Ok, so I am not the bragging type, but this referral from a customer is something I had to share :)
“Using Meta Analytix’s
cloud based Max on Demand helped us integrate data from two
different hospital systems cost effectively. We were able to generate quality
analytical reporting with very little initial investment in Data Warehousing
and Business Intelligence technologies. We were confronted with a very tight
deadline to quickly produce quality analytics and Meta Analytix was able
to deliver right on time and within our budget.” – Executive
Vice President, North East IPA Group
Monday, April 23, 2012
Monday, April 9, 2012
Functional Analytics - Data Warehouse or Data Mart?
Data Warehouse or Data Mart?
So, here is the age old question. Should I go the Kimball route (data mart first) or the Inmon route (Enterprise Warehouse first)? While both have merits and demerits (we are not going to get into that discussion here as it has been done before). I think, a "functional" approach should be taken to maximize value. Let me explain. If you look at a physical warehouse, let's say, the warehouse of a shipping company, you don't see one warehouse for canned goods and another for toys and another one for perishables. You may see sections carved out for canned goods, toys and maybe a refrigerated section for perishables. But rarely do you see three separate buildings for each type of item. So why should it be different for a Data Warehouse? Basically, if you look at the data warehousing concept, we will see data marts that are "specific" to a line of business and a warehouse who is the closest thing to a physical warehouse, but often times so scattered and/or so complex that the LOB (Line of Business, if you didn't already know that) seldom finds use for it. So are we stuck? Not really!
A Flexible, Extendable model.
What if you can have the best of both worlds? What if you could rapidly deploy a warehouse, knowing that there is going to be more LOBs added later?
What if, we could deploy this warehouse, knowing that tomorrow, "perishables" may be added to the warehouse and the model has to be adept enough to add "refrigeration"?
What if, you could add more information at different levels of granularity to the same model?
And...the kicker....what if, we could correlate information across the enterprise, with the same model and don't have to create LOB specific data marts?
"Hogwash, won't happen, what did you smoke this morning?" you say?
"Functional" Approach
So, if you are working for an organization whose primary business is NOT building software, then, you, my IT colleague, is there to support the "business user". I have yet to see a business user who has come to me and said "Boy, I wish I had a data warehouse". All of them, however, have asked me one thing. "I wish I had good quality information. And I need it today, if possible". So why not give them just that? Rapid Deployment, Ability to add more information later. Can you design such a model? The answer is a resounding YES!
Has it been done before?
Do I have to repeat myself? I did say YES, didn't I? And I added the word "resounding" to give it a dramatic emphasis! Yes we do have such a model. Unfortunately, I can't share the specifics of it just yet without an NDA or holding your first born hostage. Our FlexDimensional model does just that. It allows us to do a few things:
1. Rapidly deploy with limited up front information (business process, granularity, fact etc)
2. Add more information without having to build new star, snowflake, or whatever else schema
3. Correlate enterprise wide information in a single, extendable model.
So, there you have it. There is always a better way of delivering "good quality information" at the point of decision making!
So, here is the age old question. Should I go the Kimball route (data mart first) or the Inmon route (Enterprise Warehouse first)? While both have merits and demerits (we are not going to get into that discussion here as it has been done before). I think, a "functional" approach should be taken to maximize value. Let me explain. If you look at a physical warehouse, let's say, the warehouse of a shipping company, you don't see one warehouse for canned goods and another for toys and another one for perishables. You may see sections carved out for canned goods, toys and maybe a refrigerated section for perishables. But rarely do you see three separate buildings for each type of item. So why should it be different for a Data Warehouse? Basically, if you look at the data warehousing concept, we will see data marts that are "specific" to a line of business and a warehouse who is the closest thing to a physical warehouse, but often times so scattered and/or so complex that the LOB (Line of Business, if you didn't already know that) seldom finds use for it. So are we stuck? Not really!
A Flexible, Extendable model.
What if you can have the best of both worlds? What if you could rapidly deploy a warehouse, knowing that there is going to be more LOBs added later?
