>> <
>> The prospective payment system in the United States, in<
>> which healthcare costs are paid prospectively, based on a<
>> standard sum for well defined medical conditions (the<
>> diagnosis-related group, DRG) has created a golden<
>> opportunity to maximise profits without extra work. When<
>> classifying your patient's illness, always "upcode" into<
>> the highest treatment category possible. For example,<
>> never dismiss a greenstick fracture as a simple<
>> fracture-inspect the x ray for tiny shards of bone. That<
>> way you can upgrade your patient's
eak from a simple to<
>> a compound fracture and claim more money from the<
>> insurance company. "DRG creep" is a well recognised means<
>> of boosting hospital income by obtaining more<
>> reimbursement than would otherwise be due.13<
>> <
>> Another reason for upcoding your patients' illnesses is to<
>> manipulate reimbursement rules for your patients' benefit.<
>> A recent national survey of US doctors showed 39% had used<
>> such tactics-including exaggerating symptoms, changing<
>> billing diagnoses, or reporting signs or symptoms that<
>> patients did not have-to secure additional services felt<
>> to be clinically necessary.14 Medical fraud is estimated<
>> to account for 10% of total US spending on health care<
>> (some $120bn) in 2001.15<
>> <
>> Reducing mortality figures-gaming and clinical performance<
>> data<
>> Many clinicians worry about public release of clinical<
>> performance data, as above average mortality figures can<
>> unfairly damage your reputation. In reality half of all<
>> hospitals have above average (technically, above median)<
>> mortality, and various gaming strategies can help to<
>> disguise less than perfect clinical performance.<
>> <
>> Upcoding of morbidities<
>> "Coding creep" refers to the excessive or inappropriate<
>> coding of those risk factors that are required for<
>> calculating risk adjusted mortality. A slight decline in<
>> observed mortality from coronary artery bypass graft<
>> surgery in New York in the early 1990s was accompanied by<
>> an unexpected rise in the (calculated) expected mortality.<
>> However, 66% of the increase in predicted mortality was<
>> attributed to an increase in the severity of recorded risk<
>> factors.16 Between 1989 and 1991, the proportion of<
>> patients recorded preoperatively as having chronic<
>> obstructive pulmonary disease increased from 6.9% to 17.4%<
>> (at one hospital this increased from 1.8% to 51.9%). If a<
>> major risk factor is recorded in a higher proportion of<
>> patients before surgery the unit's predicted mortality<
>> will increase, as will the likelihood that the unit's<
>> actual mortality falls within or below the expected range.<
>> <
>> Clearly smokers have an increased risk of dying during<
>> surgery, so any patients who deny smoking when their<
>> history is taken should be questioned further. Perhaps<
>> they stopped recently, they might enjoy a cigarette on<
>> social occasions, or they may share a house or workplace<
>> with a smoker-in which case record them as being a smoker.<
>> Similarly, even a faint wheeze in any patient over 40<
>> years old who has ever been exposed to cigarette smoke<
>> could signify early chronic obstructive pulmonary disease,<
>> and patients with this condition have a higher risk of<
>> dying. By placing as many patients as possible in a high<
>> risk category, your figures for risk adjusted mortality<
>> will be reduced.<
>> <
>> Selection of risk adjustment procedure<
>> When calculating risk adjusted mortality, you can enter a<
>> bewildering number of risk factors into multivariate<
>> equations, and many proprietary risk adjustment formulas<
