Data Analytics

Data Analytics for cell and gene therapy by Dr. Ruchi Dass, @drruchibhatt

Cell and gene therapies are becoming more and more popular because of encouraging clinical results worldwide. Major pharma manufacturing companies have invested in the concept’s commercialization worldwide. Recently, we read about Takeda’s license for commercialization of Aloficel (developed by TiGenix), Celgene’s acquisition of Juno Therapeutics or Gilead’s acquisition of Kite Pharma.

Almighty Data or Hype? By INDERJITH DAVALUR @INDERDAVALUR

DIGITAL TRANSFORMATION AND THE PLACE FOR DATA


Mea Culpa, I am one of those who is guilty of getting on and staying on the Big Data wagon for the wrong reasons. “Data is the new oil” is an oft-repeated phrase. I am about to commit a “virtual” suicide by proclaiming that it is not so. Data has its place and it is not at the top of the digital food chain. I feel that we have crowned the half-naked prince, Emperor in haste.

For the sake of clarity, when I say data, I will be referring to digital data throughout this piece. Data is a by-product of any activity. Therefore, creating data is as natural as breathing. So we have data. A lot of data. So what? Accumulating data, structuring it, storing it, analyzing it are a natural progression from that point onwards. How and what we do with the data is more important. Software. 

The magic that is software, to me, is more transfixing. Consider the prospect of a language written in a semantic that is alien to our natural human language. A cryptic command, logic, condition, trigger – anything at all – that is magically read, understood and acted upon by silicon. Hardware that contains baked-in code that can parse and carry out complex instructions at blazing speeds. Pieces of such chips soldered on a board and communicating through ‘roadways’ of circuits laid out on a board. The miracle of hardware coupled with the magic that is software is what gets my adrenalin pumping. How can such a marvel not be exciting?

Even the awesomeness of hardware pales in comparison to software. Hardware is more or less static. It is confined to physical and functional dimensions. Software, however, is supreme. It can use the same hardware (with some limitations of course) and carry out simple tasks, entertain with games, or perform wildly complex calculations at very very high rates of speed, accurately all the time. And it can do this million million times with alacrity. This is just the beginning of what software can do. But wait, there’s more!

Consider intelligence in software. It suddenly becomes a living, breathing, dynamic being. Almost. Software can learn and teach itself. Crunching data and spitting out patterns and actionable analysis suddenly becomes mundane, banal almost pedestrian. No. I am not against data or big data. By itself, big data is just that. A monstrosity. Sometimes, big data actually gets in the way. Misleads us in making decisions quickly. Software breathes life into data. 

Take any software language or tool. Examine it. Study its flow, the eloquence, the nuance and its brilliance. Brevity in software coding is revered by programming perfectionists. There is elegance in a well-written piece of code that executes beautifully, perfectly, every time. Anyone that can find literary melody in Shakespeare or Milton can certainly begin to enjoy the harmony in a beautifully crafted software application code. So, my appeal goes out to all those who are worshipping big data to take a moment to reflect upon the joy that software brings to our daily lives. After all, the future is software!

Author
Inder Davalur

Inderjith Davalur is a healthcare technology specialist, speaker, writer and utopian dreamer.
Inder works with hospitals committed to transforming the healthcare paradigm with the aid of new innovative technologies. His primary area of interest lies in using data analytics and technologies such as Deep Learning to shift the current physician-driven healthcare model to a patient-driven market dynamic.
Inder focuses on the manifold ways in which data crunching and machine learning can lead to better diagnoses that can not only be made at the time of illness, but predicted way before any symptoms surface. The path ahead in the sector, he believes, lies in the deployment of evolving technologies that immensely influence both diagnostic and therapeutic aspects of healthcare, delivering real patient-driven, data-enabled, informed healthcare.
Inder currently works as the Group CIO at KIMS Hospitals Private Limited, Hyderabad and has previously assumed leadership roles at leading hospitals and companies, in India and the United States of America.

A PhD Researcher’s QnA on #BigDataAnalytics (BDA) with a Healthcare CIO by Inder Davalur, @INDERDAVALUR & Nishita Mehta


Q1. Nishita Mehta: What is data’s role in healthcare & how do you see it influencing future health sector growth in India?