What if, we could deploy this warehouse, knowing that tomorrow, "perishables" may be added to the warehouse and the model has to be adept enough to add "refrigeration"?
What if, you could add more information at different levels of granularity to the same model?
And...the kicker....what if, we could correlate information across the enterprise, with the same model and don't have to create LOB specific data marts?
"Hogwash, won't happen, what did you smoke this morning?" you say?
"Functional" Approach
So, if you are working for an organization whose primary business is NOT building software, then, you, my IT colleague, is there to support the "business user". I have yet to see a business user who has come to me and said "Boy, I wish I had a data warehouse". All of them, however, have asked me one thing. "I wish I had good quality information. And I need it today, if possible". So why not give them just that? Rapid Deployment, Ability to add more information later. Can you design such a model? The answer is a resounding YES!
Has it been done before?
Do I have to repeat myself? I did say YES, didn't I? And I added the word "resounding" to give it a dramatic emphasis! Yes we do have such a model. Unfortunately, I can't share the specifics of it just yet without an NDA or holding your first born hostage. Our FlexDimensional model does just that. It allows us to do a few things:
1. Rapidly deploy with limited up front information (business process, granularity, fact etc)
2. Add more information without having to build new star, snowflake, or whatever else schema
3. Correlate enterprise wide information in a single, extendable model.
So, there you have it. There is always a better way of delivering "good quality information" at the point of decision making!
Thursday, March 29, 2012
ICD9 - ICD10 Conversion Assistant - MxConvert
Ok,
So this is pretty slick and I have to tell you about it. We recently launched an ICD9 - ICD10 conversion assistant tool called MxConvert that works within Excel. This is geared for you Code Warriors in Healthcare who have to undertake the massive task of converting ICD-9 codes to ICD-10. Take a look:
http://www.metaanalytix.com/page.php?page=38
So this is pretty slick and I have to tell you about it. We recently launched an ICD9 - ICD10 conversion assistant tool called MxConvert that works within Excel. This is geared for you Code Warriors in Healthcare who have to undertake the massive task of converting ICD-9 codes to ICD-10. Take a look:
http://www.metaanalytix.com/page.php?page=38
Wednesday, March 7, 2012
This ain't your grandma's locker! - PHI in the Cloud
So, recently I was talking to a potential customer about our MAx on Demand offering, which is our SaaS Healthcare Informatics platform and he said that one of the challenges he's been facing is convincing his Sr. Executives that it is safe to host Protected Health Information (PHI) in the cloud. He asked me if I had any arguments for it. Boy, where do I begin? Here is a stab at it.
Not your grandma's locker!
That's right, this is not granny's locker where the key hangs "securely" around her neck! If you have doubts about how sophisticated our data protection capabilities are, take a look:
Companies like ours, take the security of our clients' and their customers' data very seriously. So, there are two high levels of security in place, Physical Security & Network Security.
Physical Security
To access our data center physically, you have to go through a series of checks. If you are an employee, you are given a badge (after background checks of course!). This badge lets you in the parking lot and to the office spaces. You still can't get to the data center. To get to the data center, you have to have biometric access and a secure access code. This will let you into the data center. From the data center, to access your server, you have to have another secure pin that allows you to physically touch the servers. All of your actions are monitored by security cameras and stored.
If you are a visitor to our data center, you have to register with the guard station. You are ID'd and photographed. You will be escorted by an employee during your visit.
Network Security
Our network security policies and procedures ensure the protection of company wide networks, related devices, and their services from unauthorized intrusion, modification, destruction, or disclosure. Network security provides assurance that a network performs its critical functions correctly, efficiently, and without any interference. Its primary goal is to provide a reliable and secure platform, designed specifically so that users and programs perform only the actions allowed.
Firewalls – Firewalls are utilized to provide dedicated, security specific processing hardware and a complete set of Unified Threat Management (UTM) security features including stateful firewall and web filtering.
Virus Protection – Antivirus software is installed on all Microsoft based servers and workstations. Automatic updates are configured to ensure latest signature download for system protections.
