4 ms·

Does this jibe with what HNers are seeing in their fields? What other quantitative skills do you perceive to have booming demand?

patio11 16y ago
Analytics, web and otherwise.
- illumin8 16y ago
  Right on. I work in healthcare IT and our most revered department is clinical analytics. These people are not technically savvy, but tools like Cognos can turn anyone with some basic math skills into a data analyst. You can literally drag and drop fields from any SQL database and make pivot tables out of them and generate reports. Of course there is a huge difference between someone that can drag and drop fields and someone that knows enough about the underlying data to actually generate good analysis.
  - stevenbedrick 16y ago
    +1, especially to your last sentence. That right there is my biggest medical informatics pet peeve- a lot of hospitals and clinics have "analysts" who are good at using the tools, and maybe even have a good grasp of the data schema of their repositories... but have very limited clinical knowledge, and even more limited knowledge of the workflows that generated their data in the first place. So, what happens is that they generate some numbers without fully understanding the "story" behind them. For example, they might get a request for data concerning the frequency with which patients with condition "X" are treated at their hospital. In most EHR systems, the way to answer questions like this is by using ICD codes... however, there is rarely a 1:1 relationship between what we might think of as a "diagnosis" and a code. Depending on how it's defined, even something seemingly simple such as "Asthma" might be represented in an EHR by (for example) a dozen different codes, and which codes are used can depend heavily on a wide variety of factors: how the EHR's designers implemented the diagnosis system and user interface, how the clinicians were trained to use the system, specifics of how the patient's symptoms presented themselves, how the billing department coded the clinicians' diagnoses, the phase of the moon, etc. etc. etc. As a result, instead of a simple query ("find all patients with ICD code A"), the query ends up looking like "find all patients with codes A, B, C, D, E .... or J; or code K, but only if it co-occurs with L, M, or N; or code O, if the patient was seen in clinic number 4 or 5 between such-and-such dates; etc. etc. etc." And that's for a simple and straightforward clinical question. Imagine if it was something more complex, like "how many patients with condition X also develop condition Y after having treatment Z". Coming up with a query like that takes significant clinical knowledge, but, more importantly, it requires intimate knowledge of the organization that created the data in the first place. It also requires some pretty serious "people skills"- the clinicians that the analyst will be working with to formulate the question will know virtually nothing about computers or databases, and so it will fall on the analyst to work with the clinicians to elucidate the implications and edge cases of the original question. It's kind of like being a detective. This, by the way, is a big part of why it's so hard to get good--- as in, reliable, valid, and comparable--- quality measures from large health care organizations. The data's often way more complex and ambiguous than novices realize, and (speaking from personal experience, here) it often takes people who come from non-clinical backgrounds and are used to more straightforward analytical questions quite a while to realize just how far down the rabbit hole they've gone. What might seem like Of course the fun doesn't stop once our analyst has finally generated some numbers. Whoever wanted the numbers in the first place usually doesn't think much about where they came from (cf. "automation bias"), and as such can go on to make ill-informed decisions as a result of some subtle mistake in the data (i.e., unbeknownst to anybody, the analyst's query missed a whole block of patients coming from a particular clinic, thereby underestimating the prevalence estimates of asthma). This is doubly true when the consumers of the data are generic statisticians (as in, not specialist biostatisticians who are experienced in clinical data analysis) The first commandment of statistics is "Know thy data", and medical data is one of those areas where that's a tricker problem than usual.
    - illumin8 16y ago
      You raise some excellent points. Our software is mainly showing providers and payers how to minimize waste, fraud, and abuse of the system so it requires a huge amount of customization because of each organizations use of coding. The clinical analytics comes in where they can analyze historical data and tell them "you could have saved X amount of money by coding this procedure differently" or "there is no medical reason to do procedure X if you've already done procedures Y and Z, thereby saving XX money." To analyze this data not only requires a statistician's grasp of math, but it requires medical knowledge and organizational knowledge as well. If you were smart enough to be a data/stats geek and also had an MD, plus years of experience working as a doctor in a hospital, I'm sure you are worth your weight in gold as this skill set is very rare.
      - stevenbedrick 16y ago
        It sounds like you guys make really useful software! As you say, MDs who have the skills and are inclined to do this sort of stuff are few and far between. My grad program in medical informatics has a master's track whose graduates are mostly MDs, and they would be quite well qualified for this sort of thing... except that most of them go on to either be CIOs or implementation consultants, and typically make far more than analysts do. I think it's something that they're going to have to start teaching in medical schools, however. As more and more places start taking quality improvement seriously, being able to think systematically about clinical data is going to become a very important skill for doctors to possess. Of course, our experience thus far with trying to get it into the curriculum has not been very encouraging. It's amazing- doctors love trying out new gadgets or drugs, so they're clearly not inherently afraid of technology or of change... but try and get them to modify their curricula, and they look at you like you're crazy.
paraschopra 16y ago
It is still early stages but online marketing and optimization will move from simple reports and metrics to sophisticated modeling of trends and behaviors of visitors. Large ad companies already do a ton of data mining and modeling but increasingly small-medium sized businesses will start leveraging statistics to optimize their businesses and websites.
harry 16y ago
The university I work at just reorganized the administrative structure to put institutional research at it's core. Involved in that mission is data mining, statistics and assessment efforts. We (the IR folk) have been pushing data for years and it's just recently been grabbing the proper attention at the decision making/VP/COO administrator level. I perceive the ability to perform predictive modeling on large data sets being increasingly valuable. E.G., A basic example would be to score how likely a particular student from a set of 4000 high school seniors in a county will be to enroll at one university and achieve a degree. If you agree: learn R, SAS, SPSS and SQL.