A. Inder Davalur: 

Big Data Analytics (BDA) will have a huge role in healthcare. Healthcare has been a latecomer to using IT as a tool but the future looks good. AI and its children – ML, IoT, and M2M are excellent candidates for advancing technology in healthcare. There is a real potential for technology to advance what I have termed “Connected Continuum of Care” in one of my blogs. This means that with wearables and other Internet of Healthcare Things (IoHT), creating a biome where the patient and doctor/hospital are always connected would become a reality. Always-on Internet is the future and extending that to healthcare is a natural progression. With the price of Internet in India being one of the lowest in the world, we will be in an excellent position to incorporate technology in advancing healthcare delivery.   


Q2. Nishita Mehta: What are the unique challenges of working with clinical data? 

A. Inder Davalur: 

Doctors. Well, the challenge lies with the fact that most clinical data is unstructured. Doctors and hospitals are notorious for NOT wanting to follow standards when it comes to coding diagnoses and treatment. Adoption of DSM, Snomed, ICD codes is very spotty. Physicians complain about the inconvenience in the classifications and prefer to use free text in writing their diagnoses and treatment protocols. This creates a credibility gap in how clinical data can be meaningfully classified and analyzed for any useful prediction or AI driven protocols. EMR applications in India struggle with the similar challenges with physicians disagreeing on a set of standards in capturing and documenting clinical data. 

Q3. Nishita Mehta: Healthcare seems to be moving from the use of structured data to unstructured data. What is the difference between them when it comes to clinical utility & improving patient outcomes?

A. Inder Davalur:  

Healthcare has always suffered from a lack of structured data. Unstructured data creates several challenges in a software trying to classify the same diagnosis written with slight variations. The same fate awaits treatment plans. If medical coding (DSM, ICD etc.) is followed, it will enable any analytics software to make sense of the data and provide useful insights. With AI, structured data is still king. Predictability of an outcome for a set of patient symptoms, medications, prior history, genetic propensity, lifestyle habits would have a high accuracy 

Q4. Nishita Mehta:What do you think does a hospital need to implement Big Data solutions, i.e. Big Data Analytics Capabilities in terms of infrastructure and personnel requirement?

A. Inder Davalur:  

One of the major challenges a CIO or an IT head faces in a hospital is the lack of budget allocation for anything beyond the basic networking, computing and storage needs. Hospitals do not see the value in the data they currently possess most likely because they are more empirically driven rather than evidence driven. What this means is that hospitals and by nature the doctors who hold a sway over management decisions are more comfortable with their own decades of experience over some hotshot CIO trying to promote the idea of data mining and predictive value of patient outcomes based on past data. There is also a severe shortage of technology-rich personnel in hospitals due to the dull routine of maintenance of existing hardware and software rather than experimenting with new technology. The pay structure for IT personnel in hospitals is also woefully poor in comparison to the technology companies. All these factors combine to deter anyone who is driven to create in hospitals a digital core

Q5. Nishita Mehta: While Big Data can generate a plethora of interesting patterns or hypotheses, there is still a need of experts to analyze the results to confirm whether they make sense or merit further inquiry. Would you like to comment on this?

A. Inder Davalur:  

Absolutely. Right now, there is a paucity of people with skills to interpret and recommend action plans once an organization implements any sort of analytics software. Unlike other verticals, healthcare is lagging far behind in its focus on data interpretation and application in its business model. It might be a whole decade before hospitals wake up to the reality of meaningful interpretation of data and building an action plan around it


Q6. Nishita Mehta: What are the major drivers of Big Data Analytics in healthcare in India?

A. Inder Davalur:  

Have not seen much evidence of it. Perhaps some hospitals have ventured into some basic AI driven applications in specific areas such as pharmacy sales or patient wait times. Other than that, BDA is yet to catch up.

Q7. Nishita Mehta: What are the key benefits Indian hospitals will draw from implementation of Big Data Analytics? 

A. Inder Davalur:  

First and foremost, hospitals will get to see for themselves how poorly structured their data is. BDA for Indian hospitals can cover a better management of the following: 
  1. Sponsored
  2. Accounts Receivables
  3. Professional Fees
  4. Disposables and Consumables
  5. Pharmacy – Generic vs brand name consumption
  6. Targeted marketing
  7. Continuum of Care post-discharge
  8. Predictability of illness propensity from regular Health Check ups
  9. Results-oriented tasking for better output from employees

Besides these areas, hospitals can contribute tremendously to public health issues by sharing anonymized patient data with the State Health Department which can then study outbreaks and lifestyle disease patters in the general public. 

