current system, switch to the cdss, input the necessary data (even if it had already been inputted into another system), and examine the results produced. the additional steps break the flow from the clinician's perspective and cost precious time. technical challenges and barriers to implementation clinical decision support systems face steep technical challenges in a number of areas. biological systems are profoundly complicated, and a clinical decision may utilise an enormous range of potentially relevant data. for example, an electronic evidence-based medicine system may potentially consider a patient's symptoms, medical history, family history and genetics, as well as historical and geographical trends of disease occurrence, and published clinical data on therapeutic effectiveness when recommending a patient's course of treatment. clinically, a large deterrent to cdss acceptance is workflow integration. while it has been shown that clinicians require explanations of machine learning-based cdss, in order to able to understand and trust their suggestions, there is an overall distinct lack of application of explainable artificial intelligence in the context of cdss, thus adding another barrier to the adoption of these systems. another source of contention with many medical support systems is that they produce a massive number of alerts. when systems produce a high volume of warnings (especially those that do not require escalation), besides the annoyance, clinicians may pay less attention to warnings, causing potentially critical alerts to be missed. this phenomenon is called alert fatigue. maintenance one of the core challenges facing cdss is difficulty in incorporating the extensive quantity of clinical research being published on an ongoing basis. in a given year, tens of thousands of clinical trials are published. currently, each one of these studies must be manually read, evaluated for scientific legitimacy, and incorporated into the cdss in an accurate way. in 2004, it was stated that the process of gathering clinical data and medical knowledge and putting them into a form that computers can manipulate to assist in clinical decision-support is "still in its infancy". nevertheless, it is more feasible for a business to do this centrally, even if incompletely, than for each doctor to try to keep up with all the research being published. in addition to being laborious, integration of new data can sometimes be difficult to quantify or incorporate into the existing decision support schema, particularly in instances where different clinical papers may appear conflicting. properly resolving these sorts of discrepancies is often the subject of clinical papers itself (see meta-analysis), which often take months to complete. evaluation in order for a cdss to offer value, it must demonstrably improve clinical workflow or outcome. evaluation of cdss quantifies its value to improve a system's quality and measure its effectiveness. because different cdsss serve different purposes, no generic metric applies to all such systems; however, attributes such as consistency (with and with experts) often apply across a wide spectrum of systems. the evaluation benchmark for a cdss depends on the system's goal: for example, a diagnostic decision support system may be rated based upon the consistency and accuracy of its classification of disease (as compared to physicians or other decision support systems). an evidence-based medicine system might be rated based upon a high incidence of patient improvement or higher financial reimbursement for care providers. combining with electronic health records implementing ehrs was an inevitable challenge. this challenge is because it is a relatively uncharted area, and there are many issues and complications during the implementation phase of an ehr. this can be seen in the numerous studies that have been undertaken. however, challenges in implementing electronic health records (ehrs) have received some attention. still, less is known about transitioning from legacy ehrs to newer systems. ehrs are a way to capture and utilise real-time data to provide high-quality patient care, ensuring efficiency and effective use of time and resources. incorporating ehr and cdss together into the process of medicine has the potential to change the way medicine has been taught and practiced. it has been said that "the highest level of ehr is a cdss". since "clinical decision support systems (cdss) are computer systems designed to impact clinician decision making about individual patients at the point in time that these decisions are made", it is clear that it would be beneficial to have a fully integrated cdss and ehr. even though the benefits can be seen, fully implementing a cdss integrated with an ehr has historically required significant planning by the healthcare facility/organisation for the cdss to be successful and effective. the success and effectiveness can be measured by the increased patient care being delivered and reduced adverse events occurring. in addition, there would be a saving of time and resources and benefits in terms of autonomy and financial benefits to the healthcare facility/organisation. benefits of cdss combined with ehr a successful cdss/ehr integration will allow the provision of best practice, high-quality care to the patient, which is the ultimate goal of healthcare. errors have always occurred in healthcare, so trying to minimise them as much as possible is important to provide quality patient care. three areas that can be addressed with the implementation of cdss and electronic health records (ehrs), are: medication prescription errors adverse drug events other medical errors cdsss will be most beneficial in the future when healthcare facilities are "100% electronic" in terms of real-time patient information, thus simplifying the number of modifications that have to occur to ensure that all the systems are up to date with each other. the measurable benefits of clinical decision support systems on physician performance and patient outcomes remain the subject of ongoing research. barriers implementing electronic health records (ehr) in healthcare settings incurs challenges; none more important than maintaining efficiency and safety during rollout, but in order for the implementation process to be effective, an understanding of the ehr users' perspectives is key to the success of ehr implementation projects. in addition to this, adoption needs to be actively fostered through a bottom-up, clinical-needs-first approach. the same can be said for cdss. as of 2007, the main areas of concern with moving into a fully integrated ehr/cdss system have been: privacy confidentiality user-friendliness document accuracy and completeness integration uniformity acceptance alert desensitisation as well as the key aspects of data entry that need to be addressed when implementing a cdss to avoid potential adverse events from occurring. these aspects include whether: correct data is being used all the data has been entered into the system current best practice is being followed the data is evidence-based a service oriented architecture has been proposed as a technical means to address some of these barriers. status in australia as of july 2015, the planned transition to ehrs in australia is facing difficulties. most healthcare facilities are still running completely paper-based systems; some are in a transition phase of scanned ehrs or moving towards such a transition phase. victoria has attempted to implement ehr across the state with its healthsmart program, but it has cancelled the project due to unexpectedly high costs. south australia (sa) however is slightly more successful than victoria in the implementation of an ehr. this may be because all public healthcare organisations in sa are centrally run. (however, on the other hand, the uk's national health service is also centrally administered, and its national programme for it in the 2000s, which included ehrs in its remit, was an expensive disaster.) sa is in the process of implementing "enterprise patient administration system (epas)". this system is the foundation for all public hospitals and health care sites for an ehr within sa, and it was expected that by the end of 2014, all facilities in sa will be connected to it. this would allow for successful integration of cdss into sa and increase the benefits of the ehr. by july 2015 it was reported that only 3 out of 75 health care facilities implemented epas. with the largest health system in the country and a federated rather than a centrally administered model, new south wales is making consistent progress towards statewide implementation of ehrs. the current iteration of the state's technology, emr2, includes cdss features such as a sepsis pathway for identifying at-risk patients based upon data input to the electronic record. as of june 2016, 93 of 194 sites in-scope for the initial roll-out had implemented emr2 status in finland the ebmeds clinical decision support service provided by duodecim medical publications ltd is used by more than 60% of finnish public health care doctors. research prescription errors a study in the uk tested the salford medication safety dashboard (smash), a web-based cdss application to help gps and pharmacists find people in their electronic health records who might face safety hazards due to prescription errors. the dashboard was successfully used in identifying and helping patients with already registered unsafe prescriptions and later it helped monitoring new cases as they appeared.