Making Healthcare Safer - a Critical Analysis of Patient Safety Practices - Assignment Example

An unintentional fall has been defined in many ways. In the Agency for Healthcare, Research and Quality (ARRQ) publication "Making Healthcare Safer: A Critical Analysis of Patient Safety Practices" (2001), Agnostini, Baker and Bogardus define a fall as "unintentionally coming to rest on the ground, floor, or other lower level, but not as a result of syncope or overwhelming external force" (p. 281) . This definition is based on Large, Gan, Basic & Jennings definition of a fall, in which a fall is described as an event which results in a person coming to rest unintentionally on the ground or lower level, not as a result of a major intrinsic event (such as a stroke) or overwhelming hazard (2006). The International Classification of Diseases 9 Clinical Modifications (ICD9-CM) attributes several codes to falls, all of which have broad descriptions: Fall on or from ladders or scaffolding (EB81); Fan from or out of building or other structure (E882); Other fall from one level to another (E884);Fall on same level from slipping, tripping, or stumbling (E885); Fallon same level from collision, pushing, or shoving by or with another person (E886); and Other and unspecified fall (E888). Inconsistencies in these definitions have made it difficult to conduct large-scale reviews because the outcome variables often have a different meaning depending on the definition used (Masud & Morris, 2001) The risk of falling may be associated with extrinsic and or intrinsic factors. Extrinsic factors include environmental hazards such as water on the floor, which might lead to slipping, or objects obstructing a clear walkway. Intrinsic factors include physiologic processes that may lead to a fall, including disease process, medication side effects, mental status or other unanticipated physiologic events such as a sudden onset of a disease process. Olsson, Wambold, Brock, Waugh & Sprague (2005) delineated a classification system that organizes patient falls into three categories: accidental falls, anticipated physiologic falls, and unanticipated physiologic falls. According to Olsson, Wambold, Brock, Waugh & Sprague, accidental falls are derived from extrinsic factors such as environmental factors; anticipated physiologic falls derive from known intrinsic physiologic factors such as confusion; and unanticipated physiologic falls derive from unexpected intrinsic events such as a new onset syncopal event or major intrinsic event such as a stroke. According to this classification approximately 78% of the anticipated physiologic falls can be identified, and thus prevented. In the clinical environment basic safety interventions seek to decrease extrinsic factors leading to falls. Despite the textbook differentiation between extrinsic and intrinsic precursors to falling, in the healthcare environment it is assumed that maintenance of patient safety is the ultimate responsibility of the healthcare provider; thus it is assumed that all falls are adverse events. Methods to distinguish between intrinsic and extrinsic origins, such as root cause analysis - will help to improve accuracy of reporting and may provide a more clear definition of falls, but are not the focus of this study. Patient Falls - Fall Risk Factors Factors associated with patients at risk of falling in the acute care setting have been explored, extensively, particularly over the past two decades. These factors include the following: history of falling; age; use of benzodiazepines or other sedative-hypnotics; impaired cognition as in dementia; impaired communication as in a language barrier; impaired mobility; impaired sensory ability as in patients who have suffered a stroke; increased toileting needs; secondary diagnosis; use of me meal equipment and depression (Olsson, Wambold, Brock, Waugh & Sprague, 2005). Age has been cited as a factor for both actual risk of falling and the severity of injury from the fall (Barrett-Connor, Grady, & Stefanick, 2005); however recent work by Hendrich did not support the association between increasing age (after the age of 65) and increasing risk of falling in the inpatient environment. Instead, Hendrich et at found that confusion was the most important risk factor associated with the risk of falling (Hendrich et a1., 2003). Nevertheless, age must be considered when discussing injury associated with falls because frequently with age often comes frailty. Harwood reviewed literature related to visual problems and falls. This research demonstrated that uncorrected visual impairment nearly doubled the risk of falling (Harwood, 2001). Cardiovascular causes of falls derived predominantly from neutrally mediated disorders (e.g. vasovagal syncope) and cardiac abnormalities (e.g. arrhythmias, infarction, valvular stenosis) (Carey &Potter, 200l). A few research studies have reported that the risk of falling increases exponentially with an increased number of risk factors (Rubenstein et al, 2001). Increasing evidence is demonstrating an association between an increased risk of falling with lower nurse-patient staffing ratios. (Dunton et al., 2004; Hitcho et al., 2004; Whitman et