Get 20M+ Full-Text Papers For Less Than $1.50/day. Start a 14-Day Trial for You or Your Team.

Learn More →

Surgical site infection surveillance in German hospitals: a national survey to determine the status quo of digitalization

Surgical site infection surveillance in German hospitals: a national survey to determine the... Background Surveillance of surgical site infections (SSI) relies on access to data from various sources. Insights into the practices of German hospitals conducting SSI surveillance and their information technology (IT ) infrastructures are scarce. The aim of this study was to evaluate current SSI surveillance practices in German hospitals with a focus on employed IT infrastructures. Methods German surgical departments actively participating in the national SSI surveillance module “OP-KISS” were invited in August 2020 to participate in a questionnaire-based online survey. Depending on whether departments entered all data manually or used an existing feature to import denominator data into the national surveillance data- base, departments were separated into different groups. Selected survey questions differed between groups. Results Of 1,346 invited departments, 821 participated in the survey (response rate: 61%). Local IT deficits (n = 236), incompatibility of import specifications and hospital information system (n = 153) and lack of technical expertise (n = 145) were cited as the most frequent reasons for not using the denominator data import feature. Conversely, reduction of workload (n = 160) was named as the main motivation to import data. Questions on data availability and accessibility in the electronic hospital information system (HIS) and options to export data from the HIS for the purpose of surveillance, yielded diverse results. Departments utilizing the import feature tended to be from larger hospitals with a higher level of care. Conclusions The degree to which digital solutions were employed for SSI surveillance differed considerably between surgical departments in Germany. Improving availability and accessibility of information in HIS and meeting inter- operability standards will be prerequisites for increasing the amount of data exported directly from HIS to national databases and laying the foundation for automated SSI surveillance on a broad scale. Keywords Automation, Digitalization, Surveillance, Surgical site infection, Healthcare-associated infection, Digital infection control Seven Johannes Sam Aghdassi and Hengameh Goodarzi have contributed equally to this work. *Correspondence: Seven Johannes Sam Aghdassi seven-johannes-sam.aghdassi@charite.de Full list of author information is available at the end of the article © The Author(s) 2023. Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http:// creat iveco mmons. org/ licen ses/ by/4. 0/. The Creative Commons Public Domain Dedication waiver (http:// creat iveco mmons. org/ publi cdoma in/ zero/1. 0/) applies to the data made available in this article, unless otherwise stated in a credit line to the data. Aghdassi et al. Antimicrobial Resistance & Infection Control (2023) 12:49 Page 2 of 10 regarding the employed hospital information systems Background (HIS). The abovementioned option to import denomi - Surgical site infections (SSI) represent one of the most nator data into webKess, however, represents a first step frequently occurring types of healthcare-associated in the direction of utilizing information technology (IT) infections (HAI) and entail a substantial burden of dis- solutions for surveillance, and possibly automated sur- ease [1–3]. Surveillance of SSI has been demonstrated veillance. Neither the degree to which IT solutions are repeatedly to effectively prevent infections and reduce currently harnessed to conduct surveillance, nor per- SSI rates [4, 5], and is therefore recommended as a pre- ceived challenges and initiatives for doing so, have been ventive strategy by the World Health Organization [6, described for German hospitals, rendering it difficult to 7]. SSI surveillance in Germany has a longstanding tra- assess the full potential of automated SSI surveillance in dition and is organized in the module “OP-KISS” of the Germany. national surveillance network “KISS” (German: Krank- To better understand the current state of data collec- enhaus-Infektions-Surveillance-System) [8]. The German tion methods and use of digital infrastructures for SSI National Reference Center for Surveillance of Nosoco- surveillance in Germany, the NRC conducted a survey mial Infections (NRC) organizes and coordinates surveil- among OP-KISS participants. lance activities in KISS. Over 1000 surgical departments regularly participate in OP-KISS [9, 10]. Participat- Methods ing departments are located primarily in Germany, and In 2020, the NRC created a survey to be sent out to surgi- in smaller numbers in Austria and Switzerland. Par- cal departments actively participating in OP-KISS. Active ticipation in OP-KISS is voluntary and data is primarily participation was defined as having transferred SSI sur - intended for internal quality assessment. Additionally, veillance data for 2018 or 2019 to the NRC. Departments data is transmitted by OP-KISS participants to the NRC, invited to participate were located in Germany, Aus- enabling the NRC to calculate aggregated reference data. tria or Switzerland. Actively participating departments OP-KISS is based on so-called “indicator procedures” were divided into three groups, based on whether or not that each comprise various procedure codes. Participants they had used the webKess import function in the pre- in OP-KISS can freely choose for which indicator proce- vious two years. “Group A” was defined as departments dures they perform SSI surveillance. As specified in the that had entered data for the years 2018 and 2019 only OP-KISS methodology, participating departments collect manually. “Group B” consisted of departments that had data for eligible procedures and observe patients for SSI imported denominator data for 2018, but not 2019. occurrence for a defined period, 30 or 90  days depend - Departments that had imported denominator data for ing on the type of indicator procedure. Surveillance ends 2019 constituted “group C”, irrespective of their mode prematurely in case of reoperation or death [11]. Data of data entry in 2018. The survey followed the same collection and interpretation as well as data transfer structure for all three groups and questions were mostly to the NRC are performed by staff at the local hospital. identical. Only where considered necessary by the inves- For data transfer to the NRC, participating departments tigators, specific questions differed between groups. In must use a specific surveillance web portal “webKess” all cases, the survey comprised of ten questions, with (https:// webke ss. chari te. de/), into which data can either additional sub-questions that had to be answered only be entered manually or imported. Data import generally if certain answers to the original ten questions were pertains to denominator data, although import of numer- selected by the respondents. Thematically, the survey can ator data (i.e. SSI) is possible. be divided into three topics: general IT and HIS aspects; Conventionally, SSI surveillance relies on a manual the webKess import feature and reasons for using or not process to identify eligible procedures and subsequently using it; and strategies employed in the practice of SSI observe operated patients concerning SSI occurrence. surveillance. Accordingly, conventional SSI surveillance frequently The survey was conducted online using Limesurvey represents a laborious and time-consuming process, (https:// www. limes urvey. org/). The survey language was usually resulting in the pragmatic but restrictive deci- German. An English translation of the survey documents sion to observe only selected types of surgeries [12, 13]. can be found in the online supplement (Additional File Automation of certain work steps may offer an impor - 1). An invitation to participate in the survey was sent to tant enhancement to HAI surveillance in general and the main contact person for every included department SSI surveillance in particular, and a means to reduce the on August 4, 2020. Data entry was possible until Sep- required workload [14–17]. Unlike several other coun- tember 30, 2020. A reminder was sent on September 1, tries [17], no large-scale automated SSI surveillance 2020. Participation in the survey was voluntary. Multiple systems exist yet in Germany. This may be due to digi - departments per hospital could participate in the survey, talization deficits in German hospitals and heterogeneity A ghdassi et al. Antimicrobial Resistance & Infection Control (2023) 12:49 Page 3 of 10 but participation was only possible once per individual 66%), and of 290 invited group C departments, 193 con- department. The survey had to be fully completed in one ducted the survey (response rate: 67%). session. Once the survey was finished, all entered data Participating departments in group A were from hos- were automatically sent to the NRC. After that, partici- pitals with a considerably lower median number of beds pants could no longer modify responses. In cases where than departments in groups B&C (327 vs. 490). Whereas participants realized afterwards that mistakes had been the percentage of departments that were located in pub- made, corrections could be requested by contacting the lic hospitals was comparable between survey groups (28% study team. group A vs. 32% groups B&C), differences were noted After reception of the responses, the NRC evaluated concerning the percentage of departments from tertiary the data. For the purpose of this analysis, questions of or maximum care hospitals (35% in group A vs. 47% in particular value to describe the current state of data col- groups B&C). The median number of procedures per lection methods and employed digital infrastructures in department transmitted to the NRC for the years 2018 the context of SSI surveillance in Germany were selected, and 2019 was lower in group A (291) than groups B&C and datasets from Austrian or Swiss departments were (315). Further structural characteristics of participating excluded. Included survey datasets were matched with departments are illustrated in Table 1. data on structural characteristics and number of surger- ies under surveillance from the OP-KISS database. As General IT and HIS aspects will be demonstrated in the results section below, only The majority (53% [434 of 821]) of survey participants few datasets from group B were received. In response to reported utilizing an infection prevention and control this, the study team decided to combine groups B and (IPC) software from an external provider that can assist C when presenting most survey results. The decision to the extraction of data relevant to HAI surveillance from combine group B with group C, instead of group A, was the HIS. Differentiated by group, 46% (279 of 605) of made based on the consideration that departments in departments in group A, and 72% (155 of 216) of depart- both group B and group C had used the import feature ments in groups B&C reported employing external IPC for at least one of the two years. A separate presentation software. While 50% (303 of 605) of departments in of results from groups