Computerized Clinical Decision Support Systems in Healthcare - Pros, Cons, and Liabilities - Literature review Example

Running head: Computerized Clinical Decision Support Systems in Healthcare: Pros, Cons & Liabilities Computerized Clinical Decision Support Systems in Healthcare: Pros, Cons & Liabilities Name: Date: Course: Institution: Abstract The health care industry has come a long way in terms of delivering essential medical services and facilities to the communities they serve. Technology and innovation are the driving forces shaping the quality of health care services today. Medical practitioners are effectively using technology based tools and applications to treat patients and provide them with high quality health care services. While computerized decision aids such as health information systems have made their presence felt in the industry and such systems are widely adopted by clinics and hospitals, there are significant barriers facing the optimal use of these systems. The paper provides a deeper insight into the reasons behind the low adoption rates of such systems and based on the observations recommends a focused approach to simplifying the systems for effective use. Introduction The health care industry has adopted new and innovative means of delivering efficient and effective services through computerized clinical decision support systems. The development in the field of medical technology has enabled health care service providers to track and maintain vital patient information. Decision support systems have been developed to review and evaluate patient symptoms, treatment alternatives and patient health information. The focus of governing bodies in the recent times have been on accelerating the pace of adopting Clinical Decision Support Systems (CDSS) for efficient delivery of health care services. CDSS provides “clinicians, staff, patients and other individuals with knowledge and specific, individualized information, intelligently filtered and presented at appropriate times, to enhance clinical performance and patient outcomes” (Zheng, 2010, p2). The tools enable the physicians to access vital diagnostic information, clinical guidelines, and patient information for effective health care practices. A number of research studies have highlighted the benefits of such systems and how they can contribute to deliver high quality health care services to communities (Ridgely and Greenberg, 2012). However, the optimized use of such systems have been limited in terms of application and effective implementation owing to the complicated nature of the systems and the high costs involved in development. The study of CDSS technologies and its implementation challenges is interesting as it provides deeper insights into the limitations and potentials of such systems and how it can contribute to effective health care operations. Numerous research studies have been undertaken over the years to explain and understand the implementation issues related to CDSS technologies and how it can be effectively applied for maximized benefits to the community. The subsequent sections explore the challenges, scope and limitations of CDSS in healthcare industry through an analysis of available literature on the topic. The paper uses various online sources, journals and publications on CDSS to understand the implications of the system and its effective usage across the healthcare industry. CDSS – a historical overview CDSS technology tools and applications face increased demand today in the face of growing pressure to improve the quality of health care services and cater to evolving health care needs of physicians and patients. The computer-based information processing tool provides a flexible and interactive system that supports efficient clinical management and effective decision making (Zheng, 2010). Information technology (IT) has been widely recognized as an efficient mechanism of improving healthcare services and providing a cost-effective platform for delivering high quality healthcare (Lee, 2012). The technology is capable of generating useful information related to drug allergies, long-term drug effects, interactions between different drugs prescribed to patients and its health implications. The first CDSS was conceptualized in the 1960s when the role of IT in assisting doctors through an effective decision making tool was widely appreciated. Artificial intelligence and extensive knowledge processing capabilities of computers provided new perspectives to healthcare services in future. One of the first tools DXplain designed at the Massachusetts General Hospital in the year 1984 provided the physicians with a list of possible diagnoses based on patient symptoms and conditions. Similar tools and applications were produced during this period to support clinical decision making process. While some of these tools were regarded helpful in hospitals, there were several limitations that restricted the adoption of these technologies in regular practice (Zheng, 2010). The primary limitations were the stand-alone applications that did not facilitate integration between hospital operation and patient information systems. This restricted the scope and benefits of the system tools. Another distinctive limitation was individual perception of doctors who felt that such systems