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Getting to Zero Adverse Errors

Getting to Zero Adverse Errors. Introduction. Lapses in patient safety are a major health care quality problem, and the occurrence of patient harm due to such lapses is remarkably common, causing tens of thousands of deaths each year.

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Getting to Zero Adverse Errors

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  1. Getting to Zero Adverse Errors

  2. Introduction • Lapses in patient safety are a major health care quality problem, and the occurrence of patient harm due to such lapses is remarkably common, causing tens of thousands of deaths each year. • A large majority of these lapses are preventable and are the unintended consequences of a highly complex and imperfect health care delivery system, in which individual minor mishaps sometimes combine to cause harmful—or even disastrous—results.

  3. Introduction • Although there is no national reporting of such occurrences, a number of standards require reporting at least some types of these adverse events • however, it is widely agreed that, even where there is required reporting, such events are grossly underreported. • The starting point in any improvement effort is to collect data. • Identifying where and when in the care process mishaps occur, and changing processes of care to reduce the chance of harm, requires reliable data about the occurrence of preventable adverse events.

  4. Introduction • Unfortunately, few such data exist, as there is no standardized national reporting system to provide information on the number and type of even the most serious preventable adverse events. • On the other hand, the public expects health care providers to take all appropriate measures to ensure that care is safe, and the public looks to government and other oversight bodies to make sure that such actions are taken.

  5. Introduction • The occurrence of a serious preventable adverse event in health care—e.g., operating on the wrong patient or wrong body part or transfusing the wrong type of blood into a patient—suggests (but does not prove) that a flaw exists in the health care organization's efforts to safeguard patients. • It is reasonable for the public to expect an oversight body to investigate such occurrences

  6. Introduction • Accountability entails both an obligation of health care providers to report on their performance and of oversight bodies to investigate specified occurrences, and to enforce compliance with accepted standards of care for ensuring safety. • Having a standardized set of reportable adverse events should facilitate fulfillment of this obligation.

  7. Reporting Adverse Events • For example, the practice followed in the USA to establish a list of serious adverse events was started by the Institute of Medicine (IOM)-USA. • As part of a comprehensive approach to improving patient safety, IOM recommended that health care errors and adverse events be reported in a systematic manner.

  8. Reporting Adverse Events • A National Quality Forum (NQF) was established in Feb., 2000 (a voluntary consensus standards-setting organization) with the mission of improving American Health Care through the endorsement of consensus-based national standards for the measurement and public reporting of health care performance data. • This was an initial step on the road to provide meaningful information about whether care is safe, timely, beneficial, patient-centered, equitable, and efficient.

  9. Reporting Adverse Events • NQF has a formal process by which it achieves consensus and endorses standards. • In addition to endorsing voluntary consensus standards, the NQF also functions as an “honest broker” for health care quality improvement, convening health care's many stakeholders to focus on specific quality-related issues, promoting the use of standards, linking quality measurement to strategies for quality improvement, providing leadership, disseminating information, and exchanging knowledge and ideas

  10. Reporting Adverse Events • In March 2000, NQF identified “a set of patient safety measurements that should be a basic component of any medical errors reporting system” . • NQF established a standardized core list of serious reportable preventable events, that would facilitate reporting of such occurrences. • To qualify for this list an event must be: • Unambiguous, usually preventable, and serious. • It should also be one of the following: • Adverse. • Indicative of a problem in a health care facility's safety systems. • Important for public credibility or public accountability.

