle, Belton, Herrin, & Horowitz, 2016), was conducted at an urban academic medical center in New Haven, Connecticut, beginning in May 2012.

The interest in preventing readmissions comes from the financial penalties imposed by the Readmission Reduction Program of the Patient Protection and Affordable Care Act (ACA) of 2010. Patient readmissions occur when patients are admitted within 30 days from the date of discharge. If patients are readmitted within 30 days, there are penalties imposed as dictated by the ACA. Hospitals have been conducting smaller clinical trials to investigate readmission reduction methods. Most of these trials included fewer than 400 patients who received an intervention. The study being discussed enrolled 10,621 patients (Jenq et al., 2016).

The target population were patients older than 65 years with Medicare FFS insurance. Part of the inclusion criteria were that patients resided in nearby ZIP codes and were discharged alive to either home or another facility. Patients who left against medical advice or who were discharged to hospice were not included. The control population was made up of discharge patients and high-risk discharge patients older than 54 years with the same discharge status and ZIP codes, but who did not have Medicare FFS insurance.

The intervention provided to the target population included:

· Personalized transitional care,

· Education,

· Medication reconciliation,

· Follow-up telephone calls, and

· Linkage to community resources (Jenq et al., 2016, para. 4).

The program was implemented in Yale-New Haven Hospitals with a total of 1,541 inpatient beds on two campuses. The program had the support of “senior executive leaders who had made readmission reduction a hospital-wide quality improvement priority” (Jenq et al., 2016, para. 14).

It was found by providing the intervention to the target population that the readmission rate was reduced by 9.3% when interventions were applied over 19 months. Only 58% of the target population actually received the intervention. CMS was looking for a 20% decrease in the rate of readmissions. The study found patients who were discharged home were followed up by transitional care consultants who were hired specifically for this program. There were times when the elderly patients did not always benefit from all the community services they could have received. There was also help from the Area Agency for the Aging, which was able to provide resources about the community (Jenq et al., 2016).

Results:  We enrolled 10 621 (58.3%) of 18 223 target discharge patients (73.9% of discharge patients screened as high risk) and included all target discharge patients in the analysis. The mean (SD) age of the target discharge patients was 79.7 (8.8) years. The adjusted readmission rate decreased from 21.5% to 19.5% in the target population and from 21.1% to 21.0% in the control population, a relative reduction of 9.3%. The number needed to treat to avoid 1 readmission was 50. In a difference-in-differences analysis using a logistic regression model, the odds of readmission in the target population decreased significantly more than that of the control population in the intervention period (odds ratio, 0.90; 95% CI, 0.83-0.99; P = .03). In a comparative interrupted time series analysis of the difference in monthly adjusted admission rates, the target population decreased an absolute −3.09 (95% CI, −6.47 to 0.29; P = .07) relative to the control population, a similar but nonsignificant effect. (Jenq et al., 2016, para. 6)

Example: Randomized Control Study (Experimental)

In 2012, a randomized-control trial (RCT), titled “Early Childhood Family Intervention and Long-Term Obesity Prevention Among High-Risk Minority Youth”(Brotman et al., 2012), was conducted to test the hypothesis that family intervention can promote effective parenting in early childhood that will affect the rate of obesity in preadolescence.

Childhood obesity is a growing epidemic associated with an increasing incidence of hypertension, and diabetes and can be extremely costly. “Rates of overweight (BMI ≥ 85th percentile) have doubled among 2- to 5-year-olds over the past 3 decades; overweight preschool-aged children are 5 times more likely to be obese (BMI ≥ 95th percentile) at age 12 than non-overweight children” (Brotman et al., 2012, para. 1). Obesity prevention is especially important during early childhood, which has already been identified as a critical period. Two characteristics of effective parenting were identified. One characteristic was responsiveness and the other control. Responsiveness included parental warmth, sensitivity, and involvement. The second was parental control, which included expectations from the child by the parent, including aspects of self-control and parental discipline (Brotman et al., 2012).

The participants were divided into two follow-up groups. They were named Follow-Up Study 1 and Follow-Up Study 2. There was a total of 186 minority youth who were at risk for behavioral problems enrolled in this study. Forty of those were girls enrolled into Follow-Up Study 1, and the remaining 146 children were enrolled into Follow-Up Study 2. There was long-term follow-up after random assignment to family intervention or control condition, which occurred at age 4. The study design included two RCTs. The first follow-up study enrolled 99 children, including 40 girls who had a familial risk for behavior problems. The second follow-up study enrolled 496 children, including 146 boys and girls at risk for behavioral problems. Neither intervention targeted obesity, nor addressed nutrition and activity of the children. The researchers did provide behavioral family interventions. Interventions included “weekly 2-hour parent and child groups over a 6-month period. Descriptions of the interventions and positive effects on parenting (e.g., responsiveness, control) and child behavior (e.g., aggression, social competence, stress response) have been reported” (Brotman et al., 2012, para. 12).