could not compensate real-time clinical decisions that were based on more factual understanding and experienced opinions (Zheng, 2010). The present era of CDSS provide a more integrative approach to clinical management and a basic necessity in any healthcare centre. The new age systems provide a complete database management system that supports “evidence-adaptive decision support” (Zheng, 2010, p4). Such efficient knowledge based systems enable the physicians to make prompt and accurate decisions derived from extensive research and practice. CDSS – adoption limitation and practice Pezzo and Pezzo (2006) in their works on the effectiveness of CDSS in healthcare concluded that the use of these systems can help in reducing medical errors but predicts a low patient satisfaction score for positive outcomes. The research study involved experimental studies conducted on medical students and undergraduates to rate doctor’s decision and quality of medical aid received using computerized decision aids and personal opinion of the doctors. The findings claimed that using decision aids provided low scores in case of positive outcomes and relatively less negative scores in case of negative outcomes. These findings indicate that people in general have low confidence levels on computerized decision aids and hence there is a preference for personal opinion of doctors based on their observations and experience. Even after years of clinical trials and application, the medical industry is yet to receive a highly intelligent system that can assist doctors and physicians in treatment process. This is largely on account of the fact that “enormous variations in patient care cannot be reduced to systematic decision making to render qualitative medical treatments” (Zheng, 2010). Moreover, patient care decisions and treatment options are to a wide extent guided by economic, psychological, and social conditions that influence final decisions. The new generation CDSS tools and applications focus on capturing and storing patient information. The extensive data stored has created new challenges on the ability of the doctors to process these data and derive useful information. Zheng (2010) in his study on barriers to the adoption of CDSS observed that the key factor limiting the potential application and usage of such high technology based tools was the extra time required to enter, codify patient data and evaluate decision prompts. The normal outpatient time for each patient is approximately 20 minutes and a large percentage of this time is spent in codifying and entering patient details. This reduces the amount of time given by doctors to personal interaction with the patients. Moreover, increased focus on the system leads to lapses in communication between the doctor and the patient leaving the patient dissatisfied with the amount of time given to his problems. Another important point raised by Zheng (2010) in his analysis on barriers to CDSS implementation was that of system interoperability. This pertains to the fact that CDSS is unable to generate accurate case specific advice unless it has access to all the relevant patient details. Systems are not designed or developed to meet the case specific requirements of each patient and interoperability issues limit the potential benefits of the system. Interoperable systems are not only complex in terms of functions but also require huge investments. Very few health providers are able to invest such huge amounts in deploying and maintaining a high-end system. A recent research study by Ridgely and Greenberg (2012) concludes that most CDSS tools used by health practitioners “overwhelm physicians with large numbers of clinically insignificant drug-drug interaction alerts” (p258). This causes most physicians to turn off the alerts that eventually limit the potential benefits of the whole system. One of the main reasons behind the low effectiveness in adoption of CDSS is thus, the physician mindset that prevents them from utilizing this system for health care. A study by Poon, Blumenthal, Jaggi, Honour, Bates and Kaushal (2004) surveyed 52 medical officers to find out the primary barriers to the effective implementation of computerized decision aids in healthcare. The findings of the survey revealed that the systems slowed down their work flow and needed extra time to process the information. They found the conventional paper based system more comfortable and less time consuming. Another study by Varonen, Kortteisto, and Kaila (2008) identified prior experience of dysfunctional systems, extra workload and the risk of losing patient confidence as the key barriers to the effective deployment of CDSS. The study concluded that while the physicians recognized the potential benefits of the systems and the role it can play in improving the quality of health care services, the systems often lacked flexibility and accuracy in terms of providing reliable decision aids. What is required is a more flexible and friendly interface that can ease the work of entering patient data and accessing accurate treatment alternatives. Koppel and Kreda (2009) highlight yet another concern that prevents the health care community from adopting CDSS in their regular practice. This relates to the legal and contractual terms that limit the liability of vendors of defective