Falls risk-prediction tools for hospital inpatients. Time to put them to bed?

Accidental falls are the commonest safety incidents affecting hospital inpatients and care-home residents 1,2.The recent National Patient Safety Agency (NPSA) report, 'Slips Trips and Falls in Hospital' 2, identified over 200,000 reported falls incidents from acute, community and mental health trusts in England and Wales from 2004/2005 alone, (some 32% of all incidents in all age groups) though we know from this and other sources that such incidents are underreported 3.Such falls are associated with a range of adverse outcomes including injury, impaired confidence and function, increased length of stay, institutionalisation anxiety and guilt for staff and relatives, complaint and litigation.They should, therefore, be a major risk management priority for hospitals and care homes (where around 50% of residents fall at least once a year) 4, and have recently been made a main focus for examining older patients' care by the Healthcare Commission 5.There is a growing body of evidence on interventions to prevent falls and falls-related injuries in hospital 1,6,7.One component of many research interventions, and a common feature of 'real-life' falls policies in hospitals, is the use of falls risk-prediction tools.By this, I do not mean 'checklists' of common risk factors to prompt specific action by staff, which might, in turn, reduce falls.Such factors might include environmental and equipment safety, medication, hypotension, visual impairment, muscle weakness or postural instability, cognitive impairment, restlessness or agitation, all of which, amongst others, have been targeted in successful falls intervention programmes 8.I have no argument with the use of such tools, which, in effect, prompt good comprehensive geriatric assessment and care-planning.My concern is over scoring tools, which purport simplistically to classify patients as having a 'high' or 'low' risk of falling so that interventions can be targeted to 'high-risk' patients.This approach can work well in other fields, for instance, with diagnostic screening tests such as troponin for acute coronary syndrome, or d-dimer for suspected pulmonary embolism (both of which are good 'true negative' tests with high specificity).It is also possible for clinical prediction tools with continuous scoring to be used to calculate overall