BMI and health behaviors were measured an average of 5 years after intervention in Study 1 and 3 years after intervention in Study 2. The results showed that youth in the intervention group had significantly lower BMI at follow-up than did youth in the control group.  There were also significant differences on blood pressure, diet, and physical activity demonstrated by both groups. Successful obesity prevention could have a huge impact on public health considering that high-risk minority groups are at risk of being obese. Further inquiry is needed regarding effective parenting, which can be seen as promising after analyzing the results of this trial (Brotman et al., 2012).

Example: Qualitative Study (Nonexperimental)

A study, titled “A Qualitative Study of Experienced Nurses’ Voluntary Turnover: Learning From Their Perspectives”(Hayward, Bungay, Wolff, & MacDonald, 2016),was conducted by performing interviews of 12 registered nurses. The 12 nurses included in the study had an average number of 16 years in practice in a wide variety of inpatient acute care settings. The researchers developed a hypothesis about what factors would contribute to the experienced nurses’ reason to voluntarily leave their jobs and pursue other avenues.

The purposive sample of 12 nurses included four who worked part-time and eight who worked full-time. The sample size was small because of the abundance of information collected from interviews in qualitative studies. The researchers believed that by choosing only nurses who had resigned to participate in the study, they could focus on the specific issues that were being explored, such as nurse fatigue and workload demands. While nurses who had not resigned also felt the same way, but were unable to resign due to daily and personal problems. The selection is vital to the outcome of the study.

The nurses’ decisions to resign were a combination of work environment and personal factors. Major themes that ran through the interviews included “higher patient acuity, increased workload demands, ineffective working relationships among nurses and with physicians, gaps in leadership support and negative impacts on nurses’ health and well-being” (Hayward et al., 2016, para. 5). Other reasons, including poor relationships with co-workers and lack of leadership, led to job dissatisfaction and their decision to leave. Lack of leadership support led the nurses “to feel dissatisfied and ill equipped to perform their job. The impact of high stress was evident on the health and emotional well-being of nurses” (Hayward et al., 2016, para. 5).

Example: Quality Improvement Study

Patient satisfaction is a very important indicator of the perception patients have of the care they received during their hospital stay. One of the quality indicators is communication with physicians. While in the hospital, patients are usually ill, and it was questioned whether patients properly remembered their provider and the communication that took place. The study, “Positive Impact on Patient Satisfaction and Caregiver Identification using Team Facecards: A Quality Improvement Study” (Martin et al., 2017), was conducted to see if patients could remember their interactions with their health care providers by having facecards with the name, picture, and specialty of the physician to improve the ability of the patient to identify members of their health care team.

The facecards were given to patients during the interventional period of the study. Each facecard identified physicians and their specialty. There were 192 patients included in the study, with 50% of the patients in the interventional group receiving the facecards identifying their physicians and their role and the remaining 50% of the patients in the control arm, who did not receive the facecards. All 192 patients received a survey to complete after discharge (Martin et al., 2017).

Results: A total of 192 patients completed the survey. They were divided into a control group (n = 96, 50%) and an interventional group (n = 96, 50%) during the period of the study (February 2016–August 2016). Patients who received the intervention were more likely to identify: their team attending (71 [74%] in the interventional group vs [34.4%] in the control group; P < 0.001); team resident (40 [40.7%] in the interventional group vs 25 [26%] in the control group; P = 0.0222); team intern (42 [43.8%] in the interventional group vs 19 [19.8%] in the control group; P = 0.0004). Patients in the interventional group reported slightly higher level of satisfaction (72 [75%] reported level of satisfaction > 9 on a scale of 1 to 10 in the interventional group vs 59 [61.5%] in the control group). (Martin et al., 2017, para. 5)

Use of facecards improved patient identification of primary team members and roles; however, patients still lacked enough knowledge of provider roles. The use of facecards showed a slight improvement on overall patient satisfaction (Martin et al., 2017).

Future/Trends – Patient-Centered Care and Shared Decision Making

The IOM has identified the six domains of health care quality. They are composed of different domains that make up an analytical framework that guides development initiatives for quality in the public and private sectors. Most measures address effectiveness and safety, while others address patient-centeredness, timeliness, efficiency, and equity of care.

· Safe: Avoiding harm to patients from the care that is intended to help them.

· Effective: Providing services based on scientific knowledge to all who could benefit and refraining from providing services to those not likely to benefit (avoiding underuse and misuse, respectively).

· Patient-centered: Providing care that is respectful of and responsive to individual patient preferences, needs, and values and ensuring that patient values guide all clinical decisions.

