Malpractice in Hospitals - Research Proposal Example

NAME: xxxxxxxxxx TUTOR: xxxxxxxxx DATE: xxxxxxxxxx ©2015 Introduction Poor, below par quality in hospitals got highlighted by the numerous malpractice cases documented in recent times. Cheryl Clark (2015) identifies the six quality issues that warrant attention in the medical profession in the USA as misdiagnoses, star ratings, socioeconomic adjustment for readmissions, end of Partnerships for inpatients initiatives, Medicaid parity expiration. Amongst the various issues, misdiagnosis has been cited the most common quality concern in medical institutions (Clark, 2015; Jayathi, 2014; Sifferlin, 2013). Misdiagnosis refers to the failure in diagnosis or the delay in diagnosis, the latter that would be normally severe the medical condition. Misdiagnosis could lead to fatalities with estimates of about 1 in 20 patients dying due to diagnostic errors (Isabel Healthcare, 2015). Isabel Healthcare (2015) suggests that diagnosis errors may get caused by flaws within a hospital such as a physician’s inadequacy in medical knowledge and changing patterns of disease, clinical negligence, failure to follow up, non-communication of the test results, faulty information processing and the inability to review the diagnosis. Diagnosis errors are therefore errors of omission that are usually difficult to identify. Cases of misdiagnoses have only surfaced through malpractice litigation, post mortems and scattered feedback from patients and physicians (Isabel Healthcare, 2015). Hospitals have to set therefore up systems to prevent the occurrence of misdiagnoses outright. To achieve this statistical process control techniques will be applied to determine the stability and consistency in preventing misdiagnoses in a case study. Literature Review Six aims for improvement in health care have been identified by the Institute of Medicine (Institute of Medicine, 2001); safety, effective, patient-centered, timely, efficient and equitable. These aims cut across misdiagnosis, which lays further emphasis on it as a critical quality issue in healthcare systems. There is no tolerance level in misdiagnosis, as it is a shared effort in world healthcare systems to get completely rid of misdiagnosis cases, thereby permanently setting the tolerance levels to zero. However, since it is estimated that on average, medial errors affect 1 in 10 patients worldwide (Wikipedia, 2015), a tolerance level of 1 in 10, which is of importance to note that is quite high, could be set. Misdiagnoses Cases Misdiagnosis stems from human error, which is inherent as the Institute of Medicine notes (Institute of Medicine, 1999). Norman Scarborough, 2008 classifies medical misdiagnoses in three categories viz., false positive which refers to the misdiagnosis of a disease that is not present, a false negative that is the failure to diagnose a disease that is present, and equivocal results that refer to inconclusive interpretation without a definite diagnosis. Research studies have revealed varying frequencies in misdiagnosis cases. According to Isabel Healthcare (2015), an analysis published in Advances in Patient Safety revealed that 10 to 30 percent of medical mishaps were due to misdiagnosis. Higher frequencies of up to 47 percent have been reported (Norman Scarborough, 2008). Misdiagnosis cases in the open category have posted even higher rates. In a study by Harvey et al. (1993), it was found that three-quarters of the most recent mammograms, which initially got interpreted as having normal findings, were later found actually to show cancer symptoms by at least one of three radiologist-reviewers. Impact of Medical Misdiagnosis “A misdiagnosis could cause a chain of events which affects, the individual patient, their families as well as the healthcare system and the society as a whole” (Norman Scarborough, 2008). From the statement, the impact of medical misdiagnosis can be categorized into the impact on quality of care, impact on patient anxiety and distress (Norman Scarborough, 2008) and finally the effect on the economy (Isabel Healthcare, 2015). The Department of Health and Human Services' annual report in 2002 estimated that on average $307,418 was paid out in every case to settle a diagnosis related malpractice in the U.S. that and 5,611 diagnostic-related medical malpractice payments were made (Isabel Healthcare, 2015). Implementing Quality Controls: Identifying and Prevention of Medical Misdiagnosis; A research study by Commonwealth Fund, revealed that top-performing hospitals were different from others in that; they developed the right culture for quality to flourish, attracted and retain the right people to promote quality, devised and updated the right in-house processes for quality improvement; and gave their staff the right tools to do the job (Sharon Silow-Carroll, 2007). Quality controls are therefore usually active in an organization, only when all arms and in this case when the administrative and clinical divisions of the hospital make an effort towards ensuring the goal. Misdiagnosed as a quality issue in hospitals can only be effectively managed by setting up controls aimed at identifying the misdiagnoses and hence their prevention. In as much as misdiagnoses have been cited as the leading cause of quality medical concerns and has indeed formed the bulk of malpractice lawsuits (35% of lawsuits in the US between 1986 and 2010, based on a John Hopkins University research), they are most easily preventable. Landro (2013) reports that hospitals and other healthcare providers are seeking to mitigate any possible occurrence of misdiagnosis by applying innovative techniques such as computer automation, new tests and devices, and changes to medical culture. Some of the techniques being employed by healthcare institutions include; mining EHR data to capture overlooked signs of trouble. The Southern California Permanente Medical Group (SCPMG) is one such institution that surveys its database, identifies any abnormal results and advice the patients to return for follow-up checks (Southern California Permanente Medical Group, 2015). Another technique proposed is the use of EHRs to double-check test results, to avoid flooding the physicians with information that they can already manage. Companies are also manufacturing devices that would adequately detect diseases, which would help alleviate the human errors and hence