Q8. Nishita Mehta: How does Big Data Analytics help better decision-making & building disease understanding?

A. Inder Davalur:  

One of the most ignored areas is a deeper dive into results from investigations. Empirically speaking, the values considered “normal range” are never questioned. If a better study is conducted, what is normal for one cohort may not be so for another cohort. As an academic exercise, I had a simple deep analysis done to study the correlation between borderline values of lipid profile and any other element from a blood test. The result was a high (>70%) correlation between borderline lipid profile values and an elevated monocytes count. It turned out that among those who fell in this group, nearly 78% of them were later admitted for some coronary complication. The medical reason is that the monocyte levels are elevated when there is presence of a heart disease. Every one of these patient was merely getting a Health Check. Imagine if hospitals did such studies on a multitude of investigations routinely conducted for patients and conducted regular follow ups as a preventive measure

Q9. Nishita Mehta: One of the biggest concerns in healthcare is the rising costs. What potential solutions does Big Data offer for this problem in Indian context?

A. Inder Davalur:  

India’s population is now facing more mortalities from lifestyle diseases – Non Communicable Diseases (NCD) as opposed to communicable diseases. There is a great potential to flip the business model of the healthcare industry to go from disease management to health management. I have written blogs on this topic. The premise is very simple. Make it more profitable for hospitals to keep the public healthy than to treat them. If the payment structure is modified to increase the prices for health checkups and promoting healthy prophylactic therapy methods as opposed to coronary by-pass surgeries, it could completely change the paradigm. These prices can be graded based on age. All old age related treatments can receive higher prices; while treatments like a heart surgery for a 40-year old can be less. At the same time, therapeutic treatments for younger population geared for promoting good health can receive higher prices. A larger healthy population means a larger market for the hospitals. This ensures that the hospitals have a higher incentive to make the healthy population larger

Q10. Nishita Mehta: What would you highlight as being the major challenges today in developing & actually implementing Big Data Analytics capabilities to truly extract meaningful insights?

A. Inder Davalur:  

An urgent awareness creation among promoters and owners of hospitals of the benefits of investing in the technical hardware and personnel resources to build and maintain a BDA infrastructure. Without that awareness, IT costs are always seen as a sunken wasteful expenditure as opposed to an investment. There is nothing else lacking in this respect.

Q11. Nishita Mehta: Do most doctors now have a checklist for what they should be doing with patients with certain conditions? How does Big Data solution change what they are doing currently?

A. Inder Davalur:  

Hard to predict. Most clinical pathways and treatment protocols are traditionally empirically driven. It is hard to imagine a medical community to take notice of what BDA might reveal and radically change their protocols. That said, things have changed – take robotic surgery – and there is hope and a high degree of probability that medicine might be “data-powered” (my phrase over the more commonly used data-driven) offering the physician to choose to use such data-powered results wherever she finds it viable or desirables

Q12. Nishita Mehta: How do hospitals need to adapt to embrace the full potential of data-driven innovation?

A. Inder Davalur:  

Promoters and owners having a greater understanding of the power of data

Q13. Nishita Mehta: How important do you think Big Data Management & Analytics is right now to enhance healthcare in India?

A. Inder Davalur:  

Tremendously. With the technical resources at its disposal, India would be imprudent not to take full advantage of the benefits of BDA. Population health data is one of the most ignored among developing nations. India would do extremely well to develop and use BDA for advancing population health

Q14. Nishita Mehta: What do you see as the main emerging opportunities for hospitals from greater adoption of Big Data Analytics?

A. Inder Davalur:  

Connected Continuum of Care (a phrase I first used in a blog) is a concept of keeping the patient engaged post treatment and post discharge through the use of wearables and IoHTs (Internet of Healthcare Things). This ensures that hospitals are not merely agents in episodic encounters and instead become agents of well-being. BDA will help provide the big picture in the overall health and well-being of the population it serves

Q15. Nishita Mehta: What are some of the biggest challenges facing the healthcare industry in terms of its ability to use Big Data to improve healthcare outcomes?

A. Inder Davalur:  

A better understanding and incentive to invest in the infrastructure is all it takes. Once that happens, India is best equipped to leverage from its large technology-aware population. At the hospital level, BDA could help establish a new approach to purely outcomes-driven pricing structure and treatment protocols that would be data-powered. 