B and C will be given only when group A reported receiving support for their surveillance necessary due to differing questions between survey activities from the hospital IT team, this number was 75% versions. (161 of 216) for groups B&C. Of the 302 departments in group A that reported not already receiving IT support Results for their surveillance activities, 96 (32%) saw the prospect A total of 1,346 German surgical departments from 707 for IT support in the future, in groups B&C the same hospitals received an invitation to participate in the sur- assessment was made by 27% (15 of 55) of departments. vey. Altogether, 821 surgical departments (department Table  2 documents the availability of important vari- response rate: 61%) from 469 hospitals (hospital response ables for SSI surveillance in the HIS. Small differences rate: 66%) conducted the survey. Stratified by the defined concerning the availability of data between groups A and groups, of 1021 invited group A departments, 605 par- B&C were observed, for instance regarding the availabil- ticipated in the survey (response rate: 59%), of 35 invited ity of the wound contamination class (67% group A vs. group B departments, 23 participated (response rate: 77% groups B&C). Table 1 Structural characteristics at the hospital level and number of procedures transmitted to the national reference center of 605 German surgical departments in group A and 216 German surgical departments in groups B&C that participated in the survey Variable Group A Number (percentage) or Groups B&C Number Median (interquartile range) (percentage) or Median (interquartile range) Number of hospital beds 327 (204, 563) 490 (305, 686) Departments in tertiary or maximum care hospitals 211 (34.9) 101 (46.8) Departments in non-tertiary, non-maximum care* hospitals 394 (65.1) 115 (53.2) Departments in public hospitals 171 (28.3) 69 (31.9) Departments in non-public hospitals 434 (71.7) 147 (68.1) Number of procedures under surveillance in 2018 and 2019 291 (156, 532) 315 (163, 626) * # § Contains primary care, secondary care, specialized care and unspecified. Contains private for profit, private non-profit, ecclesiastical, other and unspecified. Refers to all transmitted procedures, incl. ones marked as “during surveillance pause” or “not valid for reference data” Aghdassi et al. Antimicrobial Resistance & Infection Control (2023) 12:49 Page 4 of 10 Table 2 Availability of variables for surgical site infection surveillance in the hospital information system. Responses from 605 German surgical departments in group A and 216 German surgical departments in groups B&C Variable Available in HIS—Group A Number (percentage) Available in HIS— Groups B&C Number (percentage) Type of surgery (procedure code) 601 (99.3) 216 (100) Date of surgery 600 (99.2) 215 (99.5) Age (year of birth) 601 (99.3) 216 (100) Sex 598 (98.8) 216 (100) Date of hospital admission 594 (98.2) 209 (96.8) Date of hospital discharge 595 (98.3) 208 (96.3) ASA score 536 (88.6) 203 (94.0) Wound contamination class 403 (66.6) 166 (76.9) Duration of surgery 596 (98.5) 210 (97.2) Endoscopic (yes or no)* 500 (82.6) 172 (79.6) Urgent procedure (yes or no)* 449 (74.2) 159 (73.6) Revision surgery (yes or no)* 432 (71.4) 152 (70.4) Implant (yes or no)* 483 (79.8) 164 (75.9) Surgical site infection data 348 (57.5) 122 (56.5) Premature end of surveillance (due to reoperation or death) 401 (66.3) 143 (66.2) * # Collected only for selected types of indicator procedures. According to the OP-KISS methodology, urgent procedures are surgeries that were not planned 24 h or longer in advance. Abbreviations: ASA: American Society of Anesthesiologists; HIS: hospital information system A survey question focusing on the electronic avail- could be exported. The responses are summarized in ability of microbiological findings important for SSI sur - Table 3. In general, availability of variables for export was veillance (e.g. wound swabs) yielded congruous results higher in groups B&C than in group A. between groups. In group A, 90% (542 of 605) of depart- In group A, 241 departments reported that they could ments reported that microbiological findings were avail - export the type of surgery (i.e. the procedure code) from able electronically. In groups B&C, responses were the HIS, in groups B&C, it was 169 departments respec- similar (94% [203 of 216]). Departments that stated that tively. These departments were requested to specify how microbiological results were available electronically, were the allocation from procedure code to the corresponding asked to further specify whether they were available in OP-KISS indicator procedure type was executed. Here, a structured and machine-readable format (e.g. FHIR , 46% (111 of 241) in group A, and 89% (150 of 169) in CSV, HL7 v2.x). Here, 34% (182 of 542) of departments in groups B&C reported that this was performed automati- group A stated that this was the case. In groups B&C, the cally, either as a feature of the export from the HIS, or percentage was considerably higher (60% [121 of 203]). by directly importing the procedure code into webKess. Conversely, 49% (118 of 241) of departments in group webKess import function A and 9% (16 of 169) in groups B&C reported that this When asked whether data from the HIS could be was done manually by staff. The remaining departments exported to an external data management software (e.g. either specified another method or did not provide a ® ® Microsoft Excel ) and/or directly to webKess, 41% (249 response. of 605) of departments in group A replied that this was To learn more about potential hurdles of a direct possible, while 79% (171 of 216) of departments in groups export of HIS data to webKess, participants that reported B&C did so. Further information, including a distinction they could export HIS data to an external data manage- whether export was possible to both an external data ment software but not to webKess directly, were asked management software and webKess, or to only one of which parameters required manual editing before import the two, is provided in Fig. 1. The figure reveals that par - into webKess. The responses are summarized in Table  4 ticularly the possibility to export directly from the HIS to and demonstrate that manual editing is necessary more webKess is lower in group A than groups B&C. frequently in group A than groups B&C. Departments that reported that data export from the Depending on the group that departments were allo- HIS was possible, were additionally asked to specify the cated to, survey questions exploring recent use or non- parameters with significance to SSI surveillance that use of the webKess import feature differed. Departments A ghdassi et al. Antimicrobial Resistance & Infection Control (2023) 12:49 Page 5 of 10 100% 90% 80% 70% 60% 50% 40% 30% 20% 10% 0% Group AGroups B&C Export to both external data management software and webKess Export only to webKess Export only to external data management software No export or unknown Fig. 1 Feasibility of data export from the hospital information system. Responses from 605 German surgical departments in group A and 216 German surgical departments in groups B&C Table 3 Parameters exportable from the hospital information system Parameter Exportable from HIS—Group A Number Exportable from HIS— (percentage) Groups B&C Number (percentage) Type of surgery (procedure code) 241 (96.8) 169 (98.8) Date of surgery 238 (95.6) 169 (98.8) Age (year of birth) 236 (94.8) 169 (98.8) Sex 223 (89.6) 165 (96.5) Date of hospital admission 206 (82.7) 151 (88.3) Date of hospital discharge 209 (83.9) 142 (83.0) ASA score 174 (69.9) 155 (90.6) Wound contamination class 137 (55.0) 141 (82.5) Duration of surgery 205 (82.3) 163 (95.3) Endoscopic (yes or no) 150 (60.2) 131 (76.6) Urgent procedure (yes or no) 121 (48.6) 104 (60.8) Revision surgery (yes or no) 97 (39.0) 100 (58.5) Implant (yes or no) 122 (49.0) 125 (73.1) Surgical site infection data 81 (32.5) 72 (42.1) Premature end of surveillance (due to reoperation or death) 74 (29.7) 79 (46.2) Responses from 249 German surgical departments in group A and 171 German surgical departments in groups B&C that reported that data export from the hospital information system was possible According to the OP-KISS methodology, urgent procedures are surgeries that were not planned 24 h or longer in advance. Abbreviations: ASA: American Society of Anesthesiologists; HIS: hospital information system in group A had to state, why they had not previously used import specifications and HIS (n = 153), and lack of tech- the import function. The primary reasons provided were nical expertise (n = 145). Departments in group B had local IT deficits (n = 236), incompatibility of webKess to state, why they had discontinued using the import Aghdassi et al. Antimicrobial Resistance & Infection Control (2023) 12:49 Page 6 of 10 Table 4 Exportable parameters from the hospital information system requiring manual editing before import into webKess Parameter Requiring manual editing—Group A Number Requiring manual editing— (percentage) Groups B&C Number (percentage) Type of surgery (procedure code) 106 (52.5) 5 (7.7) Date of surgery 63 (31.2) 8 (12.3) Age (year of birth) 65 (32.2) 8 (12.3) Sex 73 (36.1) 8 (12.3) Date of hospital admission 76 (37.6) 3 (4.6) Date of hospital discharge 76 (37.6) 7 (10.8) ASA score 115 (56.9) 20 (30.8) Wound contamination class 134 (66.3) 30 (46.2) Duration of surgery 85 (42.1) 10 (15.4) Endoscopic (yes or no) 102 (50.5) 18 (27.7) Urgent procedure (yes or no) 112 (55.4) 29 (44.6) Revision surgery (yes or no) 123 (60.9) 25 (38.5) Implant (yes or no) 117 (57.9) 29 (44.6) Surgical site infection data 159 (78.7) 55 (84.6) Premature end of surveillance (due to reoperation or 154 (76.2) 39 (60) death) Responses from 202 German surgical departments in group A and 65 German surgical departments in groups B&C that reported export from the hospital information system was possible only to an external data documentation software, but not directly to webKess According to the OP-KISS methodology, urgent procedures are surgeries that were not planned 24 h or longer in advance. Abbreviations: ASA: American Society of Anesthesiologists; HIS: hospital information system function. Lack of technical expertise (n = 4) was the most 42% (253 of 605) of departments in group A, and 32% (69 frequently provided answer, with most other answers of 216) of departments in groups B&C. being provided as free text often citing local structural and process changes. Departments in group C were Discussion asked to state the reasons for utilizing the import func- To the best of our knowledge, this survey provides the tion, with reduction of workload (n = 160) most com- first detailed description of IT infrastructures used by monly reported. Furthermore, departments in group C hospitals for conducting SSI surveillance within a large were asked whether they used the import function, not national surveillance network. To inform a better under- only for denominator data but also for numerator data standing of the international situation regarding this (i.e. data on SSI), with 54% (104 of 193) reportedly doing matter, and to strengthen international cooperation in so. the field of SSI surveillance, we wish to encourage other countries and surveillance networks to conduct similar surveys. Practice of SSI