patio11 16y ago

Analytics, web and otherwise.

illumin8 16y ago
Right on. I work in healthcare IT and our most revered department is clinical analytics. These people are not technically savvy, but tools like Cognos can turn anyone with some basic math skills into a data analyst. You can literally drag and drop fields from any SQL database and make pivot tables out of them and generate reports. Of course there is a huge difference between someone that can drag and drop fields and someone that knows enough about the underlying data to actually generate good analysis.
- stevenbedrick 16y ago
  +1, especially to your last sentence. That right there is my biggest medical informatics pet peeve- a lot of hospitals and clinics have "analysts" who are good at using the tools, and maybe even have a good grasp of the data schema of their repositories... but have very limited clinical knowledge, and even more limited knowledge of the workflows that generated their data in the first place. So, what happens is that they generate some numbers without fully understanding the "story" behind them. For example, they might get a request for data concerning the frequency with which patients with condition "X" are treated at their hospital. In most EHR systems, the way to answer questions like this is by using ICD codes... however, there is rarely a 1:1 relationship between what we might think of as a "diagnosis" and a code. Depending on how it's defined, even something seemingly simple such as "Asthma" might be represented in an EHR by (for example) a dozen different codes, and which codes are used can depend heavily on a wide variety of factors: how the EHR's designers implemented the diagnosis system and user interface, how the clinicians were trained to use the system, specifics of how the patient's symptoms presented themselves, how the billing department coded the clinicians' diagnoses, the phase of the moon, etc. etc. etc. As a result, instead of a simple query ("find all patients with ICD code A"), the query ends up looking like "find all patients with codes A, B, C, D, E .... or J; or code K, but only if it co-occurs with L, M, or N; or code O, if the patient was seen in clinic number 4 or 5 between such-and-such dates; etc. etc. etc." And that's for a simple and straightforward clinical question. Imagine if it was something more complex, like "how many patients with condition X also develop condition Y after having treatment Z". Coming up with a query like that takes significant clinical knowledge, but, more importantly, it requires intimate knowledge of the organization that created the data in the first place. It also requires some pretty serious "people skills"- the clinicians that the analyst will be working with to formulate the question will know virtually nothing about computers or databases, and so it will fall on the analyst to work with the clinicians to elucidate the implications and edge cases of the original question. It's kind of like being a detective. This, by the way, is a big part of why it's so hard to get good--- as in, reliable, valid, and comparable--- quality measures from large health care organizations. The data's often way more complex and ambiguous than novices realize, and (speaking from personal experience, here) it often takes people who come from non-clinical backgrounds and are used to more straightforward analytical questions quite a while to realize just how far down the rabbit hole they've gone. What might seem like Of course the fun doesn't stop once our analyst has finally generated some numbers. Whoever wanted the numbers in the first place usually doesn't think much about where they came from (cf. "automation bias"), and as such can go on to make ill-informed decisions as a result of some subtle mistake in the data (i.e., unbeknownst to anybody, the analyst's query missed a whole block of patients coming from a particular clinic, thereby underestimating the prevalence estimates of asthma). This is doubly true when the consumers of the data are generic statisticians (as in, not specialist biostatisticians who are experienced in clinical data analysis) The first commandment of statistics is "Know thy data", and medical data is one of those areas where that's a tricker problem than usual.
  - illumin8 16y ago
    You raise some excellent points. Our software is mainly showing providers and payers how to minimize waste, fraud, and abuse of the system so it requires a huge amount of customization because of each organizations use of coding. The clinical analytics comes in where they can analyze historical data and tell them "you could have saved X amount of money by coding this procedure differently" or "there is no medical reason to do procedure X if you've already done procedures Y and Z, thereby saving XX money." To analyze this data not only requires a statistician's grasp of math, but it requires medical knowledge and organizational knowledge as well. If you were smart enough to be a data/stats geek and also had an MD, plus years of experience working as a doctor in a hospital, I'm sure you are worth your weight in gold as this skill set is very rare.
    - stevenbedrick 16y ago
      It sounds like you guys make really useful software! As you say, MDs who have the skills and are inclined to do this sort of stuff are few and far between. My grad program in medical informatics has a master's track whose graduates are mostly MDs, and they would be quite well qualified for this sort of thing... except that most of them go on to either be CIOs or implementation consultants, and typically make far more than analysts do. I think it's something that they're going to have to start teaching in medical schools, however. As more and more places start taking quality improvement seriously, being able to think systematically about clinical data is going to become a very important skill for doctors to possess. Of course, our experience thus far with trying to get it into the curriculum has not been very encouraging. It's amazing- doctors love trying out new gadgets or drugs, so they're clearly not inherently afraid of technology or of change... but try and get them to modify their curricula, and they look at you like you're crazy.

paraschopra 16y ago

It is still early stages but online marketing and optimization will move from simple reports and metrics to sophisticated modeling of trends and behaviors of visitors. Large ad companies already do a ton of data mining and modeling but increasingly small-medium sized businesses will start leveraging statistics to optimize their businesses and websites.

harry 16y ago

The university I work at just reorganized the administrative structure to put institutional research at it's core. Involved in that mission is data mining, statistics and assessment efforts. We (the IR folk) have been pushing data for years and it's just recently been grabbing the proper attention at the decision making/VP/COO administrator level. I perceive the ability to perform predictive modeling on large data sets being increasingly valuable. E.G., A basic example would be to score how likely a particular student from a set of 4000 high school seniors in a county will be to enroll at one university and achieve a degree. If you agree: learn R, SAS, SPSS and SQL.