Q16. Nishita Mehta: Would you like to share additional insights on the topic, which I might have missed?

A. Inder Davalur:  

Public-Private-Partnerships with educational institutions and hospitals would also be beneficial. There is going to be a severe shortage of technical resources who are trained in AI and BDA by 2020. If the government partnered with colleges to promote courses and training in AI and BDA India could be the largest supplier of technical talent to the world. If hospitals also partnered with the government to share health data, the state of overall population health will rise and costs will come down.

The article was first published on Mr. Inder Davalur’s LinkedIn Pulse page. The blog was Mr. Inder’s answers to Ms. Nishita Mehta’s Survey published on the HCITExpert Blog earlier, here. I would like to thank both the Author’s for sharing their insights via the HCITExperts Blog. 
Team @HCITExperts [Updated: 03 rd Sep 2018]
Authors
Nishita Mehta

Ph.D. Scholar at SYMBIOSIS INTERNATIONAL UNIVERSITY

Inder Davalur

Inderjith Davalur is a healthcare technology specialist, speaker, writer and utopian dreamer.
Inder works with hospitals committed to transforming the healthcare paradigm with the aid of new innovative technologies. His primary area of interest lies in using data analytics and technologies such as Deep Learning to shift the current physician-driven healthcare model to a patient-driven market dynamic.
Inder focuses on the manifold ways in which data crunching and machine learning can lead to better diagnoses that can not only be made at the time of illness, but predicted way before any symptoms surface. The path ahead in the sector, he believes, lies in the deployment of evolving technologies that immensely influence both diagnostic and therapeutic aspects of healthcare, delivering real patient-driven, data-enabled, informed healthcare.
Inder currently works as the Group CIO at KIMS Hospitals Private Limited, Hyderabad and has previously assumed leadership roles at leading hospitals and companies, in India and the United States of America.

Glossary of Terms for Healthcare Data Analytics


BALANCED SCORECARD:
A framework developed by Robert Kaplan and David Norton that suggests four perspectives of performance measurement to provide a comprehensive view of an organisation. These are service user perspective, internal management perspective, continuous improvement perspective and financial perspective.


BENCHMARK:

A point of reference or standard by which something can be measured

BENCHMARKING:

The process of comparing the cost, cycle time, productivity, or quality of a specific process or method to another that is widely considered to be an industry standard or best practice.

CASEMIX:

Casemix is an internationally recognised system of measuring clinical activity incorporating the age, gender and health status of the population served by an organisation with a view to objective determination of hospital reimbursement.

DATA:

Data are numbers, symbols, words, images, graphics that have yet to be organised or analysed

DATA DICTIONARY:

A descriptive list of names (also called representations or displays), definitions, and attributes of data elements to be collected in an information system or database.

DATA ELEMENT:

A unit of data for which the definition, identification, representation, and permissible values are

DOMAINS OF QUALITY:

Are those definable, preferably measurable and actionable, attributes of the system that are related to its functioning to maintain, restore or improve health

DESCRIPTIVE ANALYTICS

As per gartner, Descriptive Analytics is the examination of data or content, usually manually performed, to answer the question “What happened?” (or What is happening?), characterized by traditional business intelligence (BI) and visualizations such as pie charts, bar charts, line graphs, tables, or generated narratives.

PREDICTIVE ANALYTICS

As per gartner, Predictive analytics describes any approach to data mining with four attributes:

1. An emphasis on prediction (rather than description, classification or clustering)
2. Rapid analysis measured in hours or days (rather than the stereotypical months of traditional data mining)
3. An emphasis on the business relevance of the resulting insights (no ivory tower analyses)

4. (increasingly) An emphasis on ease of use, thus making the tools accessible to business users.

PRESCRIPTIVE ANALYTICS

As per Gartner, Prescriptive Analytics is a form of advanced analytics which examines data or content to answer the question “What should be done?” or “What can we do to make _______ happen?”, and is characterized by techniques such as graph analysis, simulation, complex event processing, neural networks, recommendation engines, heuristics, and machine learning.