surveillance As was expected, analysis of the survey results revealed When asked to describe the process of SSI surveillance, heterogeneity concerning availability and utilization of IT responses between group A and groups B&C were largely options in the practice of SSI surveillance among German consistent, with review of microbiological findings (95% surgical departments. Although not an outcome param- [573 of 605] in group A, 92% [199 of 216] in groups B&C) eter of the survey itself, this becomes apparent already and actively inquiring updates of treating staff (62% [378 when comparing the number of departments per group of 605] in group A, 72% [155 of 216] in groups B&C) that were invited to participate in the survey. Group A, being the most common regularly (i.e. “frequently”, “very which was defined by manual data entry into webKess, frequently” or “always” selected as response) performed contained more than three times as many departments surveillance strategies in both groups. Moreover, partici- than groups B&C, which had imported denominator pants were asked whether they regularly continued sur- data into webKess in at least one of the two considered veillance after patients were discharged from the hospital years, illustrating that SSI surveillance is still largely a (so-called “post-discharge surveillance”). Post-discharge manual process in Germany. Given that automated HAI surveillance was reportedly performed systematically by surveillance and even automated identification of eligible A ghdassi et al. Antimicrobial Resistance & Infection Control (2023) 12:49 Page 7 of 10 operations (i.e. denominator data) can be a means to save possible, which was substantially lower than for groups much needed resources [15, 17, 18], the observed distri- B&C. It is important however to be mindful, that docu- bution of departments into the respective groups, docu- mented deficits could be to a certain extent be overesti - ments a high unused potential to save IPC resources in mated by the fact that some respondents, due to lack of Germany. This interpretation is supported, when consid - adequate local IT support, might have been unaware of ering that reduction of workload was stated as the pri- data export options that actually existed but were merely mary motivation for using the webKess import feature. not utilized. Both interpretations however, the lack of Departments utilizing the import feature were found to specific IT features or the missed opportunity of using be from larger hospitals and from hospitals with a higher existing features, highlight the importance of consider- level of care than departments relying solely on manual ing surveillance use cases when designing or respectively data entry. This finding seems to corroborate the some - selecting HIS, and the significance of ensuring interoper - what intuitive assumption that tertiary care hospitals and ability between systems linked to the process of HAI sur- larger hospitals in general have more technical options at veillance [21]. their disposal. Interestingly, differences between groups Our survey provides further insights into this matter by with regards to public versus private hospital owner- detailing for individual surveillance parameters, whether ship were small. The fact that the number of procedures data export from HIS was possible, and whether manual transmitted to the NRC for the years 2018 and 2019 was editing of data before webKess import was necessary. It higher in groups B&C suggests that time conventionally is particularly critical that important procedure-related spend on manual entry of denominator data, can be real- variables, such as wound contamination class, surgical located to perform SSI surveillance for a higher number access route (endoscopic vs. open), duration of surgery of procedures. This interpretation is in alignment with and American Society of Anesthesiologists (ASA) score, studies concluding that automating certain aspects of SSI were frequently reported to be not exportable from HIS, surveillance offers potentials to increase the number of or requiring manual editing if they were. This finding is observed procedures [14]. to a certain extent surprising, since wound contamina- Our survey offered valuable insights into the underly - tion class, ASA score and duration of surgery are used ing reasons for the high number of departments still rely- for risk stratification in OP-KISS [22]. Similarly, around ing solely on manual data entry. Only half of departments half of departments in group A reported that the alloca- in group A reported receiving IT support for conduct- tion from procedure code to the corresponding OP-KISS ing surveillance, whereas in groups B&C IT support was indicator procedure was performed manually by staff. available considerably more often (circa 75%). Moreo- According to the OP-KISS methodology, the allocation ver, the fact that only around one third of departments to the correct indicator procedure is a prerequisite to not already receiving IT support, were hopeful to receive collect any useful surveillance data at all [11]. Therefore, support in the future, revealed significant shortcomings the need for a manual process to assign an operation to concerning IT support. This interpretation is reinforced the appropriate indicator procedure must be viewed as a when considering the stated reasons, why the webKess clear potential for improvement. import feature had not been used. IT deficits, technical The results and parameters discussed thus far predomi - incompatibilities and lack of technical expertise were nately pertained to denominator data. However, when seen as the main barriers to data import. Our survey trying to explore potentials to automate SSI case find - therefore highlights the importance of prioritizing inter- ing, other variables should be considered as well. Various professional cooperation and support from dedicated IT variables have been identified to yield particular value for teams, when setting up structures for HAI surveillance. automated SSI surveillance, for instance, microbiological The significance of tailoring local systems to perform sur - findings, hospital admissions, (revision) surgeries, and veillance functions has been discussed in previous publi- antimicrobial prescriptions [14]. To gain insights into this cations [19, 20]. Evidently, local IT support represents a aspect, a question regarding the availability of microbio- prerequisite for this process. logical findings was included in the survey. In both group Our survey uncovered additional factors forcing IPC A and groups B&C, electronic availability of this infor- staff to perform surveillance manually, beyond lack of mation was widespread. While this can be viewed as a IT support and expertise. While the general availability promising potential for automation, differences between of variables for SSI surveillance in HIS was comparable the groups concerning the format and machine-reada- between the different survey groups, pronounced differ - bility of microbiological results, call for a more nuanced ences were noted concerning the option to export data interpretation. Groups B&C were found to have micro- from HIS. Fewer than half of departments in group A biological results available in a structured and machine- reported that exporting surveillance data from HIS was readable format decidedly more often than group A (60% Aghdassi et al. Antimicrobial Resistance & Infection Control (2023) 12:49 Page 8 of 10 vs. 34%). However, even in groups B&C more than a third given the long trust-building history of conducting of departments reported that microbiological results surveys in the KISS network [27–29], we assess this were not available in a structured and machine-reada- risk to be rather low. Forth, certain questions, specifi - ble format, which illustrates that for departments from cally when pertaining to technical aspects, might have both groups harnessing microbiological data for auto- been difficult to understand for some survey recipients, mated surveillance might be challenging. This once again which were typically IPC professionals. To counteract stresses the crucial role of ensuring data standardization this, survey participants were encouraged to seek assis- and meeting interoperability standards in the context of tance from other professional groups (e.g. IT team) HAI surveillance [21]. whenever necessary. Nevertheless, responses indicat- In a separate section of the survey, the practice of SSI ing the non-availability of data or specific features, par - surveillance by participating departments was investi- ticularly pertaining to data export and import, might in gated. Irrespective of the survey group, review of micro- some cases not accurately reflect the actual situation, biological results and active information gathering from but rather a lack of knowledge of the respondent. Last, treating staff, were named as commonly employed sur - if data was entered erroneously, participants could not veillance strategies. As delineated above, interpretation of perform corrections themselves, but had to contact the microbiological results entails a high potential for auto- study team, requiring more effort than simply re-enter - mation. Provided a consistent way of documenting the ing the questionnaire and changing a response. Thus, clinical course of patients after surgery, information gath- the analyzed dataset might have contained incorrect ering from treating staff could be assisted by algorithms responses. However, to reduce this risk to a minimum, searching for key terms in the patient file, thus also rep - participants were advised to print out the survey on resenting a strategy that could be partly automated. This paper, and fill in answers before entering data into the interpretation is reinforced by the fact that over half of online survey template. departments in group C reported having used the web- Kess import function also for numerator data. Similarly, the practice of post-discharge surveillance should be Conclusions considered in future automated surveillance strategies, IT infrastructures play an important part in the prac- given that between one third and one half of departments tice of SSI surveillance in Germany. The degree, to reported performing it systematically. The crucial role which they are harnessed, however, varies considerably of adequate post-discharge surveillance for detecting a between surgical departments. Local IT deficits, tech - substantial portion of SSI has been described in various nical difficulties and general lack of local IT support, publications [23–25]. The Hospital-Acquired Infections were found to hinder the use of existing data import Database (HAIBA) from Denmark represents a prime features. To increase the amount of data exported example of intersectoral data exchange for the purpose of directly from local HIS to the national surveillance continuing HAI surveillance after hospital discharge [26]. database, and therefore lay the foundation for auto- Several limitations have to acknowledged when mated SSI surveillance in Germany, hospitals should interpreting the survey results. First, the survey was seek solutions to improve availability and accessibil- not distributed to a representative sample of surgical ity of information in HIS, and ensure necessary data