· Timely: Reducing waits and sometimes harmful delays for both those who receive and those who give care.

· Efficient: Avoiding waste, including waste of equipment, supplies, ideas, and energy.

· Equitable: Providing care that does not vary in quality because of personal characteristics such as gender, ethnicity, geographic location, and socioeconomic status. (AHRQ, 2016, para. 2)

These frameworks make it easier for consumers of health care to understand quality. The quality measures collected from health care organizations can be classified as structure quality measures, process quality measures, or outcome quality measures. When consumers evaluate quality measures, they can make educated decisions by comparing different organizations prior to choosing their health care provider.

The measures can provide valuable information about the organization. If one wanted to evaluate structural quality measures, then information regarding electronic health records or medication safety systems, as well as the number of board certified physicians or the physician-to-patient ratio could be explored. Process measures can give consumers information about preventative services and the status of their patient population, including how many are well and how many are ill. The outcome measures can tell consumers mortality rates and the rate of surgical complications or hospital-acquired infections of the organization being investigated (AHRQ, 2015).

Reflective Summary

Being able to understand basic research statistics will empower the health care professional to understand the results of research studies. For example, research articles can deepen the health care professional’s understanding of every aspect of health care, including new medications, new treatments, and new procedures. Understanding basic research designs will allow health care professionals not only to gain valuable knowledge by reading journals, but to advance that knowledge. Knowing the differences of quantitative research from qualitative research will enable health care professionals to tell the differences in the value of numerical data from data consisting of words and themes. Health care professionals need to know and understand the results of research, which leads to evidence-based practices, which leads to the goal of all health care professionals—to provide patients with safe quality care.

Key Terms

Case Study: A method of qualitative research in which one focus is studied.

Conceptual Framework: An analytic tool used to build a research study, this defines the tools needed to answer the research question and the variables the researcher or investigator will encounter along the way.

Control Group: The group of subjects not receiving the treatment; the sample not receiving the intervention being studied; also known as comparison group.

Correlational Research Design: A type of quantitative research that is not controlled and aims to understand relationships between variables.

DMAIC: The define, measure, analyze, improve, control approach to improving a process.

Ethnography Research Design: A method of qualitative research design focused on understanding people and cultures; often studied through observation.

Evidence-Based Practice: The integration of clinical expertise, the most up-to-date research, and patient’s preferences to formulate and implement best practices for patient care.

Experimental Group: The group in a research study that receives the experimental drug, treatment, or procedure.

Experimental Research Design: A type of quantitative research design that is highly controlled to study cause and effect with independent and dependent variables.

Extraneous Variables: A variable that can influence the relationship between the independent and dependent variables; can be controlled either through research design or statistical procedures; were not foreseen or known at the beginning of the study.

FADE: A four-step strategy for quality improvement—focus, analysis, development, execute/evaluate.

Generalization: The degree the findings can be generalized from a sample to a larger population.

Grounded Theory Research Design: The collection and analysis of data from interviews and/or observation.

Hierarchy of Evidence: A core principle of evidence-based practice that defines levels of evidence from weak to strong.

Hypothesis: A testable statement of a relationship; an epidemiologic hypothesis is the relationship is between the exposure (person, time, and/or place) and the occurrence of a disease or condition.

Independent Variable: The experimental or predictor variable. It is manipulated in the research to observe the effect on the dependent variable.

Institutional Review Board (IRB): The group assembled to review research proposals and monitor progress to ensure protection of human subjects.

Internal Validity:The ability of the researcher to minimize external influence on the data achieved in the study.

Interval Level of Measurement: The variable has rank order and equal distances on the points of the scale.

Lean Method: A quality improvement strategy in which every employee at every level is made to feel empowered to find and solve problems.

Meta-Analysis: A statistical method used to evaluate the results of a systematic review.

Nominal Level of Measurement: Used to name or categorize things; the first level of measurement.

Nonexperimental Research: A research study that does not involve an experimental drug, treatment, or procedure.

Ordinal Level of Measurement: Defines the relationship between things and assigns an order or ranking to each thing; the second level of measurement.

PDSA Cycle: A strategy tool for quality improvement: plan, do, study, act.

Phenomenology Research Design: Qualitative research method used to study people through their lived experiences.

Prevalence: Describes data collected regarding health care related illnesses, conditions, and outcomes.

Qualitative Research: Research design using nonnumeric variables.

Quality Improvement (QI): A systematic and formal approach to collecting, analyzing, and disseminating data in order to improve services or products that a business renders.

Quantitative Research: Research performed by evaluating numbers and numeric variables that result in measurable data.

Quasi-Experimental Research Design: A type of quantitative research design that is partially controlled that studies cause and effect of variables.

Randomization: A method used like chance, such as flipping a coin.