HEALTH INFORMATION:

Health Information is defined as information, recorded in any form or medium, which is created or communicated by an organisation or individual relating to the past, present or future, physical or mental health or social care of an individual or cohort. It also includes information relating to the management of the health and social care system

KPI SELECTION CRITERIA

KPIs should be chosen based on the judgement and consensus of experts and potential users. The List of characteristics and related questions which can be used to assist in the identification of KPIs. Adapted from criteria developed by the World Health Organization (WHO)


Validity 
Does the KPI measure what it is supposed to measure? A valid KPI measures what it is supposed to measure and captures an important aspect of quality that can be influenced by the healthcare facility or system. Ideally KPIs selected should have links to processes and outcomes through scientific evidence. Measures that have been selected using scientific evidence possess high content validity and measures selected through consensus and guidelines will have high face validity. Content validity refers to whether the KPI captures important aspects of the quality of care provided. Face validity can be determined by the KPI making sense logically and clinically or from previous usage.

Reliability 
Does the KPI provide a consistent measure? The KPI should provide a consistent measure in the same population and settings irrespective of who performs the measurement. Reliability is similar to reproducibility to the extent that if the measure is repeated you should get the same result. Any variations in the result of the KPI should reflect actual changes in the process or outcome. Reliability can be influenced by training, the KPI definition and the precision of the data collection methods. Inter-rater reliability compares differences between evaluators performing the same measurement. Internal consistency examines the relationship between sub-indicators of the same overall measurement, and, if reliable, there should be correlation of the results. Test-retest reliability compares the difference between results when the same evaluator performs the measurement at different times. 

Explicit evidence base
Is the KPI supported by scientific evidence or the consensus of
experts? KPIs should be based on scientific evidence, the consensus of expert opinions among health professionals or on clinical guidelines. The preferred method of choosing KPIs is through evaluating scientific evidence in support of each KPI and rating the strength of that evidence. One example of a rating system is to give the highest rating to evidence (“A” evidence) from meta-analysis of randomised controlled trials and give a lesser rating (“B” evidence) to evidence for controlled studies without randomisation and a further lower rating (“C” evidence) to data from epidemiological studies. 
In healthcare, there may only be limited scientific evidence to support a KPI and it becomes Guidance on developing Key Performance Indicators and Minimum Data Sets to Monitor Healthcare Quality Health Information and Quality Authority 33 necessary to avail of expert opinion. There are a number of methods by which a KPI can be developed through facilitating group consensus from a panel of experts, such as the Delphi technique, the RAND appropriateness method and from clinical guidelines. Appendix 2 gives a brief description of each method and Appendix 3 provides an example of a Delphi assessment instrument. The expert panel can exist independently of the advisory group and are used as a point of reference for the KPI development process
Acceptability 
Are the KPIs acceptable? The data collected should be acceptable to those being assessed and to those carrying out the assessment. 
Feasibility 
Is it possible to collect the required data and is it worth the resources? There should be a feasibility analysis carried out to determine what data are currently collected and the resources required to collect any additional required data. The feasibility analysis should determine what data sources are currently available and if they are relevant to the needs of the current project. This will include determining if there are existing KPIs or benchmarking processes based on these data sources.

The reporting burden of collecting the data contained in the KPI should not outweigh the value of the information obtained. Preferably, data should be integrated into service delivery, and, where additional data are required that are not currently part of service delivery, there should be cost benefit analysis to determine if it is cost-effective to collect.

The feasibility analysis should also include what means are used to collect data and the limitations of the systems used for collection. It should also outline the reporting arrangements, including reporting arrangements for existing data collection and frequency of data collection and analyses.

Sensitivity 
Are small changes reflected in the results? Changes in the component of care being measured should be captured by the measurement process and reflected in the results. The performance indicator should be capable of detecting changes in the quality of care and these changes must be reflected in the resulting values. 

Specificity 
Does the KPI actually capture changes that occur in the service for
which the measure is intended? Only changes in the area being measured are reflected in the measurement results

Relevance 
What useful decisions can be made from the KPI? The results of the measurement should be of use in planning and the subsequent delivery of healthcare and contribute to performance improvement

Balance 
Do we have a set of KPIs that measure different aspects of the service? The final suite of indicators should measure different aspects of the service in order to provide a comprehensive picture of performance, including user perspective

Tested 

Have national and international KPIs been considered? There should be due consideration given to indicators that have been tried and tested in the national and international arena rather than developing new indicators for the same purpose.