departments, but to all OP-KISS participants that met standardization as well as adherence to interoperability the inclusion criteria. Consequently, statements con- standards. The results of our survey strongly indicate cerning the national situation have to be made with that hospitals in Germany and their digital subsystems caution. Nevertheless, due to the large number of par- lag far behind contemporary standards. ticipating departments, careful generalizations to the national situation appear to be warranted. Second, the Abbreviations survey was based on voluntary participation. Accord- ASA American society of anesthesiologists ingly, departments with a particular interest in the sur-HAI Healthcare-associated infection(s) HAIBA Hospital-acquired infections database vey topic may be overrepresented, which could distort HIS Hospital information system(s) survey results towards an overestimation of the use of IPC Infection prevention and control IT infrastructures for surveillance. Third, although all IT Information technology KISS Krankenhaus-infektions-surveillance-system data was handled confidentially, some survey questions NRC National reference center might have been perceived as potentially compromis- SSI Surgical site infection(s) ing, which could result in “wishful reporting”. However, A ghdassi et al. Antimicrobial Resistance & Infection Control (2023) 12:49 Page 9 of 10 3. Cassini A, Plachouras D, Eckmanns T, Abu Sin M, Blank HP, Ducomble T, Supplementary Information et al. Burden of six healthcare-associated infections on european popula- The online version contains supplementary material available at https:// doi. tion health: estimating incidence-based disability-adjusted life years org/ 10. 1186/ s13756- 023- 01253-9. through a population prevalence-based modelling study. PLoS Med. 2016;13(10): e1002150. Additional file 1. Translated version of the OP-KISS survey on SSI surveil- 4. Brandt C, Sohr D, Behnke M, Daschner F, Ruden H, Gastmeier P. Reduction lance and digitalization. of surgical site infection rates associated with active surveillance. Infect Control Hosp Epidemiol. 2006;27(12):1347–51. 5. Geubbels EL, Nagelkerke NJ, Mintjes-De Groot AJ, Vandenbroucke- Acknowledgements Grauls CM, Grobbee DE, De Boer AS. Reduced risk of surgical site We wish to thank all hospitals participating in the German national nosoco- infections through surveillance in a network. Int J Qual Health Care. mial infection surveillance system for surgical site infections. Dr Aghdassi is 2006;18(2):127–33. participant in the Charité Digital Clinician Scientist Program funded by the 6. Global guidelines for the prevention of surgical site infection, second DFG, the Charité Universitätsmedizin – Berlin, and the Berlin Institute of Health edition. Geneva: World Health Organization; 2018. Licence: CC BY-NC-SA at Charité (BIH). 3.0 IGO. 7. Guidelines on core components of infection prevention and control Author contributions programmes at the national and acute health care facility level. Geneva: SA, HG and MB formulated the research question of this manuscript. AG, World Health Organization; 2016. Licence: CC BY-NC-SA 3.0 IGO. JC, BP, CG and PG gave valuable input in the process of selecting suitable 8. Schroder C, Schwab F, Behnke M, Breier AC, Maechler F, Piening B, et al. survey questions. AG and JC were in charge of disseminating the survey and Epidemiology of healthcare associated infections in Germany: Nearly 20 managing responses. SA and HG drafted the manuscript with the input of all years of surveillance. Int J Med Microbiol. 2015;305(7):799–806. other authors. All authors agreed to the final version of the manuscript and its 9. Aghdassi SJS, Schwab F, Hoffmann P, Gastmeier P. The association of submission for publication. climatic factors with rates of surgical site infections: 17 years’ data from hospital infection surveillance. Dtsch Arztebl Int. 2019;116(31–32):529–36. Funding 10. Kramer TS, Schroder C, Behnke M, Aghdassi SJ, Geffers C, Gastmeier P, Open Access funding enabled and organized by Projekt DEAL. et al. Decrease of methicillin resistance in Staphylococcus aureus in nosocomial infections in Germany-a prospective analysis over 10 years. J Availability of data and materials Infect. 2019;78(3):215–9. Not applicable, because all data were surveillance-based data which were 11. OP-KISS Protokoll: Surveillance postoperativer Wundinfektionen [OP-KISS obtained in accordance with the German Protection against Infection Act. protocol: surveillance of surgical site infections]. National Reference Center for Surveillance of Nosocomial Infections. 2020. Available from: https:// www. nrz- hygie ne. de/ files/ Proto kolle/ OP- Proto kolle/ Wundi nfekt Declarations ionen/ OP_ KISS_ Proto koll_ WI_ v2020 11. pdf. Accessed: 3 Feb 2023. 12. Mitchell BG, Hall L, Halton K, MacBeth D, Gardner A. Time spent by infec- Ethics approval and consent to participate tion control professionals undertaking healthcare associated infection Not applicable, because all data were surveillance-based data which were surveillance: a multi-centred cross sectional study. Infection, Disease & obtained in accordance with the German Protection against Infection Act. Health. 2016;21(1):36–40. 13. Stricof RL, Schabses KA, Tserenpuntsag B. Infection control resources in Consent for publication New York State hospitals, 2007. Am J Infect Control. 2008;36(10):702–5. Not applicable, because all data were surveillance-based data which were 14. Sips ME, Bonten MJM, van Mourik MSM. Semiautomated surveillance of obtained in accordance with the German Protection against Infection Act. deep surgical site infections after primary total hip or knee arthroplasty. Infect Control Hosp Epidemiol. 2017;38(6):732–5. Competing interests 15. van Mourik MSM, van Rooden SM, Abbas M, Aspevall O, Astagneau P, The authors declare that they have no competing interests. Bonten MJM, et al. PRAISE: providing a roadmap for automated infection surveillance in Europe. Clin Microbiol Infect. 2021;27:S3–19. Author details 16. Verberk JDM, van Rooden SM, Koek MBG, Hetem DJ, Smilde AE, Bril WS, Charité – Universitätsmedizin Berlin, corporate member of Freie Universität et al. Validation of an algorithm for semiautomated surveillance to detect Berlin and Humboldt-Universität zu Berlin, Institute of Hygiene and Environ- deep surgical site infections after primary total hip or knee arthroplasty-A mental Medicine, Hindenburgdamm 27, 12203 Berlin, Germany. National multicenter study. Infect Control Hosp Epidemiol. 2021;42(1):69–74. Reference Center for Surveillance of Nosocomial Infections, Hindenburgdamm 17. Verberk JDM, Aghdassi SJS, Abbas M, Naucler P, Gubbels S, Maldonado 27, 12203 Berlin, Germany. Berlin Institute of Health at Charité – Universitäts- N, et al. Automated surveillance systems for healthcare-associated medizin Berlin, BIH Biomedical Innovation Academy, BIH Charité Digital Clini- infections: results from a European survey and experiences from real-life cian Scientist Program, Anna-Louisa-Karsch-Straße 2, 10178 Berlin, Germany. utilization. J Hosp Infect. 2022;122:35–43. 18. van Mourik MS, Troelstra A, van Solinge WW, Moons KG, Bonten MJ. Auto- Received: 21 February 2023 Accepted: 11 May 2023 mated surveillance for healthcare-associated infections: opportunities for improvement. Clin Infect Dis. 2013;57(1):85–93. 19. de Bruin JS, Seeling W, Schuh C. Data use and effectiveness in electronic surveillance of healthcare associated infections in the 21st century: a systematic review. J Am Med Inform Assoc. 2014;21(5):942–51. References 20. Woeltje KF, Lin MY, Klompas M, Wright MO, Zuccotti G, Trick WE. Data 1. Behnke M, Aghdassi SJ, Hansen S, Diaz LAP, Gastmeier P, Piening B. The requirements for electronic surveillance of healthcare-associated infec- prevalence of nosocomial infection and antibiotic use in german hospi- tions. Infect Control Hosp Epidemiol. 2014;35(9):1083–91. tals. Dtsch Arztebl Int. 2017;114(50):851–7. 21. Behnke M, Valik JK, Gubbels S, Teixeira D, Kristensen B, Abbas M, et al. 2. Suetens C, Latour K, Karki T, Ricchizzi E, Kinross P, Moro ML, et al. Preva- Information technology aspects of large-scale implementation of auto- lence of healthcare-associated infections, estimated incidence and mated surveillance of healthcare-associated infections. Clin Microbiol composite antimicrobial resistance index in acute care hospitals and Infect. 2021;27(Suppl 1):S29–39. long-term care facilities: results from two European point prevalence 22. Brandt C, Hansen S, Sohr D, Daschner F, Ruden H, Gastmeier P. Finding surveys, 2016 to 2017. Euro Surveill. 2018;23(46):1800516. a method for optimizing risk adjustment when comparing surgical-site infection rates. Infect Control Hosp Epidemiol. 2004;25(4):313–8. Aghdassi et al. Antimicrobial Resistance & Infection Control (2023) 12:49 Page 10 of 10 23. Koek MB, Wille JC, Isken MR, Voss A, van Benthem BH. Post-discharge surveillance (PDS) for surgical site infections: a good method is more important than a long duration. Euro Surveill. 2015;20(8):21042. 24. Lower HL, Dale H, Eriksen HM, Aavitsland P, Skjeldestad FE. Surgical site infections after hip arthroplasty in Norway, 2005–2011: influence of duration and intensity of postdischarge surveillance. Am J Infect Control. 2015;43(4):323–8. 25. Mannien J, Wille JC, Snoeren RL, van den Hof S. Impact of postdischarge surveillance on surgical site infection rates for several surgical procedures: results from the nosocomial surveillance network in The Netherlands. Infect Control Hosp Epidemiol. 2006;27(8):809–16. 26. Gubbels S, Nielsen J, Voldstedlund M, Kristensen B, Schønheyder HC, Ellermann-Eriksen S, et al. National automated surveillance of hospital- acquired bacteremia in denmark using a computer algorithm. Infect Control Hosp Epidemiol. 2017;38(5):559–66. 27. Aghdassi SJS, Geffers C, Behnke M, Gropmann A, Gastmeier P, Kramer TS. Management of peripheral venous catheters and implementa- tion of guidelines in Germany: a national survey. J Hosp Infect. 2020;105(2):311–8. 28. Aghdassi SJS, Hansen S, Bischoff P, Behnke M, Gastmeier P. A national survey on the implementation of key infection prevention and control structures in German hospitals: results from 736 hospitals conducting the WHO infection prevention and control assessment framework (IPCAF). Antimicrob Resist Infect Control. 2019;8:73. 29. Stiller A, Schroder C, Gropmann A, Schwab F, Behnke M, Geffers C, et al. ICU ward design and nosocomial infection rates: a cross-sectional study in Germany. J Hosp Infect. 2017;95(1):71–5. Publisher’s Note Springer Nature remains neutral with regard to jurisdictional claims in pub- lished maps and institutional affiliations. Re Read ady y to to submit y submit your our re researc search h ? Choose BMC and benefit fr ? Choose BMC and benefit from om: : fast, convenient online submission thorough peer review by experienced researchers in your field rapid publication on acceptance support for research data, including large and complex data types • gold Open Access which fosters wider collaboration and increased citations maximum visibility for your research: over 100M website views per year At BMC, research is always in progress. Learn more biomedcentral.com/submissions http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Antimicrobial Resistance and Infection Control Springer Journals

