Healthcare analytics guide for practice managers

A guide on how to grow your medical practice with healthcare analytics – whether you use software or spreadsheets, here’s a starting point.

Categorized as Operations
Healthcare analytics for practice managers
Healthcare analytics for practice managers

The best way to measure performance improvement opportunities in your practice is to analyze your current business. You could be looking to improve your financial performance. Or, you could be thinking of improvement opportunities in your marketing performance. You could currently be using software or spreadsheets. Below is a brief guide on how to get started with healthcare analytics.


Descriptive healthcare analytics 

We start with descriptive analytics ( i.e. what happened?) and follow the same model that Gartner had proposed.

The key areas I look at are

  1. Financial analytics
  2. Practice management
  3. KPIs
  4. Marketing /business development

For financial analytics, I start with some basic numbers over the last 4-5 years.

These include

  1. Billing performance trends over the years
  2. Payment performance trends over the years
  3. Adjustment trends over the years
  4. Reimbursement per procedure over years, accounting for new lines of services that might have been added
  5. Net collection rate. This is a bit subjective and can really vary based on the practice’s service pricing that has been set before we come into the picture.
  6. Bad debt write off trends
  7. Trends of days in Account Receivables (A/R)
  8. A/R Balance trends over years
  9. % A/R over 120 days – 120 days are an important time span
  10. Denials – broken down into preventable vs non-preventable
  11. Unbilled amount trends over the years

For practice management analytics, I look at factors like

  1. Trend of visits per day (work day as some of our customers are open only 5 days a week)
  2. Trends of appointment cancellation rates over years
  3. Appointment no-shows trends over years
  4. Eligibility verification completion rates (this ties into preventable denials as well)
  5. Billing lag trend (time between visit and claims submission dates)
  6. Payment lag trend (time between payment made vs the visit date)
  7. Patient wait time / visit time (wait time is harder to gauge but we can look at patient visit times) trends
  8. Unfulfilled Encounters trends over years (i.e. encounters that have not been finalized or completed)
  9. Provider utilization

The main KPIs I look at are

  1. First pass resolution rate trends (that tells us about claims that get paid on first submission itself)
  2. how many claims get denied immediately upon submission – i.e the First pass denial rate trends as this tells us the opposite
  3. First pass no-response rate trends because this tells us which payers to have further escalations with (there’s nothing that our customers’ billing team is doing wrong in this KPI)
  4. Revenue realization rate trends this tells us the percentage of total allowed reimbursement collected out of the total billed amount (this, unfortunately is why many practices seek out our help with medical billing)
  5. Gross collection rate – this is also subjective to some extent as the practice can set whatever pricing they want to, however, the point of this is to show how much the practice is collecting on each dollar submitted in claims. 
  6. Net collection rate tells us about the monies collected against the monies expected to collect (not the submitted amount)
  7. Same day encounter close rate trends as this helps us identify the bottleneck from the providers’ side – how long they take to close the notes

For business development/marketing side of the business I look for the following:

  1. New appointment rates
  2. New appointment sources / channels
  3. Appointment request to appointment given timelines
  4. Call abandonment rate (inbound)
  5. Average time in queue (inbound)
  6. Service levels (percentage of inbound calls answered within a specific time in seconds)
  7. Average speed to answer inbound calls
  8. Handle time (be careful about defining smaller handle time as better – that doesn’t quite work well in good customer service and certainly not in healthcare)
  9. After call work time (this shows you very good insights into how much manual after call work agents have to do, and thereby increasing the load on inbound queue wait times for callers).
  10. Customer satisfaction with the call 
  11. Occupancy rate of agents for inbound calls
  12. First call resolution (although, we have found that sometimes patients call back more than once a day for no fault of the agents)
  13. Peak hour traffic and longest wait times
  14. Number of calls made per day per agent
  15. Total number of connects made per day per agent
  16. Number of conversions per day per agent
  17. Total number of calls per agent per patient
  18. Call quality metrics
  19. Referring physician distribution
  20. Referrals trend per doctor / health system

There are many more factors we look for, but these give us a headstart on analyzing how to optimize a healthcare practice’s growth.

Example of how I used descriptive analytics with an ophthalmology group

When we started managing this practice, we were at 4 provider FTEs.

A good study on numbers expected for a typical ophthalmology group can be found here on healio.