Safe 
Will an undue focus on the KPI lead to potential adverse effects on other aspects of quality and safety? The indicator should not lead to an undue focus on the aspect of care being measured that may in turn lead to a compromise in the quality and safety of other aspects of the service.
Avoid duplication 
Has consideration been given to other projects or initiatives? Prior to developing the indicator due consideration should be given to other projects or initiatives to ensure that there will not be a duplication of data collection.

Timeliness
Is the information available within an acceptable period of time to inform decision-makers? The data should be available within a time period that enables decision-makers utilise the data to inform their decision-making process. If the data is required for operational purposes, then it will be required within a shorter timeframe than data used for long term strategic purposes. 

METADATA:

Data that defines and describes other data

MINIMUM DATA SET:

The minimum set of data elements that are required to be collected for a specific purpose

NUMERATOR:

The specifications that define the subset of data items in the denominator that meet the indicator criteria.

KEY PERFORMANCE INDICATORS:

Performance Indicators are specific and measurable elements of practice that can be used to assess quality of care. Indicators are quantitative measures of structures, processes or outcomes that may be correlated with the quality of care delivered by the healthcare system. 

PROCESS INDICATORS:

Performance indicators that monitor the activities carried out in the assessment/diagnosis and treatment of service users.

OUTCOME INDICATORS:

Performance indicators that monitor the desired states resulting from care processes, which may include reduction in morbidity and mortality, and improvement in the quality of life.

RELIABILITY:

Reliability is the consistency of your measurement, or the degree to which an instrument measures the same way each time it is used under the same condition with the same subjects.

STRUCTURE INDICATORS:

Performance indicators that monitor the attributes of the health system that contribute to its ability to meet the healthcare needs of the population.

The Delphi Technique:

The Delphi technique is a facilitated structured process whereby a panel of experts complete questionnaires (see Appendix 3 for example) remotely and, through feedback and scoring over a number of rounds where some KPIs are discarded, a consensus is achieved on a final set of KPIs. The panel need not ever meet face-to-face and each individual’s feedback is provided anonymously to the panel, which eliminates the possibility of undue influence by dominant personalities within the panel.

The RAND appropriateness method:

The RAND appropriateness method combines scientific evidence with expert
opinion by facilitating experts to rate, discuss and re-rate KPIs. Unlike the Delphi technique the expert panel meet face-to-face to discuss possible KPIs and are given a copy of the scientific literature in support of the KPIs so that they can ground their opinion on evidence-based literature

VALIDITY:

Validity of indicators refers to whether performance indicators are measuring what they are supposed to measure. e are constantly looking for Healthcare Informatics & Digital Health Experts to share their experiences by writing articles on Technology benefiting in the delivery of Healthcare Services.

And there you go, its fairly simple and we look forward to you sharing your experiences with our community of readers. We appreciate you considering sharing your knowledge via The HCITExpert Blog
Author
Team HCITExperts

Your partner in Digital Health Transformation using innovative and insightful ideas

KPIs on fingertips – Healthcare by Jyoti Sahai @JyotiSahai


During a recent conversation with the CEO-Doctor of a multi-specialty hospital our discussion veered towards how data-driven decision-making using analytic insights could benefit the hospital. His response, typical of most of the CEOs (for that matter from any industry) was – Oh! I really don’t need any analytics! All the facts I need to run my organization are on my finger-tips!



My takeaway from that conversation were the two keywords ‘facts‘ and ‘fingertips‘! For running a successful organization, you do always need to have near real-time relevant and critical (may be up to ten, one for each fingertip!) facts on what is happening within the company. However, just the facts (measures) may not always be sufficient to arrive at a decision unless those are benchmarked against the desired performance and/or trends over different periods for those measures. Deployment of analytics enables the stakeholders to have that additional edge over the decision-making, by making the exercise based more on validated data than just a gut feeling.

That set me thinking on what could be those top key performance indicators (KPIs) which if available on fingertips (at the click of a button) could aid a CEO in achieving the organizational objectives more effectively, and what could be the ones relevant for a hospital CEO!   .
I presume that any hospital CEO’s top priority is to strive to earn the patients’ trust, and that is possible only if the hospital could meet and exceed patient expectations.

Meeting the patient expectations

What a patient expects from the hospital is a treatment that is effective, timely and fair. The following KPIs keep the hospital CEO and other stakeholders informed on how effectively that is happening?