Surgical site infection surveillance in German hospitals: a national survey to determine the status quo of digitalization

Loading next page...
 
/lp/springer-journals/surgical-site-infection-surveillance-in-german-hospitals-a-national-xKu4Ch08tA

References (36)

Publisher
Springer Journals
Copyright
Copyright © The Author(s) 2023
eISSN
2047-2994
DOI
10.1186/s13756-023-01253-9
Publisher site
See Article on Publisher Site

Abstract

Background Surveillance of surgical site infections (SSI) relies on access to data from various sources. Insights into the practices of German hospitals conducting SSI surveillance and their information technology (IT ) infrastructures are scarce. The aim of this study was to evaluate current SSI surveillance practices in German hospitals with a focus on employed IT infrastructures. Methods German surgical departments actively participating in the national SSI surveillance module “OP-KISS” were invited in August 2020 to participate in a questionnaire-based online survey. Depending on whether departments entered all data manually or used an existing feature to import denominator data into the national surveillance data- base, departments were separated into different groups. Selected survey questions differed between groups. Results Of 1,346 invited departments, 821 participated in the survey (response rate: 61%). Local IT deficits (n = 236), incompatibility of import specifications and hospital information system (n = 153) and lack of technical expertise (n = 145) were cited as the most frequent reasons for not using the denominator data import feature. Conversely, reduction of workload (n = 160) was named as the main motivation to import data. Questions on data availability and accessibility in the electronic hospital information system (HIS) and options to export data from the HIS for the purpose of surveillance, yielded diverse results. Departments utilizing the import feature tended to be from larger hospitals with a higher level of care. Conclusions The degree to which digital solutions were employed for SSI surveillance differed considerably between surgical departments in Germany. Improving availability and accessibility of information in HIS and meeting inter- operability standards will be prerequisites for increasing the amount of data exported directly from HIS to national databases and laying the foundation for automated SSI surveillance on a broad scale. Keywords Automation, Digitalization, Surveillance, Surgical site infection, Healthcare-associated infection, Digital infection control Seven Johannes Sam Aghdassi and Hengameh Goodarzi have contributed equally to this work. *Correspondence: Seven Johannes Sam Aghdassi seven-johannes-sam.aghdassi@charite.de Full list of author information is available at the end of the article © The Author(s) 2023. Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http:// creat iveco mmons. org/ licen ses/ by/4. 0/. The Creative Commons Public Domain Dedication waiver (http:// creat iveco mmons. org/ publi cdoma in/ zero/1. 0/) applies to the data made available in this article, unless otherwise stated in a credit line to the data. Aghdassi et al. Antimicrobial Resistance & Infection Control (2023) 12:49 Page 2 of 10 regarding the employed hospital information systems Background (HIS). The abovementioned option to import denomi - Surgical site infections (SSI) represent one of the most nator data into webKess, however, represents a first step frequently occurring types of healthcare-associated in the direction of utilizing information technology (IT) infections (HAI) and entail a substantial burden of dis- solutions for surveillance, and possibly automated sur- ease [1–3]. Surveillance of SSI has been demonstrated veillance. Neither the degree to which IT solutions are repeatedly to effectively prevent infections and reduce currently harnessed to conduct surveillance, nor per- SSI rates [4, 5], and is therefore recommended as a pre- ceived challenges and initiatives for doing so, have been ventive strategy by the World Health Organization [6, described for German hospitals, rendering it difficult to 7]. SSI surveillance in Germany has a longstanding tra- assess the full potential of automated SSI surveillance in dition and is organized in the module “OP-KISS” of the Germany. national surveillance network “KISS” (German: Krank- To better understand the current state of data collec- enhaus-Infektions-Surveillance-System) [8]. The German tion methods and use of digital infrastructures for SSI National Reference Center for Surveillance of Nosoco- surveillance in Germany, the NRC conducted a survey mial Infections (NRC) organizes and coordinates surveil- among OP-KISS participants. lance activities in KISS. Over 1000 surgical departments regularly participate in OP-KISS [9, 10]. Participat- Methods ing departments are located primarily in Germany, and In 2020, the NRC created a survey to be sent out to surgi- in smaller numbers in Austria and Switzerland. Par- cal departments actively participating in OP-KISS. Active ticipation in OP-KISS is voluntary and data is primarily participation was defined as having transferred SSI sur - intended for internal quality assessment. Additionally, veillance data for 2018 or 2019 to the NRC. Departments data is transmitted by OP-KISS participants to the NRC, invited to participate were located in Germany, Aus- enabling the NRC to calculate aggregated reference data. tria or Switzerland. Actively participating departments OP-KISS is based on so-called “indicator procedures” were divided into three groups, based on whether or not that each comprise various procedure codes. Participants they had used the webKess import function in the pre- in OP-KISS can freely choose for which indicator proce- vious two years. “Group A” was defined as departments dures they perform SSI surveillance. As specified in the that had entered data for the years 2018 and 2019 only OP-KISS methodology, participating departments collect manually. “Group B” consisted of departments that had data for eligible procedures and observe patients for SSI imported denominator data for 2018, but not 2019. occurrence for a defined period, 30 or 90  days depend - Departments that had imported denominator data for ing on the type of indicator procedure. Surveillance ends 2019 constituted “group C”, irrespective of their mode prematurely in case of reoperation or death [11]. Data of data entry in 2018. The survey followed the same collection and interpretation as well as data transfer structure for all three groups and questions were mostly to the NRC are performed by staff at the local hospital. identical. Only where considered necessary by the inves- For data transfer to the NRC, participating departments tigators, specific questions differed between groups. In must use a specific surveillance web portal “webKess” all cases, the survey comprised of ten questions, with (https:// webke ss. chari te. de/), into which data can either additional sub-questions that had to be answered only be entered manually or imported. Data import generally if certain answers to the original ten questions were pertains to denominator data, although import of numer- selected by the respondents. Thematically, the survey can ator data (i.e. SSI) is possible. be divided into three topics: general IT and HIS aspects; Conventionally, SSI surveillance relies on a manual the webKess import feature and reasons for using or not process to identify eligible procedures and subsequently using it; and strategies employed in the practice of SSI observe operated patients concerning SSI occurrence. surveillance. Accordingly, conventional SSI surveillance frequently The survey was conducted online using Limesurvey represents a laborious and time-consuming process, (https:// www. limes urvey. org/). The survey language was usually resulting in the pragmatic but restrictive deci- German. An English translation of the survey documents sion to observe only selected types of surgeries [12, 13]. can be found in the online supplement (Additional File Automation of certain work steps may offer an impor - 1). An invitation to participate in the survey was sent to tant enhancement to HAI surveillance in general and the main contact person for every included department SSI surveillance in particular, and a means to reduce the on August 4, 2020. Data entry was possible until Sep- required workload [14–17]. Unlike several other coun- tember 30, 2020. A reminder was sent on September 1, tries [17], no large-scale automated SSI surveillance 2020. Participation in the survey was voluntary. Multiple systems exist yet in Germany. This may be due to digi - departments per hospital could participate in the survey, talization deficits in German hospitals and heterogeneity A ghdassi et al. Antimicrobial Resistance & Infection Control (2023) 12:49 Page 3 of 10 but participation was only possible once per individual 66%), and of 290 invited group C departments, 193 con- department. The survey had to be fully completed in one ducted the survey (response rate: 67%). session. Once the survey was finished, all entered data Participating departments in group A were from hos- were automatically sent to the NRC. After that, partici- pitals with a considerably lower median number of beds pants could no longer modify responses. In cases where than departments in groups B&C (327 vs. 490). Whereas participants realized afterwards that mistakes had been the percentage of departments that were located in pub- made, corrections could be requested by contacting the lic hospitals was comparable between survey groups (28% study team. group A vs. 32% groups B&C), differences were noted After reception of the responses, the NRC evaluated concerning the percentage of departments from tertiary the data. For the purpose of this analysis, questions of or maximum care hospitals (35% in group A vs. 47% in particular value to describe the current state of data col- groups B&C). The median number of procedures per lection methods and employed digital infrastructures in department transmitted to the NRC for the years 2018 the context of SSI surveillance in Germany were selected, and 2019 was lower in group A (291) than groups B&C and datasets from Austrian or Swiss departments were (315). Further structural characteristics of participating excluded. Included survey datasets were matched with departments are illustrated in Table 1. data on structural characteristics and number of surger- ies under surveillance from the OP-KISS database. As General IT and HIS aspects will be demonstrated in the results section below, only The majority (53% [434 of 821]) of survey participants few datasets from group B were received. In response to reported utilizing an infection prevention and control this, the study team decided to combine groups B and (IPC) software from an external provider that can assist C when presenting most survey results. The decision to the extraction of data relevant to HAI surveillance from combine group B with group C, instead of group A, was the HIS. Differentiated by group, 46% (279 of 605) of made based on the consideration that departments in departments in group A, and 72% (155 of 216) of depart- both group B and group C had used the import feature ments in groups B&C reported employing external IPC for at least one of the two years. A separate presentation software. While 50% (303 of 