System use indicators
Work in progress! Please commend and add ideas. Some questions we have:
How would these data be visualized or put into a dashboard?
What do we expect to change (statistically speaking to show a true difference)?
Let's refine these KPIs (what are numerators and denominators)? How do the KPIs match with the what do we want to know? How can we make these practical and in-line with what's possible to measure?
How can we partner with the Analytics squad as part of the implementation?
How do we relate indicators to clinical areas to make these data more actionable?
Who would be using these indicators?
Reality checks on data pipeline for all indicators:
whether the ETL runs or even exists for the systems being measured (to distinguish whether data is really missing or the data pipeline has not been released at a facility)
system up/downtime, noting that downtime has to be measured by external system
PRIORITY INDICATORS
what we think is currently feasible & useful
What do we want to know? | KPIs & analytics | Calculation | What can we do with this information? | Data Collection Requirements & Source | Open Questions |
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End-user focus |
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Do we see a stable pattern of use by expected end users?
| Line graph of week on week average logins per user per week by role and Provider type | (Total number logins by role) / (Total users in a role) | With local user knowledge, we can look for concering patterns of discontinued use, or of use only by a fraction of expected end users, or patterns of use that are not consistent with point of care and timely data entry. We can investigate and resolve training, local prioritization, IT infrastructure etc. barriers to adoption. |
| Is O3 storing user logins? PIH team tracks & how can we leverage
What happens if many inactive users |
Histogram of average logins per week per user by role for the last month, 6 months |
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Logins by hour and week day |
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Is there regular and prompt (encounter) data entry and by what type of user? |
| New encounter forms | Identify data timeliness issues that may indicate barriers to the data entry workflow and work with local colleagues to resolve them.
| Encounter table (suggestion)
| Value of this indicator depends on user accounts being accurately categoried to distinguish between data clerks and users who may enter data point of care, or other clinician data entry. Is it possible to distinguish new encounters vs. updates? Is the encounter date really the most recent update? |
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Registrations |
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Is there a regular pattern of patient registration and visits that indicates regular use as the system of record for patient registration. |
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| Confirm that activity matches information we have about the current catchment area and clinical capacity. |
Patient table with
| Can we tell whether a registration has been updated? |
Patient Disposition |
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| Where is it measured: program outcome, disposition, marked outcome in system |
Clinical workflow focus | |||||
Is the check in feature being used to initiate the patient visit workflow? |
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| Inconsistent use indicates that there may be other systems (paper, Excel) used to manage patient visits |
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Is patient data being entered regularly and completely? Encounter table |
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| We can see if certain forms or form sections seem to be underused. |
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Is patient data important for continuing care and clinical program being added regularly Dispensing table Diagnoses table
| Statistics TBD on
Likely averages per week or per visit, per patient |
| Determine if these features are being used to the level consistent with other system use, if not, this indicates gaps in implementation where other systems may be used (such as registers) as the system of record for these key aspects of the patient medical record |
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Are clinical programs being used to track patient status Program table |
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| Determine if clinical program features are being used consistently and matching the scale expected for each program. Inconsistent or low patterns of use indicate that data entry is delayed, likely from a separate system of record. |
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Data completeness and quality | |||||
Is the data entered as completely as expected in order to:
| Field-by-field analysis dashboard (like PIH created) This is specific to individual forms in the EMR. | % blank per form field (observation) per encounter type or form | Shows which clinical elements of the system are most in use or ignored Review these indicators with the clinical leads to reduce fields/complexity and data entry, data prioritiziation Examples:
| This comes directly from encounter/observation data from the forms being monitored for completeness. | See Field-by-field analysis done by PIH in PowerBI |
Data entry values | Histogram about the frequencies of multiple choice or specific values |
| Are defulat values overrepresneted |
| Considering required and coded fields, and checking to see if default values are overrepresented. |
Is data in plausible range? |
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Data use focusNote, this excludes use of data from the reporting DB such as for HIV and MCH. |
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Are people using the system data exports for reporting? |
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| Monthly (or less frequent) access to priority exports indicates possible missed potential for timely and internal use for regular monitoring and decision making. |
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Are people directly querying the db? |
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Are people accessing system data via API? |
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Data Quality → added to completness & quality | |||||
Is data in plausible range? |
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FUTURE OPTIONS
what we dream up
What do we want to know? | KPIs & analytics | Calculation | What can we do with this information? | Data Collection Requirements & Source | Open Questions |
What is the most common use of the system? | Top 3/5/10 next “clicks” or actions, disaggregated by user role. |
| Determine what most people are actually doing in the system - searching for a patient? Adding a form? |
| Remove to future ideas, data not available. |
Is there frequent access to view patient summaries during clinic hours by clinicians? What do clinicians look at (click on) on patient summaries? |
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| Understand if the system is being used to inform clinicians about patient status, possibly at point of care |
| Remove to future ideas and request technical analysis for capturing patient summary views. |