>> are available. Rankings of individual hospitals vary<
>> widely depending on how you adjust for disease severity,<
>> and in principle your hospital could "shop around" for<
>> whichever adjustment measure shows it in the best possible<
>> light.17<
>> <
>> Transfer of patients<
>> The first person to produce a "league<
>> table" of hospital mortality was<
>> Florence Nightingale. Her attempts to<
>> compare mortality between different<
>> hospitals were widely criticised, not<
>> least because she accused certain<
>> hospitals of discharging hopelessly ill<
>> patients back home, and she conceded<
>> that accurate statistics were difficult<
>> to obtain: "Accurate hospital<
>> statistics are much more rare than is<
>> generally imagined, and at the best<
>> they only give the mortality which has<
>> taken place in the hospital, and take<
>> no cognizance of those cases which are<
>> discharged in a hopeless condition, to<
>> die immediately afterwards, a practice<
>> which is followed to a much greater<
>> extent by some hospitals than by others."18<
>> <
>> Many hospital databases record only those deaths that<
>> occur in the hospital of operation, so deaths in<
>> continuing care facilities may be overlooked when<
>> calculating mortality. Conversely, if your hospital seems<
>> to have a particularly high mortality perhaps it is<
>> admitting more terminally ill patients. Consider opening<
>> an off-site hospice in order to discharge the sickest<
>> patients to die there.19<
>> <
>> Change of operative class<
>> The only major cardiac surgical procedure for which<
>> mortality data have been publicly reported in the United<
>> States is coronary artery bypass grafting (CABG). When<
>> confronted with a high risk patient, or if things start<
>> going wrong during an operation, just convert the<
>> procedure to an unreported operation.20 Simply adding a<
>> few extra stitches can convert a conventional CABG to a<
>> CABG plus mitral valve repair. The apparent mortality in<
>> your CABG series falls, albeit at the expense of more<
>> deaths from the (unreported) combined procedure.<
>> <
>> You could even invent an entirely new condition by means<
>> of computer enhanced images and allocate your highest risk<
>> patients to that category (so called pixel-byte<
>> syndrome21). This could be of particular interest to<
>> doctors who are approaching retirement but who have not<
>> yet been credited with an eponymous syndrome.<
>> <
>> Refusing to operate<
>> Despite reassurances that risk adjustment techniques do<
>> not penalise surgeons who operate on high risk patients,<
>> an anonymous survey of all cardiac surgeons in New York<
>> state found that 62% had refused to operate on at least<
>> one high risk CABG patient, mainly because of fear of<
>> public reporting.22<
>> <
>> Cream skimming<
>> It is in the interests of health insurance plans to<
>> recruit only the most profitable patients ("cream<
>> skimming").23 One US health insurance company recruited<
>> members at a dinner dance, realising that elderly people<
>> who are fit enough to dance are healthy. Clinicians<
>> benefit too from pruning high risk patients from their<
>> lists: for example, doctors who are high outliers can<
>> dramatically improve their profile simply by removing<
>> their three patients with the highest haemoglobin A1c<
>> levels.24<
>> <
>> Reporting risks<
>> Always report absolute rather than relative risks.25 26 If<
>> your hospital's mortality figure is 6% and the average<
>> rate is 4%, you should point out that the absolute death<
>> rate is only 2% higher than average. If people insist on<
>>
reporting your unit as having a 50% higher mortality than<