prevent misdiagnoses (Landro, 2013). Diagnosis reminder systems, for example, have provided proper alternative diagnoses by causing the physician to consider a primary diagnosis that he had not considered. This, the system has achieved by arming physicians with the most up-to-date and relevant clinical information (Isabel Healthcare, 2015). Still, as Landro (2013) reports, other efforts to reduce instances of misdiagnoses have been centered on patients and physicians, with the patients being encouraged to participate more in the diagnostic process, with medical officers also being taught to be more receptive to patient input. Pat Croskerry proposes that “if we can teach physicians how to think more critically, delivery of good care would be more efficient and accurate in terms of diagnosis” (Dalhousie Univeristy, 2012). Original Research To determine the frequency of misdiagnosis in a St. John’s Hospital, data was collected from the both hospital sources and stakeholders in the hospital. The research tools used in the collection of data included manual inspection of the medical records, administering questionnaires to both the hospital staff and patients, and also conducting interviews with the groups. Data from the hospital sources included; i) Hospital medical records: that were collected to identify the number of patients who reported back to the hospital within a period of 2 months with same medical condition (symptoms). ii) Staff member’s satisfaction survey results: to solicits employee’s opinion about their belief that they carried out the diagnosis procedure in an adequate manner and that they are satisfied with the results. Data from patients included; i) Patient follow-up survey results; to determine the patient’s state of health after diagnosis and treatment. A sample of 2248 patients, 5 physicians, 1 state official and 6 members of the public was used. The number of patients in the follow-up survey was a total of 198 from the 2248 patients. The average research period was 12 months. Analysis and Presentation of Results Demographic characteristics of respondents Table 1 below shows the demographic features of the respondents. Table 1: Demographic characteristics of respondents % Male Respondents 46% % Female Respondents 54% Age Range 12 - 65 Median age group 32 A slightly larger number of females were sampled (54%) to 46% males. The respondents’ ages ranged from 12 years to 65 years, with a median of 32 years old. Hospital Medical Records Table 2 below indicates the hospital medical records for the 12 month period: Table 2: Hospital Medical Records Period Total Patients Diagnosed Returning with same symptoms in a period of 2 months Diagnosed with different condition Diagnosed with same condition Nov 14 - Nov 15 2248 367 213 154 A total of 367 patients returned to the facility within a period of 2 months throughout the year with the same symptoms representing 16.3% of the total number of patients. Out of these, 213 were diagnosed with a different medical condition, with the 154 others being diagnosed with the same condition. This indicates that there were possible 213 cases of misdiagnosis in the period, a percentage of 9.5% as illustrated in chart 1 below: Chart 1: Proportion of Misdiagnoses Staff Members’ Satisfaction Surveys Table 3 below shows the staff member’s satisfaction survey results: Table 3: Staff members’ satisfaction results Satisfaction Rating Number of Respondents Bad 0 Fair 1 Good 1 Excellent 3 Total 5 3 out of 5 staff members believe they were presented with adequate resources and that they carried out the medical procedure in an excellent manner, with 1 staff member believing the procedure was fair and good for each. Patient follow-up survey results Table 4 below shows the patients’ satisfaction survey results: Table 4: Patients’ satisfaction results Health state No. of Respondents Worsening 7 No Improvement signs 23 Improving 100 Healed 68 Total 198 Most of the patients’ health conditions improved when a follow up survey was conducted as shown in table 4. 7 patients however had their health state worsening with 23 others experiencing no signs of improvement. Discussion SPC tools are used in the analysis of the data. Control charts showing the upper and lower control charts are drawn to determine the stability and consistency of the results. To draw out control charts for the survey data, the satisfactory ratings and health states are assigned weighted numerical values as indicated below: Satisfactory Rating Bad Fair Good Excellent Health State Worsening No improvement Improving Healed Weighting 1 2 3 4 Chart 2 below shows the staff satisfaction survey control chart at St. John’s Hospital. From the chart, all the data points are within the upper control limit and lower control limit, and therefore the misdiagnosis cases in the hospital, based on the staff satisfaction results can be stated as being in control. The patient health state control chart is shown in chart 3 below. As in the staff satisfaction survey, all the data is within the control limits. However, there are series of consequent data points that lie either above the mean control limits or below the mean control limits, which indicate that the process is unstable and therefore the misdiagnosis in St John’ s Hospital may be described as unstable, based on the patient health state survey. The hospital patient record control chart shown in chart 4 above indicate that the misdiagnoses in St. John’s Hospital is stable, as highlighted by the low skew from the mean control limit and is also in control since all the data points lie within the upper and lower control limits. Recommendations and Conclusions The research has identified cases of misdiagnoses in St. John’s Hospital, with a frequency of 9.5% of all the patient admissions during the period, based on the hospital patient records. This may be within the preset tolerance level of 1 in 10 which equates to 10%, but still high as stated. As revealed by the control charts, the quality issue in misdiagnosis can be stated as stable and in control. No particular trend can be deduced from the research that would be of help in predicting future cases of misdiagnosis. It is recommended therefore that St. John’s Hospital institutes quality controls, as discussed in the literature review of this research to help identify and hence prevent misdiagnoses in the hospital. References Read More