The salient points to note are:

  1. Net collections (gross collections less refunds) = between $120 and $180 per patient encounter. 
  2. Most general ophthalmologists will collect between $600,000 and $900,000 / year. 
  3. Net collections = 95% to 100% of net revenue.
  4. Account receivables (A/R) balance = between 35 days and 50 days.
  5. Total account balances less than 30 days old = 40% to 60% of the total A/R.
  6. Account balances between 31 and 60 days old = 15% to 25% of the total A/R
  7. Total account balances greater than 120 days are 10% to 20% of the total A/R.
  8. Practice overhead/expenses = between 45% and 65% of net collections.
  9. Headcount = 5-7 FTEs per physician. Out of this, front office staff = 1.5 – 2 FTEs, back office staff = 3.0 – 3.5 FTEs, business office staff = 1 and 1.5 FTEs, and administrative staff at 0.5 FTEs. 
  10. Staff member compensation = between 17% and 24% of net collections. 
  11. Revenues per employee = $100,000 – $150,000 per FTE staff member.
  12. Patients seen per provider per day =  31 – 50.
  13. New patients = 20% to 30% of all encounters. 
  14. No-show appointments for existing patients = < 2% of the total.
  15. No-show appointments for existing patients = < 3% for new patients. 
  16. Patient cancellations = < 2% of all appointments.
  17. Patient visit time = 47-50 mins (routine exam). This is broken down into 15 minutes from patient check-in to starting the technician exam. The technician workup = 15 minutes (max), followed by a 10-minute wait for the physician exam. The physician exam = 5 – 8 minutes, around 2 minutes for patient checkout (changes based on patient balances). 
  18. Time taken for new patients to get on the schedule = < 10 days. 
  19. Time taken for existing patients to get on the schedule = < 14 days.
  20. Cataract and refractive surgeries scheduled = < 3 days.
  21. Other surgical procedures scheduled = < 5 days. 
  22. Emergency services scheduled = < 1.5 hours of the call.

Calculating basic business numbers

  1. Within a month of starting our engagement with this ophthalmology group, we added another 3 FTE providers. This meant a total of 7 provider FTEs, expected to collect $800K collections / provider / year * 7 providers  = $5.6 MM collections / year
  2. The total appointment slots available for booking were 40 encounters / day / provider * 7 providers = 280 encounters / day.
  3. Upon no-show analysis for this ophthalmology group, we found it to be 30%.
  4. To have 280 billed encounters / day, we needed to have 280 / (100 – 30%) = 400 booked appointments (accounting for no-shows).
  5. Since this ophthalmology group is open on Saturdays as well, this means that the business has to generate 400 appts / day * 24 days/month = 9,600 booked appointments.

Starting with baseline numbers

Here are the baseline numbers that we started our analytics with.

2016 appointments breakdown


2017 appointments breakdown


2018 appointments breakdown

2018 numbers until Sept

  • 25,473 total Appointments
  • 6,677 Cancelled
  • 292 no shows
  • 12,092 confirmed
  • 12,092 new patients
  • 2017 numbers

2018 final numbers for reference


2019 numbers – after we started our work with the group in June 2019


Conversions & utilization data

2018 data until September.

  • (6,677 Cancelled + 292 no shows) / 25,473 total Appointments.. 73% conversion
  • 4 docs, 18,500 appointments over 36 weeks (180 – 200 days) = 20-23 appointments per provider per day (78 % utilization)

2017 data

  • (7,408 Cancelled + 817 no shows) / 27,727 total Appointments.. 70% conversion
  • 4 docs, 19,500 appointments over 50 weeks (250 – 300 days) = 16 appointments per doc per day (50 % utilization)

New appointment sources

Well, let’s look at where our appointments come in from

  • Call-in w/o Referral
  • Call-in w/ Referral
  • Walk-in w/o Referral
  • Walk-in w/ Referral
  • Referrals we call
  • Zocdoc
  • Patient recall (or reactivation) – found out that the group had zero
  • Organic (Website) – found out that the group had zero
  • Paid media (ads/PPC) – found out that the group had zero
  • Earned media (mentions) – found out that the group had zero
  • Now, let’s look at the breakdown of “Referrals we call” (keep in mind only Appointments made end up in our EPM – the rest are LOST)

Looking at the EMR data, we found out that more than 90% of patient appointments were from physician referrals.

The group had no data on the following…

  • Referral outcome
  • Referrals that didn’t convert to appointments
  • Top referrers / referral volume by provider/facility
  • Splitters (Physicians referring to competitors as well as us)
  • Loyals – ours, Loyals – competitors’

Next phase is diagnostic healthcare analytics

This is where we figure out why it happened.