al., 2002). Time of day has also been implicated. Hitcho et al., identified a higher rate of falls on the night shift, but this is inconsistent with .other research and may in fact be explained by staffing patterns (2004). Patient Falls - Injury Risk Factors Factors associated with increased risk of injury include physiological processes such as increased bleeding tendencies and increased risk for fracture should a fall occur (Barrett-Connor, Grady & Stefanick, 2005). Factors that predispose an individual to increased bleeding tendencies include medications that increase bleeding time, such as thrombolytics and anti-platelet therapies, as well as physiological processes such as thrombocytopenia which may result from any number of causes. Factors that predispose an individual to increased risk for fracture includes female gender lower than normal body mass index and osteoporosis (Shabat, Gepstein, Mann, Stern & Nyska, 2003). Although much work has been carried out in the attempt to identify to predict inpatient falls, none of the current tools capture the risk that is associated with patients who are vulnerable to an injury subsequent to a fall. Patient Falls - Effects of Falls and Injuries Recurrent falls have been identified to increase the length of stay (LOS) in elderly psychiatric patients and subsequent general decompensation of physical status (Greene et al., 2001). Fall-related injuries increase resource utilization with these patients, incurring approximately 60% higher total charges than those who did not fall (Bates, 2002). Falls have also been associated with leading to a poorer quality of life because of 'fallophobia' a fear of future falls (Parry et at, 2001). Fall-related injuries increase resource utilization since injuries from falls lead to increased length of stay and an increased chance of unplanned readmission or of discharge to a residential or nursing home care (Frels et al., 2002.). Furthermore, inpatients who have incurred an injury due to a fall have approximately 60% higher total charges than those who did not fall or those who fell and did not sustain an injury (Bates, 2002). Patient/Falls - Risk Assessment Instruments Many tools have been developed to identify patients at highest risk for falling (Coker & Oliver, 2003; Hendrich et al., 2003; Oliver, Daly, Martin, & McMurdo, 2004), but most of these tools lack the sensitivity and specificity required to adequately distinguish patients who are likely to fall from those who are unlikely to fall in the acute care setting. The predominant tool that is used by nurses is the Morse Falls Risk Assessment Tool. In 2002, O'Connell and Myers conducted psychometric testing wiith this tool on a group of 1059 patients admitted to an Australian hospital. In this study, the Morse Falls Risk tool had a sensitivity of 83% and a specificity of 29%, but a positive predictive value of only 18%. This resulted in a very high false positive rate, with the tool identifying over 70% of patients who did not fall at high risk for falling (O'Connell & Myers, 2002). The Stratify Falls Prediction tool also had a low positive predictive value (30%) and relatively low sensitivity (66%) and specificity (47%) (Coker & Oliver, 2003). The Heinrich Falls Risk Model I (HFRM-I) had better sensitivity (77%) and specificity (72%) than either of the others, and the Hendrich Falls Risk Model II (HFRM-II) demonstrated even more improvement (sensitivity 74.9%; specificity 73.9%; and positive predictive value 75%) (Hendrich et al., 2003). The inclusion of a 'get up and go' test to the HFRM-II tool was the major change between versions I and version ll. The 'get up and go' test evaluates a person's ability to rise from a chair in a single movement, which is an assessment method that has been explored in earlier falls prediction research. It is surprising that the sensitivity and specificity of the tool only increases slightly with the addition of this factor, underscoring the complexity of predicting patient falls. Several studies have tested the predictive validity of fall risk assessment instruments in relation to the judgment of nurses. Myers and Nikoletti (2003) concluded that neither the fall risk assessment instrument nor nurses' clinical judgment acted as a reliable predictor (Myers & Nikoletti, 2003). Eagle et a1. (Eagle et al., 1999) compared the Functional Reach test, the Morse Falls Scale and nurses' clinical judgment in the rehabilitation and geriatric environment. This study also concluded that the two standardized assessment processes were no better at predicting fans than the clinical judgment of nurses. A limitation to both of these studies was that the evaluation occurred only at one time pointc1ose to admission, which docs not account for the variability of patient status throughout a patient's hospital stay. In the domain of rehabilitation medicine Ruchinskas compared structured assessments including Mini-Mental State Exam, the Geriatric Depression Scale, the Functional Intervention Model and the clinical judgment of physical and occupational therapists on admission and discharge(2003). This study concluded that the clinical judgment of therapists had a positive predictive power