December 2009

Importance of CDSS in Clinical Practice : Dr. Vijayabhaskar Reddy Kandula Physician, St Mark’s Hospital Salt Lake City, Utah. USA & Dr. Sanjay Deodhar Consultant, National Rural Health Mission, Ministry of Health, Govt. of India

Dr. Vijayabhaskar Reddy Kandula
Physician, St Mark’s Hospital
Salt Lake City, Utah. USA

Dr. Sanjay Deodhar
Consultant, National Rural Health Mission, Ministry of Health, Govt. of India

Modern healthcare scenario is a unique synthesis of technology, doctor and patient. The availability and use of medical software technology in clinical decision support was inadequate. However, the latest development in technology including mobile communication and high speed internet connectivity opens up a golden opportunity. Proper integration of these technologies with appropriate medical software can revolutionize the healthcare delivery models globally. The article focuses on the development of clinical decision support software (CDSS) and its various applications.

Majority of the people in the developing world do not have access to basic medical care. The international community is working to bridge this gap for many years without uniform, replicable success. Some inroads have been made but have not derived any system which could make a massive impact in the delivery of primary care. Medical science is changing at a rapid pace, which has made healthcare delivery more complex, costlier and at times inaccessible to majority of the population. However, the rapid advancement in information and communication technology and its universal reach provide new opportunity to bridge this gap. Information technology has improved access to services transforming them universally, though the impact on healthcare is not felt at all levels.

One of the undesirable consequences has been the increasing distance and loss of communication between the patient and the doctor. Information technology can be one of the several newer tools that could help to bridge this gap. Health industry can learn from the experiences of other sectors like microfinance and businesses to reach out to the people at the grass root level.

There has been an explosion in the outreach of mobile communication in the last decade benefiting the poorest communities in the developing world. Increasing access to mobile phones and availability of internet access on mobile platforms is fast bridging the digital divide as well. It seems logical to use this trend to bridge the large gap in access to basic health services. IT and related technologies can be the catalysts that can usher in changes in healthcare to bring basic health services to the door steps of the hither to inaccessible areas.

It is wise to be cautious in embracing these new technologies and emphasize on building  multidisciplinary teams represented by health, IT and management experts so that standard medical care can be developed on IT platforms in a way that is not only cost effective but also user friendly.


Clinical decision support software (CDSS) are interactive computer programs, which are designed to assist physicians and other health professionals with decision making tasks. The basic components of a CDSS include a dynamic (medical) knowledge base and an inferencing mechanism (usually a set of rules derived from the experts and evidence-based medicine) and implemented through medical logic modules.


A clinical decision support system use two or more items of patient data to generate case-specific advice. CDSS is simply a decision support system that is focused on using knowledge management in such a way to achieve clinical advice for patient care based on some number of items of patient data.


The main purposes of CDSS are:

  • To assist clinicians at the point-of-care. This means that a clinician would interact with a CDSS to help determine diagnosis, analysis, etc. of patient data.

  • The new methodology of using CDSS to assist forces the clinician to interact with the CDSS utilizing both the clinician’s knowledge and the CDSS to make a better analysis of the patients’ data than either human or CDSS could make on their own.

  • Typically the CDSS would make suggestions of outputs or a set of outputs for the clinician to look through and the clinician officially picks useful information and removes erroneous CDSS suggestions. The doctor then takes the output of the CDSS and figures out which diagnoses are relevant and which are not.

  • The doctor uses these systems at point-of-care to help them as they are dealing with a patient, with the timing of use as either pre-diagnoses, during diagnoses, or post diagnoses.

  • Pre-diagnoses CDSS systems are used to help the physician prepare the diagnoses. CDSS used during diagnoses are to help review and filter the physician’s preliminary diagnostic choices to improve their final results.

  • Post-diagnoses CDSS systems are used to mine data to derive connections between patients and their past medical history and clinical research to predict future events.