First up was our short term “referral pad” opportunities to improve

  • Research shows 35-45% of referrals actually reach us. I.e out of 100 “referral pad” referrals, 40 patients call us.
  • Assuming half of all appointments were from faxed/called-in referrals
  • This means that for 2017, out of 28K appointments, about 14K were from referrals sent via referral pad. 
  • This meant that 14K/40% = 35,000 referrals from physicians were made (and only 40% reached us to make an appointment)
  • We missed (35K – 14K) = 21K referrals/leads.

Why did we miss these many potential appointments?

Referral pads have issues.. When you have referral pads (that’s how business runs really), we don’t know

  • Who is sending us referrals
  • How many patients they are sending us everyday
  • How many patients are not calling to make an appointment

Next up, let’s look at “faxed referrals” opportunities

  • Assuming the other half of all appointments were from faxed/called-in referrals
  • Our staff claims they convert only about 20-30% of incoming referral faxes/calls into actual patient appointments.
  • This means we were sent 14K/25% = 56,000 faxed/called in referrals from physicians.
  • We missed (56K – 14K) = 42K referrals/leads were lost.

Why did we miss these many potential appointments?

Then again, there’s faxed or called-in referral issues to deal with..

  • Typos or wrong info and we need to call each patient (no one dedicated to doing this).
  • Staff reaches only 20-40% of patients on phone (voicemails). Call pending volume adds up per week.
  • Too many missed leads/opportunities.

A simple look at our backlog looks like this for “faxed referrals” – (sample weekly backlogs.. just numbers to show what backlog can turn into)

diagnostic healthcare analytics - appointment backlog class= lazyload

Next up on our list was to look at “Cancellations” – opportunities.. In other words, how many of these cancellations can be converted back to appointments on the books? Why did we miss these many potential appointments?

  • Are we rescheduling cancelled appointments when they call? NOPE
  • Are we calling cancelled appointments later to reschedule? NOPE
  • Missed opportunities based on 2017 and YTD data (6,677 Cancelled + 7,408 Cancelled)

Are we maximizing patient recalls/reactivations? (are we doing any at all?)

  • Pre-schedule phone calls – YES
  • SMS based recalls /reactivations – NOPE
  • Postcards/mailers – NOPE
  • Increase points of contact (takes avg 5-7 contacts for a patient to notice you) – not really, due to lack of time.

After that predictive healthcare analytics comes in

This is where we model data and figure out what will happen if we do take up the steps we discovered and recommended.

Our first goal was to provide immediate tangible value.

The practice was obviously bleeding and a lot could be done to improve the provider utilization, patient pipeline. So, instead of spending a bulk of our time on optimizing the practice numbers, we decided to concentrate on increasing the patient pipeline (as they say “sales solves everything”)

Based on the above new appointment sources, the options to consider were:

  1. Increase the number of appointment requests generated by digital marketing (adwords)
  2. Increase the number of referrals from our physician partners.
  3. Reduce the no shows (tough one to do with our customer’s immigrant patient population)
  4. Convert more of the incoming referrals into patient appointments.
  5. Run a patient reactivation campaign to get some of the patients.

What value do we think it is going to bring to the ophthalmology group?

Missed opportunities based on 2017 and 2018 YTD data (6,677 Cancelled + 7,408 Cancelled)

  • Best case – 100% conversion = 14K appointments (new)
  • Worst case – 20% conversion = 2,800 appointments (new)

We turn to looking into “Patient recalls” – opportunities. According to our EMR – 24,004 patients (lifetime – it’s a young practice really).

What are our Patient recall / reactivation opportunities here? Basic back of napkin numbers tells us

  • Best case of 50 % recall – 12K new appointments
  • Worst case of 5% recall – 1.2K new appointments

So, based on a few of our recommendations, what impact COULD we have?

  • Referral pad opportunities – 20K referrals/leads.
  • Faxed referrals opportunities – 42K referrals/leads.
  • Cancellations opportunities – 14K leads
  • Patient recalls opportunities – 24K leads
  • No shows – TBD

By doing these.. What can we expect?

  • Net-net = 100K new potential appointments
  • Can we convert 10%? (uplift of 10% is reasonable)
  • Can we get a 10K uplift on new appointments?
  • Can we improve our top line by 35%?

Our goal is prescriptive healthcare analytics – the highest level of maturity

Not many healthcare practices get to this level due to various constraints (clinical, operations, budgetary etc). However, this is where we can start optimizing a practice’s operations, prescriptively.