Logging – System logging occurs according to system settings defined by the administrator. Log records can be retrieved as needed. Successful and failed logon activities are logged by domain controllers.
Attck Monitoring - If that is not enough, you can always request 24x7 attack monitoring for your servers!
Disaster Recovery - If a disaster strikes, you can still sleep tight knowing that there is a triple redundant power supply to our data center. Oh, by the way, did I mention that this is a Category 5 hurricane resistant building?
Business Associate Agreements
And if you are still worried about liability, most reputed companies like ours will sign Business Associate Agreements as defined by HHS that makes us adhere to HIPAA laws and liable for breach of Private information: (http://www.hhs.gov/ocr/privacy/hipaa/understanding/coveredentities/contractprov.html)
So yeah, we know that you take your data protection seriously, so do we!
Tuesday, February 21, 2012
Accountable Care Organizations Explained
Here is a simple, easy to read ACO article on NPR
http://www.npr.org/2011/04/01/132937232/accountable-care-organizations-explained
http://www.npr.org/2011/04/01/132937232/accountable-care-organizations-explained
Monday, February 6, 2012
Predictive Modeling in Healthcare
Ok, so these are my musings over predictive modeling, not a knock on it. (That is the disclaimer). So, as I was sitting around, pretending to watch TV and ignoring the dog who seemed to want to go out and play, I thought about Predictive modeling, in a healthcare setting. Specifically, in a Healthcare Provider setting. I actually asked this question on several groups on LinkedIn and asked folks if they have had success with it. The only person who responded with a success story was Mr. Alex Zverev (you can view his profile on LinkedIn here: http://www.linkedin.com/pub/alex-zverev/1/a01/b03), and the scenarios in which he has had successes here (http://www.linkedin.com/groupAnswers?viewQuestionAndAnswers=&discussionID=90342387&gid=93115&commentID=65616962&goback=%2Egmp_93115%2Eamf_93115_10544349&trk=NUS_DISC_Q-ncuc_mr#commentID_65616962). So here are some of my thoughts.
My primary interest is in the Return on Investment of using predictive modeling. I see quite a few software providers out there touting to have predictive modeling capabilities, but haven't heard of a lot of success stories, especially in a healthcare provider setting. Even lesser information is available on the ROI of implementing a predictive modeling solution.
To me, an ultimate predictive modeling solution would be something that can predict the stock market, which has infinite number of variables to consider. But if it were that simple, everyone would be doing it. On the other hand, in healthcare, people are touting Clinical Decision Support capabilities using predictive modeling. "Which patient of yours is most likely to develop cancer?", for example. In my humble opinion, that again is quite a stretch, because of the number of variables that need to be taken into account, not to mention "objective research" that is available to create the model in the first place.
For example, it would be easy to say that a smoker of Asian descent between the ages of 18-40 may develop cancer quicker than others. But what if he is a smoker with healthy eating habits and hits the gym 4 days a week? What if that person only smokes three cigs a day? What if he has no genetic predisposition to cancer? To me, this a cool exercise to conduct and eventually, as you gather more and more data and "evidence" really starts supporting your research in cancer, your model becomes much more reliable and this will start generating a measurable ROI, by reducing the cost of treating a patient through early screening and through preventive medicine.
My thought is that if you are going to do predictive modeling, start with an area with a limited number of variables. Your "bang for the buck" would be realized sooner and it would be greater in that scenario. For example, the scenarios that Alex describes (Capacity Planning and Measured Display Times for Display Stations) have a better chance of an "immediate" ROI than, let's say, a cancer predicting algorithm. Now, if you are reading this, and have had successes with using Predictive modeling in different settings other than the ones described above, please let me know. I'd like to hear your stories.
My primary interest is in the Return on Investment of using predictive modeling. I see quite a few software providers out there touting to have predictive modeling capabilities, but haven't heard of a lot of success stories, especially in a healthcare provider setting. Even lesser information is available on the ROI of implementing a predictive modeling solution.
