Introduction
Decision-making in the nursing field is essential for finding solutions to problems affecting healthcare service provision. The decisions should be appropriate, relevant, and timely to prevent the escalation of the existing problem, thus streamlining the activities of the healthcare organization. The development of information technology has played a pivotal role in fostering efficient management of nursing processes through computerized systems. In this case, decision support systems (DSSs) have emerged in the nursing field to enable clinicians improve their decision-making strategies (McCartney, 2007).
A DSS can be described as an application that facilitates the analysis of data to assist healthcare providers in making effective clinical decisions. DSS extends the functionality of electronic health record systems to integrate the decision-making feature of clinical practice. DXplain is one of the decision support systems that are used in the nursing field to assist in the explanation of clinical manifestation of various diseases (Astillo & Kelemen, 2013). This paper will explore the different aspects of DXplain as a DSS that enhances the decision-making efficiency in the nursing sector regarding its use at the Massachusetts General Hospital (MGH).
Description of the DSS
DXplain is a developing IT-based diagnostic DSS that accepts clinical-based manifestations to facilitate the proposition of diagnostic hypotheses essential for decision-making. The program aims at explaining and justifying its interpretations by enduring in the provision of access to a knowledge base that regards the differential diagnosis of the signs and symptoms. The system integrates the attributes of a medical reference and electronic medical systems. Therefore, the capability of DXplain to accept an array of clinical findings from the symptoms, signs, and laboratory data facilitates its production of a list of ranked diagnoses that are associated with the manifestations. Therefore, clinicians can make informed decisions from the results of the DSS to enhance the diagnosis and treatment process.
The problem regarding making informed decisions for the diagnosis of disorders at the Massachusetts General Hospital triggered the adoption of the DXplain to facilitate quality health care provision. Clinicians in the hospital faced challenges in making timely decisions on which kind of treatment to administer from the diagnosis process since some disorders portray similar signs and symptoms. Therefore, the need to enhance the decision-making process regarding treatment of the diagnosed disease was overarching.
The rationale for choosing DXplain to solve the problem
The use of DXplain to reduce errors in the diagnosis and treatment of various diseases at MGH was based on various reasons that could be supported by the DSS. Based on its development that spans for over 30 years since its creation in 1984, the DXplain could detect approximately 500 diseases. Currently, the DSS facilitates the diagnosis of over 2400 diseases courtesy of the development of the Internet. Therefore, the MGH found reliability in the functionality of the DSS due to its capability to differentiate over 2400 diseases from the data provided (Massachusetts General Hospital, 2015).
Additionally, DXplain is easy to use since the interface requires common clinical vocabulary for the desired results. Therefore, clinicians at the health institution could easily make decisions in real time due to the user-friendliness aspect of the DSS. Furthermore, the diagnoses results are usually accurate and comprehensive implying that issues of errors and omissions could be mitigated in the nursing practice due to the precise interpretations (Massachusetts General Hospital, 2015).
Functional components and features of DXplain
Coincidentally, the MGH is the developer of the DXplain DSS through its Laboratory of Computer Science department. The vendor facilitates the availability of the DSS through their website, which can be accessed after payment and signing of the annual licensing agreement before it can be downloaded. The major components of DXplain are featured by the medical reference system and the electronic medical records that can be accessed on the PC.
The medical textbook component of the DSS executes the functions of the provision of over 2400 varied diseases, with emphasis on the signs and symptoms that exist in each disease. The etiological, pathological, and prognosis aspects of the disease are also provided by the medical component of DXplain. The medical reference system facilitates the provision of at least ten references to each disorder, chosen to emphasize clinical reviews. The architecture of DXplain uses Common Gateway Interface programs that run on PCs with Windows OS. The architecture integrates two function calls that include TCP/IP and Hypertext Transfer Protocol (HTTP) (Massachusetts General Hospital Laboratory of Computer Science, 2015).
How DXplain supports decision-making in advanced nursing practice
The use of DXplain has been essential in the nursing field since it has enhanced decision -making, thus contributing to advancements in the profession. DXplain has fostered the output of clinicians by facilitating the realization of positive results after correct diagnosis and treatment. Faster decision-making is attained through the application of DXplain whereby over 5000 manifestations can be discerned for accurate diagnosis in a short time.
Advanced nursing practice values the significance of collaborative communication, which is enhanced by the components of DXplain. In promoting more evidence-based decisions regarding disease diagnosis, the use of DXplain is essential for scaling up the nursing practice (Massachusetts General Hospital Laboratory of Computer Science, 2015). The automation of nursing practice due to the advancements in technology is indispensable if DSS like DXplain is regarded in decision-making situations.
The type of agency utilizing DXplain
Since the development of DXplain in 1984, the DSS has been used in various healthcare organizations to streamline decision-making procedures. MGH has played a key role in ensuring that the usefulness of the software is experienced in the health facility before being used in other healthcare organizations. As the third oldest general hospital in the US, MGH is a teaching hospital that is affiliated with Harvard Medical School. Additionally, the facility is also a biomedical research institution, which crowns it as the health facility that engages in the world’s largest hospital-based research (Massachusetts General Hospital, 2015).
