Future and Importance of Clinical Analytics by Abdullah Saleem

The need for clinical analytics is only going to expand not only as healthcare organisations gain the ability to uncover more sophisticated analytics, but this is going to be driven by the steep and rapid increase in available clinical data and an imperative to improve the 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 continuous improving of quality of care, faster development of better treatment protocols and improvement in human health on population scales.

One aspect of this is the increasing use of data enabled evidence-based solutions across the organisations. The patient care is a continuously evolving process and the availability of continuous increasing data sets should produce information that will allow to adopt and enhance for better 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 problem & symptom, diseases or conditions. The use case also exist both for insurance company and healthcare providers to examine more population-based data, to compare and contrast the treatment plans for the same disease to identify which treatment plan had the most encouraging outcome as there are lot of efforts being planned to implement the centralised clinical data repository covering public & private healthcare providers covering primary and secondary care.

In addition, the several stakeholders, particularly in the insurance community identified the importance of being able to have access to patient information that can help to guide for the appropriate care. This information, such as the ability of a patient to abide by the prescribed drug regimen or having an adequate support system to provide care upon hospital discharge would be information that will help to ensure that high quality and cost effective care can be set-up and established in the future. Finally, it will be helping to improve the insurance provider experiences.

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 and parameters 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 concerned that all diabetic patients get two-time HbA1c per year. The industry will instead be focused on more complex clinical measures.

One of the most important key factor of clinical analytics would be helping the individual providers and the hospitals to submit the research paper with accuracy of data with reference and evidence which could be impossible to generate such report manually by creating the manual data that is too in a large scale. Therefore, clinical analytics would be more demanding which could be adding value to the followings area to hospitals, hcp’s and payers:

  • ProductiveR&D
  • Disease surveillance and preventivemanagement
  • Development of more effective diagnostic and therapeutictechniques
  • Clinical trial design to prevent failures and speed up the process of drug research anddevelopment
  • To rapidly identify any adverse effects due to use of new drug
  • Improve both provider & patient experience during care delivery
  • Prevent crisis and reduce morbidity/mortality caused by diseases at population levels
  • Reducing false insurance claims andpayments
  • Continuous careimprovement
  • Improved risk scoring for chronic diseases inpopulation health
  • Bolstering patient engagement & patientsatisfaction
  • Predicting patient utilisation patterns for optimising resource utilisation
  • Improve epidemicprediction
  • Speed up new disease cures
  • Improved early detection of diseases and their complications

Challenges to Clinical Analytics

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

Second, just because data is housed electronically entered 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 converted to a discrete, or structured, field and parameters for data analysis to take place, by using natural language processing technologies.

Finally, there are concerns 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 an example of this is lab values that might be captured at an off-site that do not seamlessly transfer to the online system. Hence, this data must be either manually entered or omitted from the overall analysis which will again, not give the complete output while analysing the data.

Another challenge to being able to easily clinical analytics is ensuring that “apple-to-apple” analysis of the data can be facilitated. For example, being able to understand when studying an order sets 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).

Clinical analytics works best on a large data sets which is possible when there is EMR/EHR implementation across the group of the hospitals or state and country level covering both private and public hospitals. Most developed countries have already adopted such models, countries in Asia are on the way to take it ahead. Specially in India, the deployment & adoption of EMR by the hospital community is “Nice to Have” component whereas non-clinical /admin & billing application (HIMSS) is “Must Have” components to run their day-to-day business. However, for the past few years, Indian corporate hospitals have started thinking to take this project ahead as they have got the feeling that without technology adoption, it is impossible to improve quality of patient care, patient safety, provider & patient experience. Based on the current study, there are two hospitals across India which has got the HIMSS Stage-6 accreditation which means there is a long way to go to deploy and adopt the clinical application which could be working seamlessly with the integration of HIMSS.


Availability of cost effective EMR for Asia specific countries excluding Gulf is one of the major issues as it cost goes beyond the IT budget/spend. In other words, more than 90% of the hospitals do not allot the budget for their IT spend annually to enable clinical analytics base point EMR/EHR to be implemented. However, nowadays, there are few EMRs available on transaction-based offering to resolve the budget constraint instead of license fee offering. On the other hand, there are few world class EMR available in Open Source community like OSEHRA-VistA, World- VistA and few of the Indian IT companies are working to implement them either by offering on 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” feature clinically by having the capability to run the EMR on any gadgets (gadget independent). The selected product must be enabling 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 datasecurity
  • Excellent UX design for userease


Hospitals and payer organisations, both are key components of the healthcare delivery system in the healthcare business. 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 to 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 usage 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 that would be going to want answers to, 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 it is the right time for hospital community to take up the 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 TPA’s, having feature of electronically data interchange which could be helping to the organisation to complete the clinical work flow during treating the patient then only the clinical analytics will work to get the better and effective outcome to improve the organisation vision & mission.