Randomized Control Trials (RCTs): Research studies in which patients are chosen at random to receive the treatment/intervention being tested; considered the gold standard of research design.

Ratio: A comparison of any two numbers by division.

Ratio Level of Measurement: A measurement level with equal distances between the points and a zero-starting point.

Redundancy: Thisoccurs when information collected is repetitive and no new information is being gathered.

Retrospective Chart Review: A medical record review that collects data to answer a question.

Rigor: The accuracy and consistency in data collection.

Saturation Point: Occurs when no new data are being generated, and the endpoint of the qualitative study is defined.

Six Sigma: A quality improvement strategythat investigates sentinel events to learn to prevent future events.

Systematic Review: Literature review that summarizes evidence by identifying, selecting, assessing, and synthesizing the findings of similar but separate studies.

Transferability: How applicable the results are to other subjects and in another context.

References

Agency for Healthcare Research and Quality. (2013). Plan-do-study-act (PDSA) cycle. Retrieved from https://innovations.ahrq.gov/qualitytools/plan-do-study-act-pdsa-cycle

Agency for Healthcare Research and Quality. (2015). Types of quality measures. Retrieved from https://www.ahrq.gov/professionals/quality-patient-safety/talkingquality/create/types.html

Agency for Healthcare Research & Quality. (2016). The six domains of health care quality. Retrieved from https://www.ahrq.gov/professionals/quality-patient-safety/talkingquality/create/sixdomains.html

Agency for Healthcare Research and Quality. (2017). Section 4: Ways to approach quality improvement process. Retrieved from https://www.ahrq.gov/cahps/quality-improvement/improvement-guide/4-approach-qi-process/sect4part2.html

American Nurses Association. (n.d.). Nursing research. Retrieved from http://www.nursingworld.org/EspeciallyForYou/Nurse-Researchers

Baines, R. J., Langelaan, M., Bruijne, M. C., & Wagner, C. (2015). Is researching adverse events in hospital deaths a good way to describe patient safety in hospitals: A retrospective patient record review study.Retrieved from https://bmjopen.bmj.com/content/5/7/e007380

Brotman, L. M., Dawson-McClure, S., Huang, K. Y., Theise, R., Kamboukos, D., Wang, J., . . . Ogedegbe, G. (2012). Early childhood family intervention and long-term obesity prevention among high-risk minority youth. Pediatrics, 129(3), e621-e628. doi:10.1542/peds.2011-1568

Common cause variation. (n.d.). In iSixSigma dictionary. Retrieved from https://www.isixsigma.com/dictionary/common-cause-variation/

Department of Health and Human Services. (2011). Quality improvement. Retrieved from https://www.hrsa.gov/sites/default/files/quality/toolbox/508pdfs/qualityimprovement.pdf

Glasgow, J. (2011). Introduction to lean and Six Sigma approaches to quality improvement.Retrieved fromhttps://www.qualitymeasures.ahrq.gov/expert/expert-commentary/32943/introduction-to-lean-and-six-sigma-approaches-to-quality-improvement

Hayward, D., Bungay, V., Wolff, A. C., & Macdonald, V. (2016). A qualitative study of experienced nurses’ voluntary turnover: Learning from their perspectives. Journal of Clinical Nursing, 25, 1336-1345. doi:10.1111/jocn.13210

Institute of Medicine. (1999). To err is human: Building a safer health system. Retrieved from http://www.nationalacademies.org/hmd/~/media/Files/Report%20Files/1999/To-Err-is-Human/To%20Err%20is%20Human%201999%20%20report%20brief.pdf

Jenq, G. Y., Doyle, M. M., Belton, B. M., Herrin, J., & Horowitz, L. I. (2016). Quasi-experimental evaluation of the effectiveness of a large-scale readmission reduction program. JAMA Internal Medicine, 176, 681-690. doi:10.1001/jamainternmed.2016.0833

Kabisch, M., Ruckes, C., Seibert-Grafe, M., & Blettner, M. (2011). Randomized controlled trials: Part 17 of a series on evaluation of scientific publications. Deutsches Ärzteblatt International108(39), 663–668.

Martin, N. M., Odeh, K., Boujelbane, L., Rijhwani, M. V., Olet, S., Noor, A., . . . Battiola, R. (2017). Positive impact on patient satisfaction and caregiver identification using team facecards: a quality improvement study. Journal of Patient Centered-Research & Reviews, 4(4), 263. Retrieved from https://digitalrepository.aurorahealthcare.org/jpcrr/vol4/iss4/27/

Patient Protection and Affordable Care Act, Pub. L. 111-148, 124 Stat. 119 (2010).

Suresh, K. P. (2011). An overview of randomization techniques: An unbiased assessment of outcome in clinical research. Journal of Human Reproductive Sciences, 4(1), 8-11. doi: 10.4103/0974-1208.82352

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