Key decision makers at MGH
The leadership team at MGH is directed by the organization’s mission of delivering excellence in patient care, the advancement of the care through innovative research and education programs that seeks the improvement of the health, and well-being of diversified communities. Led by the President, Peter L. Slavin, MD, the decision-making process regarding management and the administration of clinical services has been fostered through a system of structures that include the DXplain DSS.
The President expects all the activities of the different clinical departments in the organization to be in line with the organization’s goals. Therefore, the bottom-up approach that the organization adopts in the decision-making process assesses the efficiency of the clinicians and researchers in making decisions regarding various health issues and diseases (Massachusetts General Hospital, 2015).
The organization’s chair, Cathy E. Minehan, is another key decision-maker who facilitates streamlined operations of the reputable health facility. As the principal of the board of directors in the facility, the chair supports the development of new technology that seeks to improve the services in the healthcare industry. Minehan is committed to the attainment of efficiency in the provision of healthcare services to the diverse communities that the organization serves as depicted by various IT-based systems that facilitate the decision-making aspect of operations management and service delivery.
John Iafrate, MD, Ph.D., who is the head of the Center for Integrated Diagnostics (CID), is also a key decision-maker based in the clinical services sector at MGH. Iafrate spearheads the development of actionable clinical diagnostics coupled with speeding up the embracement of personalized medicine. Therefore, the decision-making processes involving the clinicians’ diagnosis of diseases with various manifestations are expected to adopt the DXplain application.
Therefore, under the leadership of Iafrate, decision-making aspects of the clinical services at MGH have been fostered through the integration of the Pathology Laboratory Handbook with the DXplain. The Pathology Laboratory Handbook presents the test information, algorithms, and specific procedures and policies that guide the collection of specimen and reporting results. On the other hand, the DXplain interprets the specimen results to provide at least a rank of 10 diagnoses for the disease (Massachusetts General Hospital, 2015).
The three crucial decision-makers at MGH play significant roles in the use of the DXplain DSS due to the efficiency requirements of the organization. The President, who is regarded as the decision-maker with the highest authority at the institution, expects all the MGH professionals to engage in effective decision-making endeavors to streamline healthcare service delivery. Minehan also played a major role in facilitating the development of DXplain and its use to assist clinical decision-making situations concerning the administration of high standard healthcare services.
Iafrate has a direct bearing on the scope of utilization of DXplain in the health institution. Iafrate encourages the use of the DSS in all the endeavors in the diagnosis and treatment procedures to mitigate health care errors (Massachusetts General Hospital, 2015). However, the replacement of human rationality in discerning the manifestations and associating it with a certain disease with accuracy is also encouraged.
Issues affecting the use of DXplain
The use of computerized systems in an organization may cause incompatibilities that are depicted in aspects of communication, security, and privacy. Therefore, the drawbacks of a DSS system like DXplain may challenge the advancement of the nursing practice. However, this assertion does not mean that the inefficiencies cannot be rectified to foster patient safety regarding the decisions made on their conditions (Kawamoto, Houlihan, Balas, & Lobach, 2005).
The communication aspect of clinical services relays the judgments made by clinicians. In this respect, the use of DXplain at MGH could be perceived as a threat to clinical judgment. Therefore, the application of the DSS has promoted the over-reliance on software to make decisions regarding diagnoses. Consequently, clinicians are forced not to think as they rely on the device to make decisions on their behalf (Massachusetts General Hospital, 2015).
Additionally, the evaluation of the decisions made using the software has become a problem since the analysis aspect is computerized, and it might lead to wrong interpretations of the manifestations of a particular disease. Therefore, the aspect of patient safety issues could be compromised in situations where the communication is misinterpreted. Furthermore, the clinicians’ imagination and experience cannot be traced in the decision-making process since the duplication of the same cannot be achieved in a computer software (Kawamoto et al., 2005).
This assertion implies that the clinicians at MGH make communication concerning diagnostics, which is not a depiction of their mastery of the field, but the aid of IT in conducting their tasks. In this case, the interoperability element of clinical services at MGH is enhanced by the use of the decision support systems to facilitate communications in various operations of the organization. The integration of the legacy systems with the software that contains patient records is a crucial aspect that requires privacy and confidentiality to enhance the communication features of the software’s components.
The use of DXplain
The utilization of DSS programs in nursing practice has proved to be essential for facilitating the decision-making aspect of the profession. Understanding the use of the software is critical for comprehending the aspects of source data, information requirements, and the communication features that aid decision-making in the hospital environment (Varonen, Kortteisto, & Kaila, 2008). DXplain boasts of an interactive platform that collects clinical information from various data sets through the use of a modified feature of the Bayesian logic to enhance the derivation of clinical interpretations (Kawamoto et al., 2005).
The data sources are usually derived from the patients’ records regarding the symptoms, signs, or laboratory results regarding a particular disorder. The data sources are usually from the patient, agency, and domain-specific attributes that guide the diagnosis and treatment of various diseases. The patients, as a source of data, undergo admission before care is administered to obtain data associated with their complications. The data could be in the form of signs, symptoms, radiological findings, epidemiological data, or findings from laboratory endoscopy (Astillo & Kelemen, 2013).