of 33% and a negative predictive power of 82%. However, the more accurate predictors of falling for the patients in their sample were a history of falls and presence of a neurological diagnosis. In the residential care environment, Lundin-Olson et al. (2003) found that clinical judgment can contribute to the accurate prediction of falls risk, but is not sufficient on its own as a valid predictor. Clinical judgment research in the field of psychology has compared the abilities of mechanical prediction and clinical judgment. Grove et al. conducted a meta-analysis of mechanical prediction versus clinical judgment. The analysis demonstrated that, in general, mechanical prediction was l0% more accurate than clinical judgment (Grove, Zald, Lebow, Snitz,& Nelson, 2000). Myers recently has conducted a literature critique of all published fan risk assessment instruments (Myers, 2003). This review reported that even the most widely used fall risk assessment instruments have inconsistent sensitivity and specificity when used outside of the original setting, thus highlighting the need for deeper investigation. Patient Falls - Electronic Assessment Instruments Lord, Menz & Tiedemann (2003) describes an electronic fall risk assessment instrument that provides a method to measure several risk factors, including vision, peripheral sensation, muscle force, reaction time and postural sway. Although this instrument is thorough, it is meant for use by a physical therapist or a physician, nurse practitioner or physician's assistant (MD/NP/PA) for a focused fall risk assessment rather than as a triage or screening tool. The novel aspect of this instrument is the comparison of the individual's score to the nonnative scores for each of the assessments (2003). This model may be of interest to physical therapists, but has limited use in the acute care setting. Another electronic fall risk assessment instrument, described by Dyer, is an electronic 'checklist' in a fall prevention clinic. Unfortunately the researchers concluded that the 'clinic itself' was more successful than the instrument in identifying risk factors for falling, underscoring the reality that the implementation of an instrument without associated policy and procedure changes may have limited effect (Dyer et at, 1998). Recent work by a large Australian consortium, Fall Risk Assessment and Management System FRAMS, has developed a detailed informatics infrastructure for their community-based fall prevention program. In this promising and relevant work, the focus of care is on the community and the risk assessment includes screening for individual risk items followed by a thorough evaluation by a general practice physician. However, predictive validity of the instrument or instruments remains unclear. Additionally, it is unclear whether or not the PRAMS risk assessment process would easily transfer to the acute care setting. (Liaw, Sulaiman, Pearce, Sims, Hill, Grain, Tse et al, 2003) Patient Safety Efforts Currently the notion of patient safety is one that has been attacked from different fronts and utilizing various methodologies and efforts. Concurrent to these efforts, important research in nursing is focusing on the effects of nursing staffing as it relates to patient mortality and job satisfaction (Aiken, Clarke, Sloane, Sochalski & Silber, 2002). Additionally, Clarke and. Aiken(2003) look at the implications of 'failure to rescue,' which they define as being "based on the premise that although deaths in hospitals are sometimes ml avoidable, many may be prevented" (Clarke & Aiken, 2003). This supports the inclusion of unanticipated physiologic events' into the definition of falls since the patient's safety issues should be addressed at all times. These recent efforts highlight the importance for addressing the safety needs of hospitalized patients. Other Related Studies An associated study testing Technology Acceptance Model version 2 (TAM2) examined Pediatric physicians' use of the Internet as it relates to perceived usefulness and perceived ease of use. This study concluded that that perceived usefulness was the most important factor associated with adoption of this technology (Chismar & Wiley-Patton, 2002). In summary, there is a vast literature related to falls; however, much of the research in this area has been carried out in the community setting and with a focus on the elderly population. Research has demonstrated that multiple factors are related to fall risk and that risk increases as factors increase. Fall related injuries have a serious effect on those who fall, subsequently increasing the cost of care. Current instruments are inconsistent across care areas and do not include injury risk as a factor or specify tailored interventions. Informatics processes towards fall prevention have not been well-researched. Effective screening in the community setting will likely provide the best prevention; however, comprehensive community screening is a daunting task. Furthermore, patients who are at risk for falling in the hospital environment tend to have a different profile, and thus different needs, than those in the community. References Agostini, J. V., Baker, D.l, & Bogardus, S.T. (2001). Prevention