Journal of Medical Systems (2019) 43: 41 https://doi.org/10.1007/s10916-019-1162-3 SYSTEMS-LEVEL QUALITY IMPROVEMENT The Impact of Perioperative Data Science in Hospital Knowledge Management Márcia Baptista1 & José Braga Vasconcelos2,3 & Álvaro Rocha4 & Rita Silva5 & João Vidal Carvalho6 & Helena Gonçalves Jardim7 & António Quintal8 Received: 10 August 2018 /Accepted: 8 January 2019 /Published online: 12 January 2019 # Springer Science+Business Media, LLC, part of Springer Nature 2019 Abstract Conservative practices, such as manual registry have limited scope regarding preoperative, intraoperative and post- operative decision making, knowledge discovery, analytical techniques and knowledge integration into patient care. To maximize quality and value, perioperative care is changing through new technological developments. In this context, knowledge management practices will enable future transformation and enhancements in healthcare services. By performing a data science and knowledge management research in the perioperative department at Hospital Dr. Nélio Mendonça between 2013 and 2015, this paper describes its principal results. This study showed perioperative decision-making improvement by integrating data science tools on the perioperative electronic system (PES). Before the PES implementation only 1,2% of the nurses registered the preoperative visit and after 87,6% registered it. Regarding the patient features it was possible to assess anxiety and pain levels. A future conceptual model for perioperative decision support systems grounded on data science should be considered as a knowledge management tool. Keywords Perioperative data science . Knowledge management . Clinical decision support systems . Hospital information systems This article is part of the Topical Collection on Systems-Level Quality Improvement * Márcia Baptista 1 Information Technology Research Department, Santiago Compostela [email protected] University, Santiago, Spain 2 Knowledge Management and Engineering Research Group, José Braga Vasconcelos Universidade Atlântica, Barcarena, Portugal [email protected] 3 Centro de Administração e Políticas Públicas (CAPP) da Álvaro Rocha Universidade de Lisboa, Lisboa, Portugal [email protected] 4 Departamento de Engenharia Informática, Universidade de Coimbra, Rita Silva Coimbra, Portugal [email protected] 5 Bloco Operatório, Hospital Dr. Nélio Mendonça, Madeira, Portugal João Vidal Carvalho [email protected] 6 Politécnico do Porto, ISCAP, CEOS.PP, S. Mamede de Infesta, Portugal Helena Gonçalves Jardim [email protected] 7 Health Higher School, Madeira University and The Health Sciences Research Unit: Nursing, Coimbra, Portugal António Quintal [email protected] 8 Universidade da Madeira, Madeira, Portugal 41 Page 2 of 7 J Med Syst (2019) 43: 41 Introduction & Design, construct and implement a Perioperative Electronic System at Doctor Nélio Mendonça Hospital; Context & Define a research approach and conceptual model for peri- operative decision support systems grounded on data sci- Population aging is an unquestionable reality and a growing ence and knowledge management (KM); phenomenon pointing to a large increase in the elderly popu- & Assess new patient features and create new clinical knowl- lation. In 2013 life expectancy was 80 years. Old population in edge for healthcare decision making before and after the the United States will result in a significant increase of surgical Perioperative Electronic System implementation; services and in Portugal the trend will be identical. On the & Produce perioperative nursing production and quality other hand, most health systems collect at least some individ- indicators. ual patient data during clinical face-to-face encounters. While the conventional way to collect such data are on paper forms and register books, increasingly, face-to-face encounters are Contributions being captured electronically. Electronic health systems have the potential to change the health care system from a mostly This study is a relevant step for perioperative care towards a paper-based production to one that utilizes clinical and other perioperative decision support system (PDSS). The theoretical pieces of information to assist care providers in delivering background is based on a conceptual model using data science more quality to their patients [1]. Keeping these data person- and knowledge management practices. This framework in- alized rather than anonymous is facilitated by using electronic tends to deal with the complexity and scope of the current records and hospital information systems which can more eas- perioperative demands, such as infections and adverse events ily store, access, analyse, and share data. This trend will con- reduction, information added to inform healthcare profes- tinue as improvements are made in computer hardware, soft- sionals, decisions, guidelines, best practices, policy, costs ware, and telecommunication infrastructure and as countries and training, thereby improving the safety and quality of develop the skills necessary to implement electronic data stor- healthcare and its value. age and transmission systems [2]. Motivation and rationale Background and literature review Perioperative care is emerging rapidly as technology advances Perioperative electronic system as a hospital and increases complexity to maximize quality, healthcare val- information system ue, patient and professional security. While innovations in diagnostic and therapeutic technologies have driven past im- Modern hospital information systems (HIS) are comprehen- provements in quality of perioperative care, future transforma- sive, integrated and specialized information systems designed tion in care will be enabled by data [3]. to manage the administrative, financial and clinical aspects of The perioperative background is a unique environment that hospitals and healthcare facilities. They are considered one of includes many challenging variables: complex clinical care the most important focal points on which the delivery of performed by teams, high cost, sophisticated technologies that healthcare within hospitals and different types of medical in- often do not interoperate and a large array of supplies, instru- stitutions depends [5]. Despite evidence of these benefits, phy- ments, and implants that are difficult to manage. These vari- sicians’ and hospitals’ utilization of HIS and electronic health ables create a setting of massive complexity and are a source records are still low [5]. of a significant percentage of patient safety-related adverse Perioperative procedures and its different stages are often events [4]. the most intensive and efficacious therapeutic interventions Conventional methodologies, such as registry studies, are available in medicine and these treatments are frequently cu- limited in their scope for discovery and research, extent and rative and are sometimes the only alternative for patients. complexity of data, breadth of analytic techniques, and trans- Surgeons and other professionals are highly trained thus the lation or integration of research findings into patient care [3]. technology and resources that they employ are typically ad- vanced, expensive and scarce. So, the optimization of these Objectives limited resources is paramount to the safe, effective, and effi- cient delivery of healthcare [6]. The purpose of this research project was to assess the impact Perioperative information technology has the potential to of integrated perioperative data science tools in hospital improve the quality of health care, reduce costs, decrease med- knowledge management. The main objectives were: ication administration errors, reduce time spent on paperwork, J Med Syst (2019) 43: 41 Page 3 of 7 41 increase management efficacy and allow affordable access to Perioperative data science process is to create tools to measure, health care. model and quantify the pathways or processes within the con- According to Fig. 1 it should render all the preoperative text of patient health states or outcomes, and use information stages and process. Researchers concluded that IT or automa- gained to inform healthcare decisions. Data is pervasive tion of aspects of the surgical patient preparation process and throughout the surgical care pathway; thus, data science can the coordination and management of surgical equipment has impact various aspects of care including prevention, diagnosis, the potential to increase the speed of information exchange, intervention, or post-operative recovery. A data science ap- reduce interruptions to clinicians and decrease the possibility proach to surgical decision-making could more accurately pre- of adverse events in the perioperative setting. With migration dict severe complications using complex data from pre-, intra-, to the use of an electronic health record in the operating room and post-operative contexts, how it could support intra- (OR), time that nurses previously spent on paperwork and operative decision-making using both existing knowledge and administrative functions can be dedicated to providing better continuous data streams throughout the surgical care pathway, patient care and ensuring accuracy in documentation [7, 13]. and how it could enable effective collaboration between human care providers and intelligent technologies [3]. Data science in healthcare The confluence of science, technology, and medicine in our dynamic digital era has spawned new data applications to The emergence of perioperative data science