The data presented is processed by the DXplain DSS whereby at least ten diagnoses are ranked for the clinician to decide which disorder to treat. MGH has a list of diseases that it treats implying that the data presented is in line with the specific attributes of the complications to promote effective diagnosis that would foster patient satisfaction.
The framework that DXplain applies in the decision-making process incorporates the aspects of capture, storage, processing, communication, presentation, reporting, and availability. This aspect portrays the sequence of data input and the movement before it is available to influence the clinicians’ decision-making endeavors (McCartney, 2007). The data capture phase of the decision-making process through the application of a DSS entails the techniques by which data is availed to the information system. The data source may include entries by the providers regarding the manifestations inherent in the patient, or acquisition from complementary devices that could be used in the diagnosis procedures. This aspect encompasses data sources from the laboratory results, physical manifestations, and the psychological state of the patient (Kawamoto et al., 2005).
The data storage phase includes the programs, methods, and structures that facilitate the organization of data for subsequent use in the decision-making process. Standardized codes and classifications are preserved at this section of the DSS whereby the components of the software recognize the manifestations inherent in each disease (Astillo & Kelemen, 2013). In the case of DXplain, the medical textbook component aids in the storage of data associated with over 2400 diseases that can be discerned from over 5000 manifestations (Massachusetts General Hospital Laboratory of Computer Science, 2015).
The processing phase of decision-making in the nursing field aided by the integration of software like DXplain entails the methods that show how the stored data or information is utilized according to the specific needs of the patient. For instance, the DXplain DSS can be prompted to execute calculations regarding the chronic health evaluation or physiological acuteness of an ill patient to provide the scores for appropriate treatment regarding the disease identified (Massachusetts General Hospital Laboratory of Computer Science, 2015). Therefore, the transformation feature of the application’s execution depicts attributes of abstraction, aggregation, and the summarization of the data presented to it before communication.
The communication of DXplain is essential since the sources of data become meaningful to the clinicians. Since the existing knowledge base (KB) of the DXplain can provide communication associated with over 2400 disorders and 5000 manifestations as portrayed by the patients, then the accuracy of the communication is requisite. Through the user-friendly interface, the communication is relayed by a disease description that averages 53 results that sort between 10 and 100 diagnoses. The ranking enhances the clinicians’ ability to make accurate decisions about the treatment offered.
The presentation of the output for guiding the decisions made by clinicians entails the form in which the delivery of the information reaches the end user, who is the clinician in this regard (Varonen et al., 2008). The presented information concerning the range of diagnoses is usually presented on the PC through the DXplain interface whereby the clinician can view it and take the necessary course of action in improving the health status of the patient.
Reporting of the information presented upholds the essence of confidentiality and privacy as it is recorded in the patient records. Electronic health records (HER) systems can be used along with the DXplain software to facilitate effective information management systems in the hospital setting (Massachusetts General Hospital Laboratory of Computer Science, 2015). This aspect depicts interoperability. The availability of the information that guided the decisions made pertaining a particular disease is normally disclosed to relevant parties who have an interest in the disease that affected a particular patient. The system saves the history of diagnoses for various disorders affecting patients (Astillo & Kelemen, 2013).
Dxplain can be integrated into other systems to enhance communication at MGH. The systems that have portrayed compatibility with the DSS at MGH include TopCare, Primary Care Office Insite (PCOI), Mojo, ebridge, and DischargeExpress. The integrative aspect of DXplain has made it an essential decision-making and communication tool.
Conclusion
The integration of computerized systems in the practice of nursing has improved the decision-making activities involved in quality healthcare provision. Decision support systems (DSS) like DXplain have played significant roles in enhancing efficient decision-making endeavors of clinicians.
The use of the DSS at MGH has depicted its efficiency since it was developed over 30 years ago through the Laboratory of Computer Science department. The DXplain DSS is user-friendly, and it can capture diverse data to aid clinicians in making sound decisions in healthcare provision. Its capabilities to be integrated with other systems in the hospital setting have boosted communication, thus fostering interoperability.
References
Astillo, R., & Kelemen, A. (2013). Considerations for a Successful Clinical Decision Support System. Computers, Informatics, Nursing, 31(7), 319- 326.
Kawamoto, K., Houlihan, A., Balas, A., & Lobach, F. (2005). Improving clinical practice using clinical decision support systems: a systematic review of trials to identify features critical to success. BMJ, 330(7494), 765.
Massachusetts General Hospital Laboratory of Computer Science: DXplain. (2015). Web.
Massachusetts General Hospital (MGH): About Mass General. (2015). Web.
McCartney, P. (2007). The New Networking: Clinical Decision Support Systems. The American Journal of Maternal/Child Nursing, 32(1), 58-59.
Varonen, H., Kortteisto, T., & Kaila, M. (2008). What may help or hinder the implementation of computerized decision support systems (CDSSs): a focus group study with physicians. Family Practice, 25(3), 162-167.