of falls in hospitalized and institutionalized older people (Evidence Report/Technology Assessment No. 43); Agency for Healthcare Research and Quality. Aiken, L R., Clarke, S.P., Sloane, D. M., Sochalski, J., & Silber, J. H. (2002). Hospital nurse staffing and patient mortality, nurse burnout, and job dissatisfaction. Journal of the American Medical Association; 288(16), 1987-1993. Barrett-Connor, E., Grady, D. & Stefanick, M.L. (2005). The rise and fall of menopausal hormone therapy. Annual Review Of Public Health, 26, 115-40. Bates, D. W. (2002). The quality Case for information technology in healthcare BioMed Central BMC Medical Informatics and Decision Making, 2(7). Carey, B. J. & Potter; J. F. (2001); Cardiovascular causes of falls. Age and ageing, 30(84), 19-24. Clarke, S. P. & Aiken, L H. (2003). Failure to rescue: Needless deaths are prime examples of the need for more nurses at the bedside. American Journal of Nursing, 103(1), 42-47. Coker, E., & Oliver, D. (2003). Evaluation of the STRATlFY Falls Prediction Tool on a Geriatric Unit. Outcomes Management, 7(1), 8-14. Dunton, N., Gajewski, B., Taunton, R.L. & Moore, J. (2004). Nurse staffing and patient falls on acute care hospital units. Nursing Outlook, 52(1). Dyer, C., Watkins, C., Gould, C., & Rowe, J. (1998), Risk-factor assessment for falls: from Written check list to the penless clinic. Age Ageing, 27(5), 569-572. Eagle, D.J., Sa1am S., Whitman, D., Evans, L. A., Ho., E., & Olde, J. (1999). Comparison of three instruments in predicting accidental falls in selected inpatients in a general teaching hospital. Journal of Gerontological Nursing, 25(7), 40-45. Frels, C., Williams, P., Narayanan, S., & Gariballa, S.E. (2002). Iatrogenic causes of falls in hospitalised elderly patients: a case-control study. Postgrad Med J, 78(922), 487-489. Greene, E., Cunningham, C. J., Eustace, A., Kidd, N., Clare, A. W., & Lawlor. RA. (2001). Recurrent falls are associated witb increased length of stay in elderly psychiatric inpatients. International Journal of Geriatric Psychiatry, 16, 965-968. Grove, W. M., Zald, D, H., Lebow, B.S., Snitz, RE., & Nelson, C. (2000). Clinical versus mechanical prediction: A meta-analysis. Psychological Assessment, 12(1), 19-30. Harwood, R. H. (2001). Visual problems and fans. Age and Ageing. 30-S4(S4), 13-18. Hendrich, A., Bender, P. S., & Nyhuis, A. (2003). Validation of the Hendrich II fall risk model: a large concurrent case/control study of hospitalized patients. Applied Nursing Research, 16(1), 9-21. Hitcho, E. B., Krauss, M.J., Birge, S., Claibome Dunagan, W., Fischer, I., Johnson, S., et a1. (2004). Characteristics and Circumstances of Falls in a Hospital Setting. A Prospective Analysis. J Gen intern Meet 19(7), 732-739. Large, J., Gan, N., Basic, D. & Jennings, N. (2006). Using the timed up and go test to stratify elderly inpatients at risk of falls. Clinical Rehabilitation, 20 (5), 421-8. Liaw, S.T., Sulaiman, N., Pearce, C., Sims, J., Hill, K., Grain, H., et a1..(2003). Falls Prevention within the Australian General Practice Data Model: Methodology, Information Model, and terminology Issues. J Am Med Inform Assoc, 10(5), 425-432. Lord, S. R, Menz, H. B.; & Tiedernann, A.(2003) A Physiological Profile Approach to Falls Risk Assessment and Prevention, Journal of the American Physical Therapy Association. 83(3), 236-252. Masud, T., & Morris,R. O. (2001). Epidemiology of falls. Age Ageing, 30(90004), 3-7. Myers, R., & Nikoletti, S. (2003). Falls Risk Assessment: A Prospective Investigation of Nurses' Clinical Judgment and Risk Assessment Tools in Predicting Patient Fans. International Journal of Nursing Practice, 9(3), 158-165. O'Connell, B., & Myers, R. (2002). The sensitivity and specificity of the Morse Fall Scale in an acute care setting. Journal of Clinical Nursing, 11(1), 134-136. Oliver, D., Daly, F., Martin, F. C., & McMurdo, M. E. (2004). Risk factors and risk assessment tools for falls in hospital in-patients: asystematic review. Age Ageing, 33(2), 122-130. Olsson, R.H., Wambold, S., Brock, B., Waugh, D. & Sprague, H. (2005). Visual spatial abilities and fall risk: an assessment tool for individuals with dementia. Journal Of Gerontological Nursing, 31 (9), 45-51. Parry, S.W., Steen, N., Ganoway, S.R., Kenny, R.A., & Bond, J. (2001). Falls and confidence related quality of life outcome measures in an older British cohort. Postgraduate Medical Journal, 77, 103-108. Rubenstein, L.K, Powers, C.M., & MaCLean, C.H. (200 1). Quality indicators for the management and prevention of falls and mobility problems in vulnerable elders. Annals o f Internal Medicine, 135(8,2), 686-693. Ruchinskas, R.P. (2003). Clinical Prediction of Falls in the Elderly. American Journal of Physical Medicine & Rehabilitation. 82(4), 273-278. Shabat, S., Gepstein, R., Mann, G., Stern, A. & Nyska, M. (2003). Simultaneous distal radius and hip fractures in elderly patients--implications to rehabilitation. Disability and Rehabilitation 25 (15), 823-6. Whitman, G.R., Kim, Y., Davidson, L.J., Wolf, G.A. & Wang, S.L. (2002). The impact of staffing on patient outcomes across specialty units. Journal of Nursing Administration, 32(12), 633-639. Read More