  • Often these systems are stand-alone applications, requiring the clinician to cease working on their current report system, switch to the CDSS, input the necessary data, and receive the information. These additional steps break the flow from the clinician’s perspective, and cost precious time. Of additional irritation is that the data the clinician may need to enter is already contained elsewhere in a digital form in that hospital’s system, and some CDSSs are not equipped to automatically pull this relevant information.

  • Clinical decision support systems face steep technical challenges in a number of areas. Biological systems are profoundly complicated, and a clinical decision may utilize 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 medicinal effectiveness when recommending a patient’s course of treatment. Furthermore, new data is constantly being published which must be integrated into the system in order to maintain its relevance.

Why eClinician CDSS

eClinician CDSS (Clinical Decision Support System) is the outcome of an ambitious project conceived 9 years ago. It is developed over the years by physicians and software developers who have successfully integrated information from standard medical text books and literature systematically. The working medical team consists of 24 medical specialists, many of whom are academic faculty members including professors in reputed medical colleges and hospitals throughout the world. In addition to these experts, the team has general physicians who ensure that the software is user friendly for general physicians.

The software has been refined over several years now and is seen as an innovative tool that can improve the quality of care and decision at the point-of-care.

Advantages of CDSS


      Medical information is simplified such that it is ready to use at point-of-care Incorporates the latest guidelines recommended by professional medical bodies. For example, HIV and AIDS incorporates WHO guidelines, which includes extensive literature review and treatment guidelines.
  • International Classification of Diseases (ICD 10) codes are incorporated and updated constantly to avoid nomenclature disparities which helps in data mining and to monitor standards across health care delivery sites.

  • The program is tailored to be used by health workers with minimum or no computer expertise

  • It is flexible enough to be adapted to suit the needs of different levels of practitioners- from general practitioners to specialist.

  • The differential diagnosis module is capable of generating all probable differential diagnoses from signs and symptoms of the patient. It includes both common and uncommon probabilities in clinical diagnosis.

  • The system helps physicians to avoid overlooking uncommon conditions and provide decision support in difficult cases.

  • eClinician is tailor made fundamentally focusing on Physicians who provide clinical services in rural, semi-urban and even urban areas of developing countries.

  • eClinician is an aid to medical practitioners as it is simple to use, add value to practice and provide immediate access to the most relevant medical knowledge at the point of care.

Accuracy of eClinican in Generating Differential Diagnosis

We did a pilot study to access the accuracy and utility of the software. Four doctors, two general physicians, one paediatrician and one orthopedic surgeon, working in both hospital and clinic environments were selected using convenient sampling methods.

Forty real clinical encounters by these doctors were selected.  The presenting signs and symptoms of these patients were entered into the software and the list of differential diagnoses generated was documented and compared with actual final diagnosis made by the doctors after investigations.

The final diagnosis by the doctor in all the cases (100%) showed up in the list of differential diagnosis generated by eClincian CDSS. In 35 cases, the final diagnoses made by the doctors were first among the list of differential diagnosis. For three cases the final diagnosis matched with the second among the list and for the remaining two it matched with third among the list of differential diagnosis generated by eClinician (Table 1 and Figure 1).


eClinician CDSS software is fairly accurate, user friendly and has high potential to not only improve efficiency in providing clinical care but also to improve quality of health care. The program is developed in flexible computing platform that can be migrated to mobile technology. Since mobile communication technology is becoming universal, eClinician system can be made available to the doctors and health practitioners anywhere in the world.

In addition, eClinican can be linked to any electronic medical records so that it can be integrated into the normal patient flow stages- history taking, vitals signs, examination and lab test- and the data entered can be used as inputs for the program based on which differential diagnosis is generated. This will increase the possibility of making a more accurate diagnosis. Equally important is the ability of the software to provide instant access (with one or two clicks) to relevant medical literature which would normally require 15-20 minutes of extra time and disruption of work flow and possibly patient dissatisfaction.

Other potential benefits include the possibility for general practitioners to diagnose and better manage more severe clinical conditions, which they would normally either refer to a specialist or completely miss the diagnosis. This in turn benefits the patients by healthier outcomes and reduced health care costs.

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