605) of departments in of results from groups B and C will be given only when group A reported receiving support for their surveillance necessary due to differing questions between survey activities from the hospital IT team, this number was 75% versions. (161 of 216) for groups B&C. Of the 302 departments in group A that reported not already receiving IT support Results for their surveillance activities, 96 (32%) saw the prospect A total of 1,346 German surgical departments from 707 for IT support in the future, in groups B&C the same hospitals received an invitation to participate in the sur- assessment was made by 27% (15 of 55) of departments. vey. Altogether, 821 surgical departments (department Table  2 documents the availability of important vari- response rate: 61%) from 469 hospitals (hospital response ables for SSI surveillance in the HIS. Small differences rate: 66%) conducted the survey. Stratified by the defined concerning the availability of data between groups A and groups, of 1021 invited group A departments, 605 par- B&C were observed, for instance regarding the availabil- ticipated in the survey (response rate: 59%), of 35 invited ity of the wound contamination class (67% group A vs. group B departments, 23 participated (response rate: 77% groups B&C). Table 1 Structural characteristics at the hospital level and number of procedures transmitted to the national reference center of 605 German surgical departments in group A and 216 German surgical departments in groups B&C that participated in the survey Variable Group A Number (percentage) or Groups B&C Number Median (interquartile range) (percentage) or Median (interquartile range) Number of hospital beds 327 (204, 563) 490 (305, 686) Departments in tertiary or maximum care hospitals 211 (34.9) 101 (46.8) Departments in non-tertiary, non-maximum care* hospitals 394 (65.1) 115 (53.2) Departments in public hospitals 171 (28.3) 69 (31.9) Departments in non-public hospitals 434 (71.7) 147 (68.1) Number of procedures under surveillance in 2018 and 2019 291 (156, 532) 315 (163, 626) * # § Contains primary care, secondary care, specialized care and unspecified. Contains private for profit, private non-profit, ecclesiastical, other and unspecified. Refers to all transmitted procedures, incl. ones marked as “during surveillance pause” or “not valid for reference data” Aghdassi et al. Antimicrobial Resistance & Infection Control (2023) 12:49 Page 4 of 10 Table 2 Availability of variables for surgical site infection surveillance in the hospital information system. Responses from 605 German surgical departments in group A and 216 German surgical departments in groups B&C Variable Available in HIS—Group A Number (percentage) Available in HIS— Groups B&C Number (percentage) Type of surgery (procedure code) 601 (99.3) 216 (100) Date of surgery 600 (99.2) 215 (99.5) Age (year of birth) 601 (99.3) 216 (100) Sex 598 (98.8) 216 (100) Date of hospital admission 594 (98.2) 209 (96.8) Date of hospital discharge 595 (98.3) 208 (96.3) ASA score 536 (88.6) 203 (94.0) Wound contamination class 403 (66.6) 166 (76.9) Duration of surgery 596 (98.5) 210 (97.2) Endoscopic (yes or no)* 500 (82.6) 172 (79.6) Urgent procedure (yes or no)* 449 (74.2) 159 (73.6) Revision surgery (yes or no)* 432 (71.4) 152 (70.4) Implant (yes or no)* 483 (79.8) 164 (75.9) Surgical site infection data 348 (57.5) 122 (56.5) Premature end of surveillance (due to reoperation or death) 401 (66.3) 143 (66.2) * # Collected only for selected types of indicator procedures. According to the OP-KISS methodology, urgent procedures are surgeries that were not planned 24 h or longer in advance. Abbreviations: ASA: American Society of Anesthesiologists; HIS: hospital information system A survey question focusing on the electronic avail- could be exported. The responses are summarized in ability of microbiological findings important for SSI sur - Table 3. In general, availability of variables for export was veillance (e.g. wound swabs) yielded congruous results higher in groups B&C than in group A. between groups. In group A, 90% (542 of 605) of depart- In group A, 241 departments reported that they could ments reported that microbiological findings were avail - export the type of surgery (i.e. the procedure code) from able electronically. In groups B&C, responses were the HIS, in groups B&C, it was 169 departments respec- similar (94% [203 of 216]). Departments that stated that tively. These departments were requested to specify how microbiological results were available electronically, were the allocation from procedure code to the corresponding asked to further specify whether they were available in OP-KISS indicator procedure type was executed. Here, a structured and machine-readable format (e.g. FHIR , 46% (111 of 241) in group A, and 89% (150 of 169) in CSV, HL7 v2.x). Here, 34% (182 of 542) of departments in groups B&C reported that this was performed automati- group A stated that this was the case. In groups B&C, the cally, either as a feature of the export from the HIS, or percentage was considerably higher (60% [121 of 203]). by directly importing the procedure code into webKess. Conversely, 49% (118 of 241) of departments in group webKess import function A and 9% (16 of 169) in groups B&C reported that this When asked whether data from the HIS could be was done manually by staff. The remaining departments exported to an external data management software (e.g. either specified another method or did not provide a ® ® Microsoft Excel ) and/or directly to webKess, 41% (249 response. of 605) of departments in group A replied that this was To learn more about potential hurdles of a direct possible, while 79% (171 of 216) of departments in groups export of HIS data to webKess, participants that reported B&C did so. Further information, including a distinction they could export HIS data to an external data manage- whether export was possible to both an external data ment software but not to webKess directly, were asked management software and webKess, or to only one of which parameters required manual editing before import the two, is provided in Fig. 1. The figure reveals that par - into webKess. The responses are summarized in Table  4 ticularly the possibility to export directly from the HIS to and demonstrate that manual editing is necessary more webKess is lower in group A than groups B&C. frequently in group A than groups B&C. Departments that reported that data export from the Depending on the group that departments were allo- HIS was possible, were additionally asked to specify the cated to, survey questions exploring recent use or non- parameters with significance to SSI surveillance that use of the webKess import feature differed. Departments A ghdassi et al. Antimicrobial Resistance & Infection Control (2023) 12:49 Page 5 of 10 100% 90% 80% 70% 60% 50% 40% 30% 20% 10% 0% Group AGroups B&C Export to both external data management software and webKess Export only to webKess Export only to external data management software No export or unknown Fig. 1 Feasibility of data export from the hospital information system. Responses from 605 German surgical departments in group A and 216 German surgical departments in groups B&C Table 3 Parameters exportable from the hospital information system Parameter Exportable from HIS—Group A Number Exportable from HIS— (percentage) Groups B&C Number (percentage) Type of surgery (procedure code) 241 (96.8) 169 (98.8) Date of surgery 238 (95.6) 169 (98.8) Age (year of birth) 236 (94.8) 169 (98.8) Sex 223 (89.6) 165 (96.5) Date of hospital admission 206 (82.7) 151 (88.3) Date of hospital discharge 209 (83.9) 142 (83.0) ASA score 174 (69.9) 155 (90.6) Wound contamination class 137 (55.0) 141 (82.5) Duration of surgery 205 (82.3) 163 (95.3) Endoscopic (yes or no) 150 (60.2) 131 (76.6) Urgent procedure (yes or no) 121 (48.6) 104 (60.8) Revision surgery (yes or no) 97 (39.0) 100 (58.5) Implant (yes or no) 122 (49.0) 125 (73.1) Surgical site infection data 81 (32.5) 72 (42.1) Premature end of surveillance (due to reoperation or death) 74 (29.7) 79 (46.2) Responses from 249 German surgical departments in group A and 171 German surgical departments in groups B&C that reported that data export from the hospital information system was possible According to the OP-KISS methodology, urgent procedures are surgeries that were not planned 24 h or longer in advance. Abbreviations: ASA: American Society of Anesthesiologists; HIS: hospital information system in group A had to state, why they had not previously used import specifications and HIS (n = 153), and lack of tech- the import function. The primary reasons provided were nical expertise (n = 145). Departments in group B had local IT deficits (n = 236), incompatibility of webKess to state, why they had discontinued using the import Aghdassi et al. Antimicrobial Resistance & Infection Control (2023) 12:49 Page 6 of 10 Table 4 Exportable parameters from the hospital information system requiring manual editing before import into webKess Parameter Requiring manual editing—Group A Number Requiring manual editing— (percentage) Groups B&C Number (percentage) Type of surgery (procedure code) 106 (52.5) 5 (7.7) Date of surgery 63 (31.2) 8 (12.3) Age (year of birth) 65 (32.2) 8 (12.3) Sex 73 (36.1) 8 (12.3) Date of hospital admission 76 (37.6) 3 (4.6) Date of hospital discharge 76 (37.6) 7 (10.8) ASA score 115 (56.9) 20 (30.8) Wound contamination class 134 (66.3) 30 (46.2) Duration of surgery 85 (42.1) 10 (15.4) Endoscopic (yes or no) 102 (50.5) 18 (27.7) Urgent procedure (yes or no) 112 (55.4) 29 (44.6) Revision surgery (yes or no) 123 (60.9) 25 (38.5) Implant (yes or no) 117 (57.9) 29 (44.6) Surgical site infection data 159 (78.7) 55 (84.6) Premature end of surveillance (due to reoperation or 154 (76.2) 39 (60) death) Responses from 202 German surgical departments in group A and 65 German surgical departments in groups B&C that reported export from the hospital information system was possible only to an external data documentation software, but not directly to webKess According to the OP-KISS methodology, urgent procedures are surgeries that were not planned 24 h or longer in advance. Abbreviations: ASA: American Society of Anesthesiologists; HIS: hospital information system function. Lack of technical expertise (n = 4) was the most 42% (253 of 605) of departments in group A, and 32% (69 frequently provided answer, with most other answers of 216) of departments in groups B&C. being provided as free text often citing local structural and process changes. Departments in group C were Discussion asked to state the reasons for utilizing the import func- To the best of our knowledge, this survey provides the tion, with reduction of workload (n = 160) most com- first detailed description of IT infrastructures used by monly reported. Furthermore, departments in group C hospitals for conducting SSI surveillance within a large were asked whether they used the import function, not national surveillance network. To inform a better under- only for denominator data but also for numerator data standing of the international situation regarding this (i.e. data on SSI), with 54% (104 of 193) reportedly doing matter, and to strengthen international cooperation in so. the field of SSI surveillance, we wish to encourage other countries and surveillance networks to conduct similar surveys. Practice of SSI surveillance As was