>> average, you can retort that the average is actually only<
>> 33% lower.<
>> <
>> Discussion<
>> <
>> One feature is common to all examples hitherto<
>> discussed-the individuals or institutions that used these<
>> techniques were discovered. Further research is needed to<
>> uncover the truly compelling examples of creative<
>> accounting. Future dishonest researchers, incompetent<
>> surgeons, and corrupt managers will have to devise more<
>> devious ways to avoid falling foul of the 11th<
>> commandment, "Thou shalt not get caught."<
>> <
>> On a serious note, however, despite claims of widespread<
>> gaming and manipulation, there are comparatively few<
>> documented examples. This review highlights some dilemmas<
>> faced by those under pressure to ensure that healthcare<
>> providers conform to performance targets. These include<
>> competing targets, in which achieving success in one area<
>> comes at the expense of failing another. We also<
>> demonstrate the consequences of gaming, especially in<
>> sensitive targets such as mortality figures-and where<
>> gaming exists, the entire credibility of targets is<
>> undermined.<
>> <
>> Summary points<
>> <
>> Performance managed healthcare settings encourage<
>> gaming and "creative accounting" of data<
>> <
>> Creative accounting is driven by three dominant<
>> factors-attracting additional resources, meeting<
>> performance related targets, and improving position<
>> in league tables<
>> <
>> Additional resources may be obtained through<
>> fraudulent claims, inducements, self referrals, and<
>> "DRG creep"<
>> <
>> The non-clinical performance targets that lend<
>> themselves most readily to creative accounting are<
>> hospital waiting times<
>> <
>> Position in clinical league tables may be enhanced by<
>> "coding creep," choice of risk adjustment method,<
>> transfer of patients, change of operating class,<
>> denial of treatment, and "cream skimming" of<
>> healthier patients<
>> <
>> ------------------<
>> We are profoundly grateful to those anonymous health<
>> professionals whose anecdotes were unwittingly provided<
>> while under the influence of varying quantities of<
>> ethanol.We cannot be held responsible for any consequences<
>> that may result from attempting to use any of the<
>> techniques discussed in this review.<
>> <
>> Funding: If only.<
>> <
>> Competing interests: The need to enhance the publications<
>> section of our curricula vitae.<
>> <
>> References<
>> <
>> 1. Farthing M, Lock S, Wells F. Fraud and misconduct in<
>> biomedical research. 3rd ed. London: BMJ Books, 2001.<
>> 2. United Kingdom Parliament. House of Commons Select<
>> Committee on Public Administration. 30 January 2003:<
>> 942.<
>><
>www.parliament.the-stationery-office.co.uk/pa/cm200203/cmselect/cmpubadm<
>/uc62-ix/uc6202.htm<
>> (accessed 15 Aug 2003).<
>> 3. House of Commons Committee of Public Accounts.<
>> Inappropriate adjustments to NHS waiting lists.<
>> Forty-sixth report of session 2001-2002. London:<
>> Stationery Office, 2002.<
>><
>www.publications.parliament.uk/pa/cm200102/cmselect/cmpubacc/517/517.pdf<
>> (accessed 10 Dec 2003).<
>> 4. BBC News. NHS managers `fiddle figures.' 7 October<
>> 2002. news.bbc.co.uk/1/hi/health/2299291.stm<
>> (accessed 15 Aug 2003).<
>> 5. BBC News. Transcript of BBC1 programme Panorama:<
>> Fiddling the figures. 29 June 2003.<
>><
>news.bbc.co.uk/nol/shared...anscripts/<
>fiddlingthefigures.txt<
>> (accessed 15 Aug 2003).<
>> 6. Auditor General, Audit Scotland. Review of the<
>> management of waiting lists in Scotland. Edinburgh:<
>> Auditor General, 2002.<
>>
www.audit-scotland.gov.uk/publications/pdf/2002/02pf03ag.pdf<
>> (accessed 10 Dec 2003).<