software by blaming the learned intermediaries who provide the knowledge base for the systems. “According to this doctrine and legal language, healthcare information technology vendors are not responsible for errors their systems introduce in patient treatment, because physicians, nurses, pharmacists, and health care technicians should be able to identify – and correct – any errors generated by software faults” (Koppel and Kreda, 2009). Hence the health care community is held responsible for faulty software or errors made while using these decision aids. The study also highlights the fact that complex knowledge management systems in healthcare are liable to errors in terms of calculations and this can lead to fatal errors. Observations and conclusion The review of existing literature and research studies on CDSS in the previous sections have highlighted some of the key aspects involved in the adoption and implementation challenges of CDSS. These can be broadly categorized into technical, socio-cultural, and legal perspectives. The technical perspective includes complexities of the system usage, interoperability issues, and interface design that prevent effective usage of the system. The socio-cultural perspective involve the barriers related to patient perception and thought process that does not regard the technical decision aids highly and holds the conventional interactive sessions with their physicians in high esteem. This perspective also prevents the physicians from using the system to its optimum capabilities. The legal perspective limits vendor liability when errors are noted in the systems. CDSS has enormous potentials in terms of its contribution to processing extensive and complex healthcare information and provide effective treatment solutions. The decision aids may not compensate physician knowledge, experience and judgment of patient situations but it does help in identifying some vital aspects of treatment including drug-drug interactions and its possible impacts on the patient health. However, as evident from the previous sections, the systems are complex and require a high degree of physician involvement in order to be effective. The implementation barriers are significant in understanding the realistic view of such tools and its application in healthcare institutions. The focus should be on designing simple and easy to understand interfaces that enable the users to skip the complicated information processing steps and avail the knowledge base facilities supported by the system. The interface design and development of such extensive knowledge based systems are vital aspects that define the effective usage of the systems (Ash, Sittig, Guappone, Dykstra, Richardson, Wright, Carpenter, McMullen, Shapiro, Bunce and Middleton, 2012). It is also important to note that such systems are based on extensive information processing capabilities that have widespread implications. Drugs and their counter effects, possible treatment options, and individual patient information are all considered important parameters in final decision making. The complexities of such systems and their serious implications on community healthcare have its risks and hence the focus should be on developing systems that aid medical practitioners by providing them with readily accessible information. “The essential people who will customize, implement, manage and support CDSS efforts are key to national efforts and meaningful use of health information technology” (Ash et al., 2012). References Ash, J.S., Sittig, D.F., Guappone, K.P., Dykstra, R.H., Richardson, J., Wright, A., Carpenter, J., McMullen, C., Shapiro, M., Bunce, A., and Middleton, B. (2012). Recommended practices for computerized clinical decision support and knowledge management in community settings: a qualitative study. BMC Medical Informatics and Decision Making. Vol 12, No. 6. Koppel, R., and Kreda, D. (2009). Health care information technology vendors “hold harmless” clause – implications for patients and clinicians. The Journal of American Medical Association. Vol 301, No. 12, pp 1276-1278. Lee, D. and Rutsohn, P. (2012). Racial Differences in the Usage of Information Technology: Evidence from a National Physician Survey. Perspectives in Health Information Management (Summer 2012): 1-11. Pezzo, M.V. and Pezzo, S.P. (2006). Physician evaluation after medical errors: does having a computer decision aid help or hurt in hindsight? Medical Decision Making. Vol 26, No.1, pp48-56. Poon, E.G., Blumenthal, D., Jaggi, T., Honour, M.M., Bates, D.W., and Kaushal, R. (2004). Overcoming barriers to adopting and implementing computerized physician order entry systems in US hospitals. Health Affairs. Vol 23, No. 4, pp 184-190. Ridgely, M.S., and Greenberg, M.D. (2012). Too many alerts, too much liability: sorting through the malpractice implications of drug-drug interaction clinical decision support. Saint Louis University Journal of Health Law & Policy. Vol 5, pp 257-296. Varonen, H., Kortteisto, T., and Kaila, M. (2008). What may help or hinder the implementation of computerized decision support systems (CDSSs): a focus group study with physicians. Family Practice. Vol 25, No.3., pp 162-167. Zheng, K. (2010). Clinical decision support systems. Encyclopedia of Library and Information Sciences. 3rd ed. Taylor and Francis. Read More