expected, analysis of the survey results revealed When asked to describe the process of SSI surveillance, heterogeneity concerning availability and utilization of IT responses between group A and groups B&C were largely options in the practice of SSI surveillance among German consistent, with review of microbiological findings (95% surgical departments. Although not an outcome param- [573 of 605] in group A, 92% [199 of 216] in groups B&C) eter of the survey itself, this becomes apparent already and actively inquiring updates of treating staff (62% [378 when comparing the number of departments per group of 605] in group A, 72% [155 of 216] in groups B&C) that were invited to participate in the survey. Group A, being the most common regularly (i.e. “frequently”, “very which was defined by manual data entry into webKess, frequently” or “always” selected as response) performed contained more than three times as many departments surveillance strategies in both groups. Moreover, partici- than groups B&C, which had imported denominator pants were asked whether they regularly continued sur- data into webKess in at least one of the two considered veillance after patients were discharged from the hospital years, illustrating that SSI surveillance is still largely a (so-called “post-discharge surveillance”). Post-discharge manual process in Germany. Given that automated HAI surveillance was reportedly performed systematically by surveillance and even automated identification of eligible A ghdassi et al. Antimicrobial Resistance & Infection Control (2023) 12:49 Page 7 of 10 operations (i.e. denominator data) can be a means to save possible, which was substantially lower than for groups much needed resources [15, 17, 18], the observed distri- B&C. It is important however to be mindful, that docu- bution of departments into the respective groups, docu- mented deficits could be to a certain extent be overesti - ments a high unused potential to save IPC resources in mated by the fact that some respondents, due to lack of Germany. This interpretation is supported, when consid - adequate local IT support, might have been unaware of ering that reduction of workload was stated as the pri- data export options that actually existed but were merely mary motivation for using the webKess import feature. not utilized. Both interpretations however, the lack of Departments utilizing the import feature were found to specific IT features or the missed opportunity of using be from larger hospitals and from hospitals with a higher existing features, highlight the importance of consider- level of care than departments relying solely on manual ing surveillance use cases when designing or respectively data entry. This finding seems to corroborate the some - selecting HIS, and the significance of ensuring interoper - what intuitive assumption that tertiary care hospitals and ability between systems linked to the process of HAI sur- larger hospitals in general have more technical options at veillance [21]. their disposal. Interestingly, differences between groups Our survey provides further insights into this matter by with regards to public versus private hospital owner- detailing for individual surveillance parameters, whether ship were small. The fact that the number of procedures data export from HIS was possible, and whether manual transmitted to the NRC for the years 2018 and 2019 was editing of data before webKess import was necessary. It higher in groups B&C suggests that time conventionally is particularly critical that important procedure-related spend on manual entry of denominator data, can be real- variables, such as wound contamination class, surgical located to perform SSI surveillance for a higher number access route (endoscopic vs. open), duration of surgery of procedures. This interpretation is in alignment with and American Society of Anesthesiologists (ASA) score, studies concluding that automating certain aspects of SSI were frequently reported to be not exportable from HIS, surveillance offers potentials to increase the number of or requiring manual editing if they were. This finding is observed procedures [14]. to a certain extent surprising, since wound contamina- Our survey offered valuable insights into the underly - tion class, ASA score and duration of surgery are used ing reasons for the high number of departments still rely- for risk stratification in OP-KISS [22]. Similarly, around ing solely on manual data entry. Only half of departments half of departments in group A reported that the alloca- in group A reported receiving IT support for conduct- tion from procedure code to the corresponding OP-KISS ing surveillance, whereas in groups B&C IT support was indicator procedure was performed manually by staff. available considerably more often (circa 75%). Moreo- According to the OP-KISS methodology, the allocation ver, the fact that only around one third of departments to the correct indicator procedure is a prerequisite to not already receiving IT support, were hopeful to receive collect any useful surveillance data at all [11]. Therefore, support in the future, revealed significant shortcomings the need for a manual process to assign an operation to concerning IT support. This interpretation is reinforced the appropriate indicator procedure must be viewed as a when considering the stated reasons, why the webKess clear potential for improvement. import feature had not been used. IT deficits, technical The results and parameters discussed thus far predomi - incompatibilities and lack of technical expertise were nately pertained to denominator data. However, when seen as the main barriers to data import. Our survey trying to explore potentials to automate SSI case find - therefore highlights the importance of prioritizing inter- ing, other variables should be considered as well. Various professional cooperation and support from dedicated IT variables have been identified to yield particular value for teams, when setting up structures for HAI surveillance. automated SSI surveillance, for instance, microbiological The significance of tailoring local systems to perform sur - findings, hospital admissions, (revision) surgeries, and veillance functions has been discussed in previous publi- antimicrobial prescriptions [14]. To gain insights into this cations [19, 20]. Evidently, local IT support represents a aspect, a question regarding the availability of microbio- prerequisite for this process. logical findings was included in the survey. In both group Our survey uncovered additional factors forcing IPC A and groups B&C, electronic availability of this infor- staff to perform surveillance manually, beyond lack of mation was widespread. While this can be viewed as a IT support and expertise. While the general availability promising potential for automation, differences between of variables for SSI surveillance in HIS was comparable the groups concerning the format and machine-reada- between the different survey groups, pronounced differ - bility of microbiological results, call for a more nuanced ences were noted concerning the option to export data interpretation. Groups B&C were found to have micro- from HIS. Fewer than half of departments in group A biological results available in a structured and machine- reported that exporting surveillance data from HIS was readable format decidedly more often than group A (60% Aghdassi et al. Antimicrobial Resistance & Infection Control (2023) 12:49 Page 8 of 10 vs. 34%). However, even in groups B&C more than a third given the long trust-building history of conducting of departments reported that microbiological results surveys in the KISS network [27–29], we assess this were not available in a structured and machine-reada- risk to be rather low. Forth, certain questions, specifi - ble format, which illustrates that for departments from cally when pertaining to technical aspects, might have both groups harnessing microbiological data for auto- been difficult to understand for some survey recipients, mated surveillance might be challenging. This once again which were typically IPC professionals. To counteract stresses the crucial role of ensuring data standardization this, survey participants were encouraged to seek assis- and meeting interoperability standards in the context of tance from other professional groups (e.g. IT team) HAI surveillance [21]. whenever necessary. Nevertheless, responses indicat- In a separate section of the survey, the practice of SSI ing the non-availability of data or specific features, par - surveillance by participating departments was investi- ticularly pertaining to data export and import, might in gated. Irrespective of the survey group, review of micro- some cases not accurately reflect the actual situation, biological results and active information gathering from but rather a lack of knowledge of the respondent. Last, treating staff, were named as commonly employed sur - if data was entered erroneously, participants could not veillance strategies. As delineated above, interpretation of perform corrections themselves, but had to contact the microbiological results entails a high potential for auto- study team, requiring more effort than simply re-enter - mation. Provided a consistent way of documenting the ing the questionnaire and changing a response. Thus, clinical course of patients after surgery, information gath- the analyzed dataset might have contained incorrect ering from treating staff could be assisted by algorithms responses. However, to reduce this risk to a minimum, searching for key terms in the patient file, thus also rep - participants were advised to print out the survey on resenting a strategy that could be partly automated. This paper, and fill in answers before entering data into the interpretation is reinforced by the fact that over half of online survey template. departments in group C reported having used the web- Kess import function also for numerator data. Similarly, the practice of post-discharge surveillance should be Conclusions considered in future automated surveillance strategies, IT infrastructures play an important part in the prac- given that between one third and one half of departments tice of SSI surveillance in Germany. The degree, to reported performing it systematically. The crucial role which they are harnessed, however, varies considerably of adequate post-discharge surveillance for detecting a between surgical departments. Local IT deficits, tech - substantial portion of SSI has been described in various nical difficulties and general lack of local IT support, publications [23–25]. The Hospital-Acquired Infections were found to hinder the use of existing data import Database (HAIBA) from Denmark represents a prime features. To increase the amount of data exported example of intersectoral data exchange for the purpose of directly from local HIS to the national surveillance continuing HAI surveillance after hospital discharge [26]. database, and therefore lay the foundation for auto- Several limitations have to acknowledged when mated SSI surveillance in Germany, hospitals should interpreting the survey results. First, the survey was seek solutions to improve availability and accessibil- not distributed to a representative sample of surgical ity of information in HIS, and ensure necessary data departments, but to all OP-KISS participants that met standardization as well as adherence to interoperability the inclusion criteria. Consequently, statements con- standards. The results of our survey strongly indicate cerning the national situation have to be made with that hospitals in Germany and their digital subsystems caution. Nevertheless, due to the large number of par- lag far behind contemporary standards. ticipating departments, careful generalizations to the national situation appear to be warranted. Second, the Abbreviations survey was based on voluntary participation. Accord- ASA American society of anesthesiologists ingly, departments with a particular interest in the sur-HAI Healthcare-associated infection(s) HAIBA Hospital-acquired infections database vey topic may be overrepresented, which could distort HIS Hospital information system(s) survey results towards an overestimation of the use of IPC Infection prevention and control IT infrastructures for surveillance. Third, although all IT Information technology KISS Krankenhaus-infektions-surveillance-system data was handled confidentially, some survey questions NRC National reference center might have been perceived as potentially compromis- SSI Surgical site infection(s) ing, which could result in “wishful reporting”. However, A ghdassi et al. Antimicrobial Resistance & Infection Control (2023) 12:49 Page 9 of 10 3. Cassini A, Plachouras D, Eckmanns T, Abu Sin M, Blank HP, Ducomble T, Supplementary Information et al. Burden of six healthcare-associated infections on european popula- The online version contains supplementary material available at https:// doi. tion health: estimating incidence-based disability-adjusted life years org/ 10. 