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patient registry or databank for common hernias makes complete sense. This chapter will deal with the “why-a-database?” question and shows that the rationale for their existence rests on four factors: they must (1) pursue a clearly-dened goal (what purpose do they serve?); (2) be fruit­ful in answering the relevant medical questions (what type of information and how much will be obtained?); (3) collect reliable and complete data (can the data be trusted?); and lastly, but most importantly, (4) gather data that further improves treatment of the patient. If this last factor is miss­ing, the keeping of a hernia registry would be a waste of time.

Much remains to be learned regarding hernia treatment. Randomized clinical trials and cohort studies have been criticized and need to be dem­onstrated that they can produce meaningful, reli­able, and useful results. In the eld of hernia surgery, randomized clinical studies have been shown to only rarely generate results of practical value, due mainly to the wide variability in the relevant factors. Moreover, randomized clinical studies can do little to help standardize the wide variability in medical products for treating hernia patients. In recent years, evaluation of prospec­tive registries has gained in importance. For the rst time, impressive data on results outside study conditions are available, results that allow conclusions regarding epidemiological and prog­nostic information. The Swedish and Danish Hernia Registries, in particular, but also EuraHS, the registry of the European Hernia Society, can

be named as examples. In addition to the “why-a­database?” question, this chapter will examine the limits and dangers of evaluating databanks and discuss practical measures for their management.

ity, and is not in itself an existing actuality. Wolfgang Lienemann (University of Berne, Switzerland) denes quality as “… a form of judgement by means of which something is attributed [to something], it is not anything that belongs to things by their nature.” Since quality is not something in itself, it cannot be quanti­ed; a key gure can be measured. One can also agree to dene the attainment of a certain key gure as a quality. But one must always be aware that dening a given quality based on key gures is arbitrary and never to be interpreted as representative of “truth.” So, quality assurance involves in the rst instance an overview of key gures, which the inexplicable practice of medi­cine renders “explicable” by making the subjec­tive “objective.” Typical key gures in a medical database are, for example, the number of ambu­lant interventions, mesh implantations yes/no, OP time (elapsed time from incision to suture), perioperative antibiotic treatment yes/no, com­plications at discharge, or duration of hospital­ization. Such key gures dened as quality indicators do not say for individual patients whether guidelines have been followed, whether ambulant follow-up was necessary, how long the patient was unt for work, and also not what the 30-day morbidity was, to name just a few examples. The renowned economist Mathias Binswanger (St. Gallen, Switzerland) sees three basic problems with today’s “quality assurance culture”:(1) it creates a data cemetery, bureau­cracy grows, exibility is lost, and mere num­bers generate interest at the expense of long term strategies. In this regard, the renowned social critic Simon Sinek has coined the phrase “innite vs. nite perspectives,” where the attainment of pre-established goals corresponds to a “nite” perspective. (2) This problem is eas­ily derived from the rst: “nite” goals open the oodgates to lining one’s own pockets, because key gures can be arbitrarily dened to “arti­cially improve” performance (resulting, e.g., in large bonus payments to managers and CEOs), which paradoxically can lead to purportedly “positive” key gures that are ultimately dam­aging to a hospital or private clinic. The third problem with quality assurance based on “arti-

cial” key gures according to Binswanger is that (3) they function as conceptual blinders that limit the ability to make real-world judgements. To quote Binswanger, “Actions based on key gures do not make an enterprise successful, only the exible managing of problems confers a competitive advantage” [2]. The concept of quality assurance must be abandoned in favor of a transparency of results that is value-neutral and opened to continuous questioning and ever new analyses.

the codication of treatment procedures—with the goal of minimizing the amount patients are billed—are a powerful bias and produce a com­pletely distorted picture with regard to individual patients. It is not surprising, then, that there is little agreement between medical data from “medical registries” and from “billing regis­tries,” with a kappa value of <0.4, about equal to chance (compare ACSNSQIP and Medicare data on 110,000 patients) [4]. In the same category can be included databanks for certication or specialty accreditation (e.g., the Center of Excellence or Surgeon of Excellence): upon closer examination, they are of no use for medi­cal questions because the input of data—to cite Sinek—only follows a “nite” goal.

Surgeons must know, therefore, for what purpose they want to gather data, so they can determine which database best suits that purpose.

Regarding how much information should be gathered, a wide gap exists between the aspira­tion to gather “all the data that can be collected” and the real-world expenditure in time and money that is needed to accomplish this.

confusion in the data, clarity is provided by means of “pop-up” information. In clinical routine, collecting data is not a trivial task. Scant time is available to harvest patient data from les and OP reports and transfer it to the registry. Entering the data of a single patient into a robust database will take between 15 and 30 min depending on its complexity, with a similar investment of time for every subsequent follow­up. The Swedish Hernia Registry has addressed this problem by reducing the number of ques­tions to a minimum. Moreover, a large propor­tion of the questions can be gathered perioperatively by the nursing staff in the OP room and in postoperative consultation with the surgeon. In theory, regarding the collection of follow-up data, patients do not need to be sched­uled for ambulant questioning but can be con­tacted by post, by telephone, or by apps. It is imperative, though, that the ethical, insurance, and legal parameters for follow-ups are claried in advance.

of statistics in scientic papers, he speaks of data that is “p-certied, not p-hacked” [8]. For the statistician Richard Royall (Johns Hopkins, Baltimore), three questions must be answered after completion of every study: (a) What evi­dence is presented? (b) Should I believe it? And (c) what can I do with it? Certainly a single method (not even a single registry) cannot answer these questions. Results from registries and data­banks can be taken as an incentive to reect and try new approaches to research. Steven Goodman (Stanford University) says: “The numbers (i.e., the statistical ndings, comment of the author) are where the scientic discussion should start, not end” [9].