To me, an ultimate predictive modeling solution would be something that can predict the stock market, which has infinite number of variables to consider. But if it were that simple, everyone would be doing it. On the other hand, in healthcare, people are touting Clinical Decision Support capabilities using predictive modeling. "Which patient of yours is most likely to develop cancer?", for example. In my humble opinion, that again is quite a stretch, because of the number of variables that need to be taken into account, not to mention "objective research" that is available to create the model in the first place.
For example, it would be easy to say that a smoker of Asian descent between the ages of 18-40 may develop cancer quicker than others. But what if he is a smoker with healthy eating habits and hits the gym 4 days a week? What if that person only smokes three cigs a day? What if he has no genetic predisposition to cancer? To me, this a cool exercise to conduct and eventually, as you gather more and more data and "evidence" really starts supporting your research in cancer, your model becomes much more reliable and this will start generating a measurable ROI, by reducing the cost of treating a patient through early screening and through preventive medicine.
My thought is that if you are going to do predictive modeling, start with an area with a limited number of variables. Your "bang for the buck" would be realized sooner and it would be greater in that scenario. For example, the scenarios that Alex describes (Capacity Planning and Measured Display Times for Display Stations) have a better chance of an "immediate" ROI than, let's say, a cancer predicting algorithm. Now, if you are reading this, and have had successes with using Predictive modeling in different settings other than the ones described above, please let me know. I'd like to hear your stories.
Tuesday, January 17, 2012
The Informatics M.U.S.E
Recently, I was talking to a customer and he asked what I thought about a comprehensive informatics platform should contain. Currently, our focus is on Healthcare, but as I thought about it, I realized this applies everywhere. So, I said to him, "you have to have your M.U.S.E.". Ok, I am not talking about the Hollywood version with Sharon Stone, but the informatics version. So what is an informatics MUSE?
Measure
You have heard me harping about this over and over again. What are you measuring? Why are you measuring it? When you measure your business (healthcare or others), you understand your business better. So, measure everything that impacts your customers (internal or external)
Utilize
Alright, the second element for you to have a successful implementation, you need to have utilization modules. For example, in healthcare, you measure OR Utilization. Great, now that you know that your utilization is below 100%, what are you going to do about it? How about a OR scheduling module that allows you to maximize your OR scheduling? Utilize the information that you gathered during your Measuring process.
SaaS (Software as a Service) it!
That's right, I said SaaS it. The biggest advantages between a SaaS solution for your business versus you buying the tools and technologies and building it yourself is cost savings and a much faster implementation lifecycle. It puts the focus on "information" and not the technology. Apprehensive about your data being hosted elsewhere? Don't be. Most reputable SaaS solution providers have hardened, HIPAA compliant Data Centers, with 24X7 monitoring capabilities.
Evaluate
That's right. Once your implementation is complete, constantly evaluate your data, decisions you make based on the data and evaluate your performance improvements. This'll allow you to revise your strategies on the go and allow you to make decisions as fast as small companies do.
So there, get your MUSE!
Measure
You have heard me harping about this over and over again. What are you measuring? Why are you measuring it? When you measure your business (healthcare or others), you understand your business better. So, measure everything that impacts your customers (internal or external)
Utilize
Alright, the second element for you to have a successful implementation, you need to have utilization modules. For example, in healthcare, you measure OR Utilization. Great, now that you know that your utilization is below 100%, what are you going to do about it? How about a OR scheduling module that allows you to maximize your OR scheduling? Utilize the information that you gathered during your Measuring process.
SaaS (Software as a Service) it!
That's right, I said SaaS it. The biggest advantages between a SaaS solution for your business versus you buying the tools and technologies and building it yourself is cost savings and a much faster implementation lifecycle. It puts the focus on "information" and not the technology. Apprehensive about your data being hosted elsewhere? Don't be. Most reputable SaaS solution providers have hardened, HIPAA compliant Data Centers, with 24X7 monitoring capabilities.
Evaluate
That's right. Once your implementation is complete, constantly evaluate your data, decisions you make based on the data and evaluate your performance improvements. This'll allow you to revise your strategies on the go and allow you to make decisions as fast as small companies do.
So there, get your MUSE!
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