Treating the patients effectively …

The top hospital stakeholders should be worried if higher % of patients who have been already discharged (whether out-patients from day-care or inpatients with hospital-care) return to hospital for re-treatment or re-admittance for the same ailment. That will show that either the initial diagnosis was flawed, or some critical elements were missed out while administering the treatment. Either way it would be matter of great concern for the hospital CEO, who should always be aware of the Re-admittance Index – % of discharged patients who required re-treatment or re-admittance.

... and timely …

One of the most critical performance indicator within a day-care hospital is the TAT, the turnaround time – the elapsed time between entry of the patient in the hospital (registration) and start of consultation of that patient by the physician. Other important TATs that are tracked within a hospital include – for a test being conducted, the elapsed time between the ordering of the test till the report collection, and most importantly for an inpatient, the elapsed time between the decision to discharge and the actual vacating of the bed. Inordinate delays in these lead to irritated patients, increased costs, and avoidable queueing issues too. Typically hospitals set internal benchmarks, or compare with any available industry benchmarks, to track the various TATs. In case of inordinate delays, hospitals could carry out a root cause analysis and take preventive and corrective actions.
What any hospital CEO should strive for is that the TAT Index for any given period is less than 5%, that means not more than 5% patient-visits experience a delay beyond a set benchmark in treatment or in discharge.

… and fairly …

I remember once a CEO of a hospital was concerned about if any of the eleven consultants in the hospital were at any time prescribing investigations and/or medicines that were not warranted for the observed symptoms and the medical condition of the patient. Periodic audit of all prescriptions comparing those prescriptions with a defined set of rules (lines of treatment) for corresponding symptoms will give a fair idea of the deviations if any. What a CEO has to do to control it, is to always ensure that the Unfair Treatment Index (% of possible deviations from an appropriate line of treatment) is kept below the minimum acceptable tolerance benchmark.

… and thus earning patients’ trust!

A hospital may expect that it has earned a patient’s trust by providing treatment that is effective, timely and fair, but it can really know that for sure by arriving at the Patient Satisfaction (P-SAT) Index only. P-SAT can be derived by analyzing the feedbacks received from the patients, results of internal surveys, and the comments (adverse or commending) on the social media. A prudent CEO always depends upon the P-SAT Index to accurately gauge the extent of the hospital’s success and reputation.
We have now understood that patients’ trust can be earned by providing effective, timely and fair treatment. However none of that is possible unless the hospital itself is run efficiently and profitably.
How does the CEO keep track whether the hospital is run efficiently?

Managing the hospital operations efficiently

For meeting and exceeding the patients’ expectations it is imperative that the hospital operations including administrative and clinical processes are efficient and stable. Primarily it means that the all the hospital resources are used optimally, and are available for use when needed. The above-mentioned TAT Index is one such KPI. The following other KPIs too provide an indication of a hospital’s operational efficiency.

Are the resources and infrastructure used optimally?

Hospital resources and infrastructure, if not used optimally, lead to lost opportunity, frittering away of resources, and most importantly increase in operating costs. The Management has to ensure that the various Wards, Operation Theaters, Labs, and various equipments, and even the service providers (human resources) are available for providing service to the patient when needed. Out of these various parameters, tracking of the bed utilization (% of hospital beds occupied at any given time) is considered very critical for any large hospital as it has a direct impact on the efficiency of that hospital. A consistently low bed utilization could mean among other things, either faulty planning (resulting in over-investment) or a low P-SAT. On the other hand a consistently high bed utilization could lead to severe strain on resources and maybe result in declining quality of service.
Thus it is imperative that the hospital CEO constantly monitor the Bed Utilization Index.

Are the patients kept in hospital for a period that is necessary and sufficient?

One of the most critical KPIs for a hospital is the Average Length of Stay (ALOS) of inpatients for specific types of ailments or procedures carried out. The hospital could compare its such averages with either the industry benchmarks, or internally set benchmarks. For example assume that for a specific operation procedure (including the pre-operation and post-operation in-hopsital care) the ALOS is 6 days. If elsewhere in the industry the ALOS for the same procedure is 7 days, that will mean either your administrative and/or clinical processes are more efficient than others or you may be missing out on some necessary hospital-care (a point not in your favor). On the other hand if the ALOS elsewhere is 5 days, that will mean either you are providing some additional necessary services that others are not offering (a point in your favor) or your treatment more often is less efficient (your processes take extra time and/or resources for the same procedure).
Either way the CEO should keep a close watch on ALOS to optimize the services provided under the various procedures offered by the hospital.
However, even an efficiently run hospital having earned it patients’ trust to may fail if it is financially weak.