  11. List of serious reportable events • The list of serious and preventable events developed by NQF contains 27 serious reportable events that should be reported and investigated by all health care facilities if they occur (see appendix). • The events are organized in six categories, five that relate to the provision of care: • Surgical, product or device, patient protection, care management, and environmental. • One category includes four criminal events, which includes illegal acts, or acts of misconduct, and are included because they could be indicative of an environment that is unsafe for patients

  12. Road Map to Improve Adverse Events • The data collected using the list of adverse events should be used in every way possible to actually improve patient safety. • There are various approaches for performance improvements: • PDPC • Juran improvement methodology: • Define problem • Diagnose Causes • Develop remedies • Replicate results

  13. Road Map to Improve Adverse Events • Six Sigma methodology (DMAIC): • Define: define the problem • Measure: define the various factors affecting the problem and measure the problem under different values of such factors. • Analyze: analyze the impact of the different factors on the problem and identify the root causes • Improve: propose a solution to the problem and implement it. • Control: put the new solution under control • Replicate: Repeat with other adverse events • There is a misconception about Six Sigma, that it requires using sophisticated statistical tools. However, many problems can be solved with simple tools (and statistical knowledge could be acquired).

  14. Case Study: Reducing Medication Errors • Assume we collected some data on medication errors in a healthcare facility. Data in table 2 is hypothetical data. • Define the problem: • We have an average medication errors of 895 per 1000 patient days. • Establish process baseline: • Draw an ImR chart to understand how the process performs.

  15. Establish Process Base Line Out of control situation Out of control situation • Out of control points should be investigated. If its causes can be prevented, it is removed from the data set

  16. Define the Factors Influencing the Problem • We need to investigate the factors affect the problem. We can look at medication errors by drug type. To do so we need data. • If the medication errors are already documented by drug type, then we will extract the required data from our records. If not, we will need to monitor the process over a reasonable time period and record data on medication errors by drug type.

  17. Define the Factors Influencing the Problem • Factor 1: • Drug type • The Pareto tells us that errors associated with Insulin alone accounted for 45% of the total. The first three errors accounted for 89%.

  18. Define the Factors Influencing the Problem • We need to dig more into details of medication errors associated with Insulin. • The previous Pareto was on the number of errors. We might be more interested about which of these errors caused harm to patients. • knowing which of these errors caused harm to the patient requires more data. We need to collect data about for harm by drug type and draw another Pareto chart.

  19. Define the Factors Influencing the Problem • Factor 2: • Harm to patient • Again, Insulin alone accounted for 39.5% of the harm due to medication. The three first drugs together accounted for 89% of the total.

  20. Define the Factors Influencing the Problem • The Pareto shows that again, harm due to heparin and Warfarin is large, which suggests we probably need to start new improvement projects to look into each of them individually. • The two Pareto charts confirms that Insulin is a major contributor (vital few) to the problem of medication errors, whether we were concerned about the number of errors, or the number of harmful events. • We now need to dig more into Insulin errors, and collect data on types of errors occurring in Insulin medication process.

  21. Define the Factors Influencing the Problem • The improvement team decided to brainstorm the types of medication errors occurring in their facility.

  22. Define the Factors Influencing the Problem • After colleting data, the following Pareto was constructed

  23. Define the Factors Influencing the Problem • The Pareto shows that omissions and improper dosage together account for 49% of the total. The first three categories account for 64% of the total. • At this point, we may choose to form three different teams; each team handles one of the three major contributors to the problem. • We can also construct a similar Pareto chart for both Heparin and Warfarin and see what major types of medication errors we will see in both cases.

  24. Analyze causes • Assume we decided to take omissions and brain storm for possible causes of omission. • A cause and effect diagram is constructed

  25. Analyze causes • Identify Root Causes • From the list of possible causes on the C-E diagram, the team selected possible root causes to test. • The solution to the problem will address the root cause. Data proved that the root causes for “Omission” were: • Drug unavailable • Nurse to patient ratio. • The solution would be: • Revisit drug ordering system and identify what drugs need to be available and in what quantities. • Adjust nurse to Patient ratio.

  26. Improve • An action plan must be developed for both solutions. • After implementing the solutions, a new performance baseline is developed for the medication process, using an ImR control chart. • It will be possible to reduce medication errors even more, if we investigate the errors associated with Heparin and Warfarin. • By repeating the whole cycle again, it is possible to keep reducing the medication until we reach the zero level.

  27. Comparison Before and After

  28. Control

  29. Appendix: List of Reportable Adverse Errors

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