To provide better and cost effective patient care, one needs to exchange healthcare information. For this to happen seamlessly, there is a dire need of Standards that facilitate this interoperability.Continue reading “Terminology Standards for Health Information Exchange in the times of SARS-Cov2 by Prof. Supten Sarbadhikari, @supten”
I was out of doctor’s room in couple of minutes with a scribbled prescription in hand, not very sure if the physician had actually understood my problem. Clinic’s pharmacist words gave me confidence “Doctor is very experienced, he can diagnose problems within a minute. You will get better in couple of days”.Continue reading “Re-Imagining #EMR for India by Kumar Satyam, @kr_satyam”
In this blog post, we talk about one component of the National Health Stack – Federated Personal Health Records: its design, the role of policy and potential use casesContinue reading “Federated Personal Health Records – The Quest For #UseCases by Anukriti Chaudhary, @anukritichaudh2”
Finding an alternative with the most cost effective or highest achievable performance under the given constraints, by maximizing desired factors and minimizing undesired ones. In comparison, maximization means trying to attain the highest or maximum result or outcome without regard to cost or expense.Continue reading “Hospital Information System The two sides of a coin by Dr. Paridhi Mathur”
Continue reading “#EHR in India: Challenges and Opportunities vis-a’-vis’ Ayushman Bharat by Dr. Oommen John, @oommen_john”
As India is embarking on a journey towards providing Universal Health Coverage through multi-pronged approaches of reducing catastrophic out of pocket expenditure and increasing access to essential health services , it is envisaged that Health Information Technologies (HIT) / Digital Health would create enabling environments for addressing some of the system level challenges in healthcare delivery.
Practicing physicians these days are barraged with a lot of technical jargon promoted by the Information technology professionals such as Big Data, Hadoop, Artificial Intelligence and Predictive analytics. For a physician not introduced to the these terms, the conversation is of little value unless there is a specified value in the clinical setting.Continue reading “Algorithms in #EMR by Dr. Joyoti Goswami @Joyoti10”
– Based on the legal entity ( Private , Trust or Corporate)
– Based on specialty ( Super specialty, Multi-specialty, Single specialty)
– Based on bed strength ( Larger hospitals and Nursing Homes)
Hospital Industry is unique as compared to BFSI and FMCG industry as there is minimal or almost no standardization in the Processes/Operations between hospitals of similar nature, for example “Admission, Discharge and Billing Processes may vary from hospital to hospital. One more major difference is about the employment of Doctor’s. In some hospitals Doctors are Consultants and in some hospitals they are employees or on the payroll of that hospital.
Due to above factors it’s very difficult to build and implement a global product for hospital industry. Although many companies have attempted to build a global product for the hospital industry they have not been very successful.
The software product developed by the vendors may be technically sound for the hospital industry. However, most of the vendors face major implementation challenges as they are not aware of the practical scenarios in different hospitals since the nature of the hospitals and processes in every hospitals vary as mentioned above.
Hence the customization percentage is very high and the stability of the product becomes an issue. As the degree of customization various from hospital to hospital, the customized product becomes local to that hospital and it becomes difficult for the vendor to maintain and give support to a particular hospital. The other major challenge faced by the vendors is to implement a software product/solution in a brown field project (running hospital), where the processes are set, hence there is a resistance to change by the users to implement a new software. In case of a green field (new hospital) it is not very difficult to implement a software solution as there are no preset processes.
Now let’s see the major software applications used in hospital industry.
– HMS (Hospital Management System).
– RIS (Radiology Information System) and
– PACS (Picture Archival and Communication Systems).
– DMS (Document Management System).
– Mobility Apps.
HMS is the core application or like the ERP used in hospital industry. It mainly contains modules like Admission, Discharge, Transfers, Billing, In Patients, etc. Since it has the mentioned modules like Admission and Billing, it’s difficult to develop a global HMS application as the variation in processes across hospitals. Other modules like Finance, Inventory, PACS are standard in nature and may or may not be a part of HMS. These modules can be separately developed and seamlessly integrated with the HMS application. Most of the hospitals have adopted the practice of having HMS with only the core Billing and Admission modules and build & integrate other modules around HMS.
Mobile apps & BI tools have helped Vendors to build standard applications wherein they have to fetch the data from HMS and other modules and display it in the app. Unlike HMS application which is dependent on the processes of that particular hospital, mobile apps & BI tools are not process dependent and just fetch data from HMS and other modules to be displayed to the top management for analysis of Business process and making key decisions.
Lot of vendors are now focusing on capturing clinical data and converting the same into EMR/EHR. Although there are various solutions available for capturing clinical data adoption of such software is still an issue. Since most of the hospitals have started capturing clinical data, the next logical step is to use the data to develop applications that can assist doctors in their diagnosis and treatment. CDSS (Clinical Decision Support System) and Artificial Intelligence will be the focus of the vendors which will bring a revolution in the Healthcare ecosystem. These applications will be widely used by Doctors not only for preventive health to diagnose and treat their patients, but also will be used to predict the health of a patient depending on the amount of data that has been captured.
The article was first published in the CIO Insider Magazine, here. The article has been republished here with the authors’ permission.
