Future & Importance of Clinical Analytics

Abdullah Saleem

Clinical analytics is poised to become an essential tool that makes use of real-time medical data to generate insights, take decisions, predict outcomes, and decrease costs by enabling early interventions for potential clinical complications, writes Abdullah Saleem, Group, CIO, Omni Hospitals for Elets News Network (ENN).

The need for clinical analytics is going to increase not only because healthcare organisations gain the ability to uncover more sophisticated analytics, but this is also going to be driven by the steep and rapid increase in available clinical data backed by an imperative to improve clinical outcomes. Clinical analytics is poised to become an essential tool that makes use of real-time medical data to generate insights, take decisions, predict outcomes, and decrease costs by enabling early interventions for potential clinical complications. It will lead to continuous improvement of quality of care, faster development of better treatment protocols and improvement in human health on population scales.Clinical Analytic

One aspect of this is the increasing use of data enabled, evidence-based solutions across organisations. Patient care is an evolving process and the availability of continuous increasing data sets should produce information that will allow to adopt and enhance patient care. This is true in the provider aspect, where healthcare providers can ensure that they are prescribing the best treatment for patients with certain problems & symptoms, diseases or conditions.

Clinical Analytics will provide better output to examine more population-based data, to compare and contrast treatment plans for the diseases to identify which treatment plan & clinical pathway has the most encouraging outcome as lot of efforts are planned to implement the centralised clinical data repository covering public & private healthcare providers covering primary and secondary care, which could be beneficial for both insurance providers and hospitals.

On the other hand, there is also a concern that the level of clinical analytics, that is necessary, will become little complex. As the field evolves, respondents raised the concern that the items that are appropriate for data analysis today will become mainstream and the healthcare industry will have an appetite for more complex questions that we presently don’t have the software and tools to address with accuracy. One example shared was that it will no longer be a concern that all diabetic patients get two-time hba1c test done per year. The industry will instead be focused on more complex clinical measures.

One of the key factors of clinical analytics will be helping individual providers and the hospitals to submit research paper with accuracy of data with reference and evidence Therefore, clinical analytics will be more demanding which could be adding value to the followings areas to hospitals, HCPs and Payers:

  • Productive R&D
  • Disease surveillance and preventive management
  • Development of more effective diagnostic and therapeutic techniques
  • Clinical trial design to prevent failures and speed up the process of drug research and development
  • Rapidly identify any adverse effects due to use of a new drug
  • Improve both provider & patient experience
  • Prevent crises and reduce morbidity/mortality
  • Reducing false insurance claims and payments
  • Continuous care improvement
  • Improved risk scoring for chronic diseases in population health
  • Bolstering patient engagement & patient satisfaction
  • Predicting patient utilisation patterns for optimising resource utilisation
  • Improve epidemic prediction
  • Speed up new disease cures
  • Improved early detection of diseases and their complications


As expected, individuals participating in research have identified several barriers and challenges in their efforts to mine date effectively. One of the most common problems is the format in which the data exists. This manifests itself in several formats. First, respondents have issues with being able to find and structure data that exists only in a non-standard format, as this requires extensive data entry and structures to yield a satisfactory format in which the data can be analysed.

Second, just because data is housed electronically into the system doesn’t mean that it is ready for analysis. Several respondents indicated that in some instances, data is captured in a free-form method, such as a text note. As such, the information would need to be structured for data analysis to take place, by using natural language processing technologies.

Another challenge is to ensure “apple-to-apple” comparison of the data analysed. For example, being able to understand when studying an order set where that information begins and where the data ends. Additionally, it is also important to understand what the clinical context of a data point exists in and how does the clinical context impact the data point. There are also issues with nomenclature and ensuring that data is captured using the same language and medical coding system across the system and other organisations (i.e. data normalisation and semantic interoperability).

Finally, there are concerns related to interoperability that some data elements that are required for data analysis are missing because they were captured in an alternate format that is not streamlined into the main data collection tool. For example, the lab values which might be captured at an off-site that do not seamlessly transfer to the on-line system. Hence, this data must be either manually entered or omitted from the overall analysis which will not give the complete output while analysing the data.


Availability of cost effective EMR for Asia specific countries excluding Gulf is one of the major issues as cost goes beyond the IT budget/spend allocated. In other words, more than 90% of the hospitals do not allot the budget for their IT spends annually to enable clinical analytics base point EMR/EHR to be implemented.

However, nowadays, there are few EMRs available on transactionbased offering to resolve the budget constraint. On the other hand, there are few world class EMRs available in Open Source community like OSEHRA-Vista, World-Vista etc. Few Indian IT companies are working to implement them either by offering on IP/OP encounter and episode-based transaction fee or one-time license fee excluding the implementation cost.

Based on the available options, we need to select the right EMR product which could be addressing all the MUST HAVE features clinically by having the capability to run the EMR on any gadgets (gadget independent). The selected product must enable the following user experiences:

Faster than paper e-prescription process that can complete digital prescription in less than manual time taken

  • Sub-second response times
  • Minimal training needs
  • Evidence based clinical content management
  • Enterprise wide mobile device capable
  • Excellent data security
  • Excellent UX design for user ease


Hospitals and payer organisations, both are key components of the healthcare delivery system. Respondents from both types of organisations agree that cost, efficiency, effectiveness and safety need to be the guiding principles of healthcare delivery globally. These organisations have different business models and as such, the key objective of each organisation about the effective use of clinical analytics is somewhat different. Among the respondents from the hospital community, the costs were identified in the context of being able to deliver quality care at a cost effective price. In comparison, in the insurance provider community, respondents were concerned with being able to provide quality care for their constituents, with an eye for cost-effectiveness.

Clinical analytics can bring huge value to the organisations, yet there are huge hurdles that exist. In order to have effective data to evaluate, healthcare organisations need to consider a number of aspects, including the ability of their software systems to collect the data in a proper format that lend itself to data analysis; the willingness of clinicians to capture clinical information in discrete data fields and parameters; and the willingness of the healthcare organisation to invest in the analysis of this information, from both the perspective of software and tools, integrated & interoperability.

The challenges that organisations face today regarding clinical analytics are only going to be amplified in the future, as is evident in the later stages of clinical use criteria. Several respondents to this research noted that when the healthcare industry figures out the answer to some of the simple questions that we are presently asking, such as, does a diabetic patient get the right preventative care at the organisation level, local community level and at a later stage it would be state and country level. The questions are going to be increasingly complex, requiring a higher skill set and more complex analytical tools. An example may be analysing the genetics and proteomics of people to begin to assess the impact of these factors on their conditions and response to treatments or specific medications.

Now is the right time for the hospital community to take up clinical analytics which is critical for organisations to survive and flourish during changing governmental regulations. Before bringing the analytics, the organisation should begin with EMR deployment having a goal of complete paperless organisation clinically across all the units of the group. It should be integrated with all TPAs having features to facilitate data interchange which can help the organisation to complete the clinical work flow during treating the patient then only the Clinical Analytics will work to get better and effective outcome to improve the organisation vision & mission.


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