1186/ s13756- 023- 01253-9. through a population prevalence-based modelling study. PLoS Med. 2016;13(10): e1002150. Additional file 1. Translated version of the OP-KISS survey on SSI surveil- 4. Brandt C, Sohr D, Behnke M, Daschner F, Ruden H, Gastmeier P. Reduction lance and digitalization. of surgical site infection rates associated with active surveillance. Infect Control Hosp Epidemiol. 2006;27(12):1347–51. 5. Geubbels EL, Nagelkerke NJ, Mintjes-De Groot AJ, Vandenbroucke- Acknowledgements Grauls CM, Grobbee DE, De Boer AS. Reduced risk of surgical site We wish to thank all hospitals participating in the German national nosoco- infections through surveillance in a network. Int J Qual Health Care. mial infection surveillance system for surgical site infections. Dr Aghdassi is 2006;18(2):127–33. participant in the Charité Digital Clinician Scientist Program funded by the 6. Global guidelines for the prevention of surgical site infection, second DFG, the Charité Universitätsmedizin – Berlin, and the Berlin Institute of Health edition. Geneva: World Health Organization; 2018. Licence: CC BY-NC-SA at Charité (BIH). 3.0 IGO. 7. Guidelines on core components of infection prevention and control Author contributions programmes at the national and acute health care facility level. Geneva: SA, HG and MB formulated the research question of this manuscript. AG, World Health Organization; 2016. Licence: CC BY-NC-SA 3.0 IGO. JC, BP, CG and PG gave valuable input in the process of selecting suitable 8. Schroder C, Schwab F, Behnke M, Breier AC, Maechler F, Piening B, et al. survey questions. AG and JC were in charge of disseminating the survey and Epidemiology of healthcare associated infections in Germany: Nearly 20 managing responses. SA and HG drafted the manuscript with the input of all years of surveillance. Int J Med Microbiol. 2015;305(7):799–806. other authors. All authors agreed to the final version of the manuscript and its 9. Aghdassi SJS, Schwab F, Hoffmann P, Gastmeier P. The association of submission for publication. climatic factors with rates of surgical site infections: 17 years’ data from hospital infection surveillance. Dtsch Arztebl Int. 2019;116(31–32):529–36. Funding 10. Kramer TS, Schroder C, Behnke M, Aghdassi SJ, Geffers C, Gastmeier P, Open Access funding enabled and organized by Projekt DEAL. et al. Decrease of methicillin resistance in Staphylococcus aureus in nosocomial infections in Germany-a prospective analysis over 10 years. J Availability of data and materials Infect. 2019;78(3):215–9. Not applicable, because all data were surveillance-based data which were 11. OP-KISS Protokoll: Surveillance postoperativer Wundinfektionen [OP-KISS obtained in accordance with the German Protection against Infection Act. protocol: surveillance of surgical site infections]. National Reference Center for Surveillance of Nosocomial Infections. 2020. Available from: https:// www. nrz- hygie ne. de/ files/ Proto kolle/ OP- Proto kolle/ Wundi nfekt Declarations ionen/ OP_ KISS_ Proto koll_ WI_ v2020 11. pdf. Accessed: 3 Feb 2023. 12. Mitchell BG, Hall L, Halton K, MacBeth D, Gardner A. Time spent by infec- Ethics approval and consent to participate tion control professionals undertaking healthcare associated infection Not applicable, because all data were surveillance-based data which were surveillance: a multi-centred cross sectional study. Infection, Disease & obtained in accordance with the German Protection against Infection Act. Health. 2016;21(1):36–40. 13. Stricof RL, Schabses KA, Tserenpuntsag B. Infection control resources in Consent for publication New York State hospitals, 2007. Am J Infect Control. 2008;36(10):702–5. Not applicable, because all data were surveillance-based data which were 14. Sips ME, Bonten MJM, van Mourik MSM. Semiautomated surveillance of obtained in accordance with the German Protection against Infection Act. deep surgical site infections after primary total hip or knee arthroplasty. Infect Control Hosp Epidemiol. 2017;38(6):732–5. Competing interests 15. van Mourik MSM, van Rooden SM, Abbas M, Aspevall O, Astagneau P, The authors declare that they have no competing interests. Bonten MJM, et al. PRAISE: providing a roadmap for automated infection surveillance in Europe. Clin Microbiol Infect. 2021;27:S3–19. Author details 16. Verberk JDM, van Rooden SM, Koek MBG, Hetem DJ, Smilde AE, Bril WS, Charité – Universitätsmedizin Berlin, corporate member of Freie Universität et al. Validation of an algorithm for semiautomated surveillance to detect Berlin and Humboldt-Universität zu Berlin, Institute of Hygiene and Environ- deep surgical site infections after primary total hip or knee arthroplasty-A mental Medicine, Hindenburgdamm 27, 12203 Berlin, Germany. National multicenter study. Infect Control Hosp Epidemiol. 2021;42(1):69–74. Reference Center for Surveillance of Nosocomial Infections, Hindenburgdamm 17. Verberk JDM, Aghdassi SJS, Abbas M, Naucler P, Gubbels S, Maldonado 27, 12203 Berlin, Germany. Berlin Institute of Health at Charité – Universitäts- N, et al. Automated surveillance systems for healthcare-associated medizin Berlin, BIH Biomedical Innovation Academy, BIH Charité Digital Clini- infections: results from a European survey and experiences from real-life cian Scientist Program, Anna-Louisa-Karsch-Straße 2, 10178 Berlin, Germany. utilization. J Hosp Infect. 2022;122:35–43. 18. van Mourik MS, Troelstra A, van Solinge WW, Moons KG, Bonten MJ. Auto- Received: 21 February 2023 Accepted: 11 May 2023 mated surveillance for healthcare-associated infections: opportunities for improvement. Clin Infect Dis. 2013;57(1):85–93. 19. de Bruin JS, Seeling W, Schuh C. Data use and effectiveness in electronic surveillance of healthcare associated infections in the 21st century: a systematic review. J Am Med Inform Assoc. 2014;21(5):942–51. References 20. Woeltje KF, Lin MY, Klompas M, Wright MO, Zuccotti G, Trick WE. Data 1. Behnke M, Aghdassi SJ, Hansen S, Diaz LAP, Gastmeier P, Piening B. The requirements for electronic surveillance of healthcare-associated infec- prevalence of nosocomial infection and antibiotic use in german hospi- tions. Infect Control Hosp Epidemiol. 2014;35(9):1083–91. tals. Dtsch Arztebl Int. 2017;114(50):851–7. 21. Behnke M, Valik JK, Gubbels S, Teixeira D, Kristensen B, Abbas M, et al. 2. Suetens C, Latour K, Karki T, Ricchizzi E, Kinross P, Moro ML, et al. Preva- Information technology aspects of large-scale implementation of auto- lence of healthcare-associated infections, estimated incidence and mated surveillance of healthcare-associated infections. Clin Microbiol composite antimicrobial resistance index in acute care hospitals and Infect. 2021;27(Suppl 1):S29–39. long-term care facilities: results from two European point prevalence 22. Brandt C, Hansen S, Sohr D, Daschner F, Ruden H, Gastmeier P. Finding surveys, 2016 to 2017. Euro Surveill. 2018;23(46):1800516. a method for optimizing risk adjustment when comparing surgical-site infection rates. Infect Control Hosp Epidemiol. 2004;25(4):313–8. Aghdassi et al. Antimicrobial Resistance & Infection Control (2023) 12:49 Page 10 of 10 23. Koek MB, Wille JC, Isken MR, Voss A, van Benthem BH. Post-discharge surveillance (PDS) for surgical site infections: a good method is more important than a long duration. Euro Surveill. 2015;20(8):21042. 24. Lower HL, Dale H, Eriksen HM, Aavitsland P, Skjeldestad FE. Surgical site infections after hip arthroplasty in Norway, 2005–2011: influence of duration and intensity of postdischarge surveillance. Am J Infect Control. 2015;43(4):323–8. 25. Mannien J, Wille JC, Snoeren RL, van den Hof S. Impact of postdischarge surveillance on surgical site infection rates for several surgical procedures: results from the nosocomial surveillance network in The Netherlands. Infect Control Hosp Epidemiol. 2006;27(8):809–16. 26. Gubbels S, Nielsen J, Voldstedlund M, Kristensen B, Schønheyder HC, Ellermann-Eriksen S, et al. National automated surveillance of hospital- acquired bacteremia in denmark using a computer algorithm. Infect Control Hosp Epidemiol. 2017;38(5):559–66. 27. Aghdassi SJS, Geffers C, Behnke M, Gropmann A, Gastmeier P, Kramer TS. Management of peripheral venous catheters and implementa- tion of guidelines in Germany: a national survey. J Hosp Infect. 2020;105(2):311–8. 28. Aghdassi SJS, Hansen S, Bischoff P, Behnke M, Gastmeier P. A national survey on the implementation of key infection prevention and control structures in German hospitals: results from 736 hospitals conducting the WHO infection prevention and control assessment framework (IPCAF). Antimicrob Resist Infect Control. 2019;8:73. 29. Stiller A, Schroder C, Gropmann A, Schwab F, Behnke M, Geffers C, et al. ICU ward design and nosocomial infection rates: a cross-sectional study in Germany. J Hosp Infect. 2017;95(1):71–5. Publisher’s Note Springer Nature remains neutral with regard to jurisdictional claims in pub- lished maps and institutional affiliations. Re Read ady y to to submit y submit your our re researc search h ? Choose BMC and benefit fr ? Choose BMC and benefit from om: : fast, convenient online submission thorough peer review by experienced researchers in your field rapid publication on acceptance support for research data, including large and complex data types • gold Open Access which fosters wider collaboration and increased citations maximum visibility for your research: over 100M website views per year At BMC, research is always in progress. Learn more biomedcentral.com/submissions

Journal

Antimicrobial Resistance and Infection ControlSpringer Journals

Published: May 19, 2023

Keywords: Automation; Digitalization; Surveillance; Surgical site infection; Healthcare-associated infection; Digital infection control

There are no references for this article.