The article was first published here, it has been republished on the HCITExperts Blog with the authors’ permission.
The 2 part paper (review part 1 here): Discusses the key role of evidence-adaptive clinical decision support systems (CDSS) in the healthcare system of the future. Weighs the pros and cons that hospitals should consider when deciding to buy or build such decision support tools
Care that is important is often not delivered. Care that is delivered is often not important1.
IMPLICATIONS OF BUILDING CDSS
Medical knowledge-base construction and maintenance is a significant challenge. After the first few years of creating the knowledge base, adding new evidence to the system is no longer research – it is system development. As such, it becomes increasingly difficult to recruit a cadre of medically knowledgeable individuals who can devote substantial effort to knowledge-base maintenance over time. Creating a Clinical Practice Guideline (CPG) usually takes three to six months (or even a year), depending on the subject matter9.
The maintenance of a CPG is likely to take more than a quarter of the time it took to originally develop the Guideline10. To develop CPGs, a standard set of guidelines covering all specialties, represents 12,000 hours of work at a cost of more than 1 million USD for just the content alone10. The total cost of authoring, reviewing, and EHR integration can surpass 3 million USD for just 200 Order Sets11. With the growth of Fifth and Sixth Generation EHRs, the concept of building in-house CDSS will increasingly become less favorable.
BENEFITS OF BUYING CDSS
As we move from logical (rules-based) CDSS to a foreseeable future of statistical (machine learning-based) systems, the decision to purchase rather than build and maintain knowledge-based CDSS becomes a sensible, convenient and cost-effective choice (Table 2).
First and foremost, choosing to “buy” third-party CDS Solutions can help to outsource the huge burden of managing and updating the clinical knowledge base to a vendor that provides the dual advantage of access to peer-reviewed content created by experts, and amalgamating organisational workflows with
evidence-based practices via collaborative platforms. Building consensus amongst “experts” becomes easier when the source of evidence is credible.
Advanced CDSS are usually built on accepted and defined standards that have been peer-reviewed and fine-tuned to provide higher sensitivity and specificity for each condition.
Customisation can also be taken a step further by selecting solutions that have a content management system for ease of customising the content to fit specific guidelines of the organisation. These external CDSS may also have a proven track record of effectiveness with other organisations, which in turn results in costs savings for less ‘trial-and-error’ as compared to “building” CDSS. The return on investment is primarily in the form of reduced spending on unnecessary tests and procedures as well as avoidance of costly adverse events (and in many systems, malpractice litigation claims), and secondly in the form of saved care replacement costs that result from pulling clinicians away from care processes (to build CDSS). These savings can add up to significant amount annually – almost 2.6 million USD as per one estimation11.
Furthermore, such standard CDSS implementations enable interoperability in Health Information Exchanges. As far as project implementation is concerned, an external influence provides the opportunity to reengineer improvements into your original processes. Advancements in interoperability standards also facilitate more seamless integration with EHR. Professional practice services for EHR integration and implementation support that are provided by progressive knowledge partners, can cut down the implementation costs significantly and improve the efficiency and effectiveness of a large-scale CDSS roll-out.
Lastly, with pharmacogenomics becoming an emerging field in patient care, demand for this new form of CDSS is increasing. In this case, building this knowledge base seems even less of an option when considering the expertise and time needed to manage and update it.
CONSIDERATIONS IN SELECTING CDSS
As with all third-party platforms, the convenience that comes with buying often includes challenges that need consideration. These include the integration effort for disparate platforms, investment in system upgrades and the additional effort from IT staff for monitoring the performance of these external platforms. Legacy stand-alone CDSS systems need to be integrated or discontinued. Yet, certain safeguards or mitigation plans can be considered to maximize the advantage of buying CDSS.
These are best summarized in the following five C’s:
1. Clinical team: Selection and implementation of the CDSS should involve the clinical teams to ensure that it meets the needs of the end-user for successful adoption and continued usage
2. Credibility: Consider vendors with a proven case of working with other providers to smoothly manage the change experience
3. Capability: Evaluate vendor’s ability to effectively synthesize evidence into evidence-adaptive technology platforms, thereby successfully bringing about true practice transformation
4. Configuration: Ensure EHR platforms have been configured to integrate with CDSS and updated to comply with latest interoperability standards
5. Computation: Define metrics that measure performance of each CDS element and outline a clear process for monitoring to tweak elements that are underperforming
Following the appointment of a vendor, healthcare organizations should further establish governance structures and develop a clinical knowledge management framework that can consistently track and improve effectiveness of their chosen CDSS platform. In all, keeping in mind the above considerations will enable providers to better chart out their journey towards a successful CDSS adoption.
With the deluge of evidence that is often fallible and slow to diffuse into clinical practice, along with advanced EHR platform integration requirements, hospitals must reconsider their likely initial inclination towards building their own CDSS. A number of major initial and ongoing challenges with home-grown solutions, including care replacement costs, time and effort to constantly update evidence; usability; implementation and maintenance costs; and accepted functional practice integration can be overcome with the purchase of proprietary CDSS. Overall, the selection of CDSS should also involve the clinical team from the start, as well as careful selection of vendors who show a high level of willingness to partner in the transformation journey.