Monitoring the financial health of the hospital

For a hospital to ensure efficiency in its operations, it is imperative that its finances are stable and profitable. Without that the hospital will not be able to sustain its efficient operations for a longer period. It is the hospital CEO’s prime responsibility to ensure that that does not happen. The hospital CEO can depend upon the following KPIs to keep a check on the financial health of the hospital itself.

What is the hospital’s margin on an average for each patient-visit?

Whether you are an individual or an establishment, the universal fact remains that you cannot consistently spend more than what you earn if you have to sustain financially in the long-term.
What is critical for the hospital Management is to know what is the hospital earning on an average for each visit that a patient makes to it for treatment. Once ARPV is known for a period, and is compared with the average cost of operations for that period (ACPV), the hospital CEO knows whether the hospital operations at the current levels are sustainable or not.
Trends of ARPV and ACPV over a period give sufficient insights to the CEO to arrive at fair pricing of services, and take steps to manage optimal utilization of resources.
However a strong ARPV or a manageable ACPV alone will not be sufficient for financial stability unless the cash management is also strong.

Are the insurance claims being settled in time by the insurance companies?

Once a CEO of a 100-bed hospital was complaining that though he knew that the hospital had been having a strong revenue stream during that period, he was finding it difficult to pay on time for even the relatively small purchases made for materials and services. Why was that? A quick look at the hospital accounts revealed that (as is typical of all medium-large hospitals) almost 75% of the hospital revenue was derived thru insured patients, provided care under cash-less treatment schemes. It was also found that a substantial portion of that money was blocked in over-due claims submitted to the insurance companies and remaining outstanding for various reasons. That meant that the cash-flow was heavily dependent upon the timely settlement of insurance claims.
Any prudent CEO keeps a tight watch on the number of days claim outstanding (DCO) with the insurance companies; monitoring closely the TPAs – Third-party Administrators – ensuring that the claims are settled by the insurance companies as per agreed contractual terms. Timely settlement of insurance claims results in improved and predictable cash-flows and strengthens financial stability.
A hospital CEO may track the above-mentioned KPIs and ensure that the hospital is earning patients’ trust, and is operationally efficient and is financially stable too. But the litmus test of a hospital’s reputation and success is when its performance is compared with its peers, the other similar hospitals in the geography or with the same specialization.

Where does the hospital stand when compared with its peers?

Several independent agencies periodically rank the participating hospitals based on various performance factors, and the ranking could be geography-wise, type of hospital-wise, or specialty-wise.
For a CEO it is imperative that whichever ranking is most important for the hospital is thoroughly analyzed, and a proper strategy to improve/maintain the ranking in future put in place.

In conclusion

How does the CEO keep track of the above-mentioned top KPIs? The CEO’s Dashboardcould display the current status of the KPIs, available at any time at the click of a button (literally putting those on fingertips). A typical dashboard containing the critical KPIs could look like as shown below:
(The numbers and the traffic-light shown against each KPI in the dashboard are for illustration purpose only and do not represent any industry benchmark or desired value)
The above list contains the typical KPIs critical for gauging any hospital’s performance on various operational and financial parameters. However depending upon the criticality for a particular hospital, different and more relevant KPIs could replace those less relevant for that hospital.
By design, I have not included any KPIs or insights produced by clinical analytics, as those will be specialized and specific to each individual hospital.
My suggestion is that let the CEOs use their fingertips for recalling critical tricks of their trade and expertise only, and let an analytics system recall the KPIs for them whenever needed for reference!
[Glossary:
ACPV – Average Cost per Patient-Visit; ALOS – Average Length of Stay; ARPV – Average Revenue per Patient-Visit; DCO – Days Claims Outstanding; KPI – Key Performance Indicator; P-SAT – Patient Satisfaction; TAT – Turn-around Time]

Note: A version of this article also appears in my blog gyaan-alytics and more…

Author
Jyoti Sahai

Jyoti Sahai has over 42 years of experience in banking and IT industry, and is currently the CMD of Kavaii Business Analytics India. Kavaii provides analytic solutions in Healthcare and IT Services domains.
Scroll to Top
Connect
1
👋 Hello
Hello!! 👋 Manish here, Thanks for visiting The Healthcare IT Experts Blog !! How can i help you?