>> 7. Revill J. Hospitals faking cuts in casualty wait<
>> times-operations axed to rig targets, documents<
>> reveal. Observer, 11 May 2003.<
>> observer.guardian.co.uk/n...95,00.html<
>> (accessed 15 Aug 2003).<
>> 8. BMA. BMA survey of A&E waiting times. May 2003.<
>>
www.bma.org.uk/ap.nsf/Content/AEsurvey/$file/AEsurvey.pdf<
>> (accessed 15 Aug 2003).<
>> 9. Gulland A. NHS staff cheat to hit government targets,<
>> MPs say [News]. BMJ 2003;327: 179.[Free Full Text]<
>> 10. Mehigan BJ, Monson JRT, Hartley JE. Stapling<
>> procedure for haemorrhoids versus Milligan-Morgan<
>> haemorrhoidectomy: randomised controlled trial.<
>> Lancet 2000;355: 782-5.[CrossRef][ISI][Medline]<
>> 11. Helmy MA. Stapling procedure for hemorrhoids versus<
>> conventional haemorrhoidectomy. J Egypt Soc Parasitol<
>> 2000;30: 951-8.[Medline]<
>> 12. Kalb PE. Health care fraud and abuse. JAMA 1999;282:<
>> 1183-8.<
>> 13. Simbourg DW. DRG creep: a new hospital-acquired<
>> disease. N Engl J Med 1981;304: 1602-4.[ISI][Medline]<
>> 14. Wynia MK, Cummins DS, VanGeest JB, Wilson IB.<
>> Physician manipulation of reimbursement rules for<
>> patients: between a rock and a hard place. JAMA<
>> 2000;283: 1858-65.[Abstract/Free Full Text]<
>> 15. Hyman DA. Health care fraud and abuse: market change,<
>> social norms, and the trust "reposed on the workmen."<
>> J Legal Studies 2001;30: 531-67.[CrossRef][ISI]<
>> 16. Green J, Wintfeld N. Report cards on cardiac<
>> surgeons: assessing New York state's approach. N Engl<
>> J Med 1995;332: 1229-32.[Free Full Text]<
>> 17. Iezzoni LI. The risks of risk adjustment. JAMA<
>> 1997;278: 1600-7.[Abstract]<
>> 18. Nightingale F. Notes on hospitals. 3rd ed. London:<
>> Longman, 1863.<
>> 19. BBC News. Stoke and Staffordshire local news.<
>> Hospital blames `lack of hospice care.' 15 October<
>> 2002.
www.bbc.co.uk/stoke/news/2002/10/121002.shtml<
>> (accessed 15 Aug 2003).<
>> 20. Jones RH. In search of the optimal surgical<
>> mortality. Circulation 1989;79(6 Pt 2):<
>> I132-6.[Medline]<
>> 21. Cutrone M, Grimalt R. The true and the false:<
>> pixel-byte syndrome. Pediatr Dermatol 2001;18:<
>> 523-6.[CrossRef][ISI][Medline]<
>> 22. Burack JH, Impellizzeri P, Homel P, Cunningham JN Jr.<
>> Public reporting of surgical mortality: a survey of<
>> New York State cardiothoracic surgeons. Ann Thorac<
>> Surg 1999;68; 1195-200.[Abstract/Free Full Text]<
>> 23. World Bank Institute. Flagship program on health<
>> sector reform and sustainable financing. Glossary to<
>> distance learning module 1-Basics of health<
>> economics.<
>>
www.worldbank.org/wbi/healthflagship/dl_glossary.html<
>> (accessed 15 Aug 2003).<
>> 24. Hofer TP, Hayward RA, Greenfield S, Wagner EH, Kaplan<
>> SH, Manning WG. The unreliability of individual<
>> physician "report cards" for assessing the costs and<
>> quality of care of a chronic disease. JAMA 1999;281:<
>> 2098-105.[Abstract/Free Full Text]<
>> 25. Bucher HC, Weinbacher M, Gyr K. Influence of method<
>> of reporting study results on decision of physicians<
>> to prescribe drugs to lower cholesterol<
>> concentration. BMJ 1994;309:<
>> 761-4.[Abstract/Free Full Text]<
>> 26. Fahey T, Griffiths S, Peters TJ. Evidence based<
>> purchasing: understanding results of clinical trials<
>> and systematic reviews. BMJ 1995;311:<
>> 1056-9.[Abstract/Free Full Text]<
>> <
>> Rapid Responses:<
>> <
>> Read all Rapid Responses<
>> <
>> Intrigued by ref 10 & 11<
>> Pierre-Yves Boelle<
>> bmj.com,
19 Dec 2003 [Full text]<
>> <
>> ------------------------------------------------------------<
>> Home Help Search/Archive Feedback Table of Contents<
>> [BMJ] [The general medical journal website.]<
>> C 2003 BMJ Publishing Group Ltd<
