Facilitating pediatric diagnosis using dynamic programming approach
The scenario of maternal and pediatric health in the Philippines has been an ongoing problem. This paper discusses a tool developed to expedite the process of diagnosing, informing the patient and prescribing the appropriate medication using novel techniques in dynamic programming. With the integration of a medical knowledge base, existing patient data and an inference engine, we are able to generate a case specific advise acting as a decision support system for medical practitioners. The goal to facilitate an improved delivery of diagnosis aiding physicians in giving timely and appropriate advice. A team of pediatric physicians piloted and tested this tool garnering an average usability rating of 9.2 over 10.
Keywords: Inference engine, dynamic programming, knapsack, e-health;
Clinical Decision Support Systems (CDSS) are active knowledge systems which use two or more items of patient data to generate case-specific advice. This system forms a significant part of the field of clinical knowledge management technologies through their capacity to support the clinical process and use of knowledge, from diagnosis and investigation through treatment and long-term care. It usually uses integrated database systems with custom front-end user interface that is designed to help physicians address their computerization needs with regards to the information of their patients.
This paper discusses the creation, development and testing of a CDSS targeted for Pediatric Physicians. Related literature is discussed in the next section while having results of usability testings and discussions in mentioned in the latter sections. Findings and insights are discussed as well in the latter parts of this paper.
It actually integrated Decision Support Systems (DSS) into their day to day operating activities. Decision Support Systems are a class of computerized information system that supports decision-making activities. It is an interactive computer-based systems and subsystems intended to help decision makers use communication technologies, data, documents, knowledge and/or models to complete decision process tasks. When DSS is used effectively, they may improve the quality of the diagnostic process in accuracy and efficiency. Making an update on patients’ health information, and giving accurate prescriptions will be more strategic. Also, chosen users (e.g. physicians, medical staff, and patients) can access the information they need without hassle. CDSS has included the impact of the system on the quality of decision making, impact on clinical actions, usability, integration with workflow, and the quality of clinical advice offered.One theory that prompted this research on how patients are being diagnosed that leads to misdiagnosis or giving a delayed diagnosis by the doctor. A number of fetal injuries can be caused by medical malpractice; including brain injuries (such as cerebral palsy and seizure disorders), fractured bones, and erb’s and klumpke’s palsy (damage to nerves that control the arms and hands). However, keep in mind that these injuries are more often caused by something other than medical malpractice (Studdert 2006) . A physician or obstetrician’s negligence can happen during childbirth or long before. If negligent medical treatment is provided during the pregnancy, it could harm the fetus or the mother (or both). Some examples of negligent prenatal care include the physician or obstetricians:
failure to diagnose a medical condition of the mother, such as preeclampsia, Rh incompatibility, hypoglycemia, anemia, or gestational diabetes
failure to identify birth defects
failure to identify ectopic pregnancies, or
failure to diagnose a disease that could be contagious to the mother’s fetus (such as genital herpes or neonatal lupus).
A doctor’s negligence during childbirth could cause injury to the baby and harm to the mother. Common medical errors during childbirth include the physician or obstetricians:
failure to anticipate birth complications due to the baby’s large size or because the umbilical cord got tangled
failure to respond to signs of fetal distress
failure to order a cesarean section when one was appropriate, or
incompetent use of forceps or a vacuum extractor.
Therefore, further study helps us understand how to improve the quality of medical decisions at the time and place that these decisions are made. The specific objectives of this study is to develop a better Clinical Decision Support System that will provide guidelines through which the physicians can model their decisions, and to create decision support system that can lead to a reduction of the practice pattern variation that plagues the healthcare delivery process. The dynamic environment surrounding patient diagnosing complicates the process of diagnosis due to the numerous variables in play, such as individual patient circumstances, the location, time and physician’s previous experiences. A clinical decision support system aims to reduce the effects of these variables.
In addition, misdiagnosis will be eliminated due to the Decision Support System which will output the exact interpretation based on the diagnoses done from the patient. The study mainly focused on the development of decision-making in pediatrics and electronic prescribing (e-prescribing) which generated an intelligent interpretation of the diagnoses and prescribe the best treatment as an action plan for the patients (such as newborn babies up to 21 years of age).
Clinical Decision Support Systems (CDSS) has several definitions, one of which is: Any piece of software that takes as input information about a clinical situation and that produces as output inferences, that can assist practitioners in their decision making and that would be judged as intelligent by the program’s users (Musen 2014).
CDSS is a tool to solve many problems that doctors have to face, namely the information overload, the overspecialization, the lack of cooperation between specialties, and the existence of errors in the health care systems like human errors, health care system errors (Kim 2005).
Clinical Decision Support Systems (CDSS) are computer-based information systems used to integrate clinical and patient information to provide support for decision-making in patient care (Berner 2007). The medical tasks in which CDSS have been successfully used included diagnostic assistance, the generation of alerts and reminders, therapy critiquing/planning, information retrieval, and image recognition and interpretation (Sintchenko 2005).
Computerized systems have been developed to assist the care of newborn infants since Perlstein 1976 first described their system for incubator temperature control. Indeed, CDSS have been created for many areas of neonatal care including management of the ventilated neonate (Tallman 1995) and in prescriptions, for example of parenteral nutrition solutions (Ball, 1985). Systems have also been used for the prediction of length of inpatient stay (Hechler 2009) as well as prognosis of respiratory distress syndrome (Hermansen 2007). These systems were generally reported to have beneficial effects on neonatal care.
A number of CDSS have been successfully evaluated using the randomized controlled trial design (Randolph 1999). A systematic review of these rigorously conducted studies showed that CDSS were effective in improving physician performance and patient outcome, but this review did not investigate systems developed for use in newborn infants (Hunt 1998). Although there are general reviews on the use of CDSS in pediatrics, like the effect of CPOE on prescribing (Kaushal 1998), there are no systematic reviews on the effects of CDSS on care of newborn infants.The concept of computerized decision-support in pediatrics is not new. A mathematical approach was described for the diagnosis of congenital heart disease (Warner 1961). In this study, long before the advent of echocardiography, data were drawn from 1,035 patients referred for cardiac catheterization. Given multiple clinical findings, a matrix of 33 different congenital heart diseases and 50 associated clinical findings was used to calculate the probability of a specific diagnosis. The diagnostic accuracy of this system matched that of 3 pediatric cardiologists. Although, historically, CDSS was primarily were focused on diagnostic recommendations, pediatric decision-support that can be provided by any computer system which deals with clinical data and medical knowledge to help deliver patient-specific advice. Laboratory systems that flag abnormal values, immunization registries that issue vaccination reminders and automated pediatric electrocardiograph (ECG) interpretation are just a few examples of CDSS in common use today. Support systems may be helpful for managing illness and the survival of newborns in the first 28 days of life. They may also influence the performance of doctors treating these newborn infants. The review authors searched the medical literature and contacted experts to find studies on CDSS used with newborns. They identified three randomized controlled studies that met the criteria for the review. Two of these three studies were on computer-aided drug prescribing and the other one was on computerized physiological monitoring of newborns. One of the studies on computer-aided prescribing showed that the CDSS used resulted in fewer drug dosage errors. The studies found no other benefits. The studies did not consider long-term outcomes in the newborns, just short-term effects. Also, with rapid changes in computer technology, current CDSS are more advanced than those used in the three studies. The Cochrane review authors conclude that there is not enough data to determine whether or not CDSS are beneficial for newborn care (Bowen 2010).