DHER REPORT – ANALYSIS AND INTERPRETATION
DHER GUIDELINES
-PART 2
WORD REPORT
APRIL 2012
Compiled by Tracey Hattingh, KZN DoH
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DHER REPORT – ANALYSIS AND INTERPRETATION
Table of Contents
1INTRODUCTION
1.1THE PURPOSE OF THE DHER
1.2PROCESS OF THE DHER – add to
2INTERPRETATION AND ANALYSIS
2.1DATA QUALITY
2.2CORE DHIS DATA ELEMENTS
2.3LINKING OF INDICATORS
3DHER REPORT – WORD DOCUMENT
3.1LAYOUT OF DHER WORD REPORT
3.2FORMATING OF DHER WORD REPORT
3.3DHER WORD TEMPLATE / REPORT
ACKNOWLEDGEMENTS
SUMMARY
INTRODUCTION
PRIMARY HEALTH CARE
HIV / AIDS
ENVIRONMENTAL HEALTH
HOSPITALS (DISTRICT & TB)
DISTRICT ALLOCATION AND USE OF RESOURCES IN PROGRAMME 2
DISCUSSION: SUMMARY OF RECOMMENDATIONS
4ALIGNMENT OF DHER & DHP
4.1TRANSLATION OF CHALLENGES BETWEEN DOCUMENTS
4.2RELATIONSHIP BETWEEN DHP & DHER
5ACKNOWLEDGEMENTS
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DHER REPORT – ANALYSIS AND INTERPRETATION
1INTRODUCTION
The DHER paints the picture of how resources are allocated within districts and of financial performance related to pertinent criteria and indicators. Focuses on financial data and links this to other resources such as staff as well as to service delivery and population data.
The DHER Report is made up 3 main data sources, namely BAS, PERSAL and DHIS data. The blending of these three data sources in relation to each other will produce the DHER report which will give a ‘snapshot’ of the district at that point in time.
The District Health Expenditure Review (DHER) is an annual Expenditure Report submitted by each district reporting down to sub-district level for PHC and facility level for hospitals. Currently, the report is only required for Programme 2, 3 & 4, but it is hoped that in the future this will be expanded to include all service performance activities at a district level.
Below is an outline of the Programme and Sub-Programmes as per budget allocation. The programmes and sub-programmes relevant to the DHER are highlighted in red.
Programme 1-Administration
Programme 2-District Health Services
2.1 District Management Expenditure
2.2 PHC Expenditure
2.3CHC Expenditure
2.4 Community Services (i.e. services delivered at the CHC / PHC)
2.5 Other Community Services (i.e. services delivered outside of PHC structure i.e. community care givers)
2.6 HIV / AIDS
2.7 Nutrition
2.8Environmental Health
2.9 District Hospital Expenditure
2.10 Coroner / Forensic Services (not yet included as part of DHER)
Programme 3-Emergency Health Services
Programme 4-Provincial Hospital Services
Programme 5-Central Hospital Services
Programme 6-Health Sciences Training
Programme 7-Health Care Support Services
Programme 8-Health Facilities
Community-based services are accounted for in terms of expenditure under sub-programme ‘2.5 Other Community Services Expenditure’.
1.1THE PURPOSE OF THE DHER
The DHER is used as a justification for resources used within the district in terms of service delivery performance and forms part of the Budget Allocation Process. It is a situational analysis of the district in terms of allocated resources, including personnel, in relation to health outcomes. It ‘holds’ the district management team accountable for service delivery against expenditure at a district level.
- Based on the analysis and interpretation of the data, it will assist the district in identifying challenges / backlogs in the system.
- It can identify misallocated funds / staff so that systems can be amended.
- Cost drivers can be identified and reviewed.
- Can assist the Districts with short-term planning and linkages with the District Health Plans.
- Can improve overall management of District.
- Can improve service delivery performance at a ‘grass-root’s’ level.
- Accountability of District Management.
- Budget allocation – Under-resourced districts must have a higher % increase than the others. Planning of budgets cannot be made by adding the same percentage increase to all sub-districts.
1.2PROCESS OF THE DHER
The DHER process is made of 3 phases all combining to form the DHER Report.
- Initially the raw data is entered into the DHER Excel Workbook which automatically calculates certain indicators.
- Secondly, the data is interrogated and analysed within the context of the district. Operational challenges are noted in the matrix for resolving.
- Thirdly, the interpretation of data is included in the DHER Word Report and a draft submitted to Province for comment.
- Lastly, DHER Word Report is finalised. The DHER Excel Workbook and the DHER Word Report per district are submitted to NDoH as per the PFMA regulations. See diagram below.
Figure 1: DHER Process Flow
Workshops are facilitated by Province for the inputting of the raw data into the Excel Template and on the analysis and interrogation of data.
Below is a process map of the DHER showing the linkage between the DHER Excel Template and the DHER Word Report.
Figure
2: DHER Process Flow
Province provides support to the Districts for the DHER process, however it remains the responsibility of the district to meet with and disseminate information to facility managers. This is an integral part of the Planning, Monitoring and Evaluation Process as feedback is essential.
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DHER REPORT – ANALYSIS AND INTERPRETATION
Figure 2: Diagram of the 3 Sources of Information
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DHER REPORT – ANALYSIS AND INTERPRETATION
2INTERPRETATION AND ANALYSIS
The interpretation and analysis of indicators is an important aspect of the DHER process and cannot be over-emphasised. Without the ability to analyse and interpret data, the DHER holds no value for the District / Province and becomes a compliance based exercise only. It is therefore essential that the District DHER Management Team understand this aspect of the DHER to ensure that the DHER Report is meaningful and appropriate for their district.
2.1DATA QUALITY
Data quality has a HUGE impact on the analysis and interpretation. Future budget allocations, will take the DHER into consideration to identify where extra budget is requiredfor resources and staff.
The integrity of data can be compromised by the completeness of the data. Incomplete data submitted by districts/ facilities can cause discrepancies in data but can also lead to incorrect conclusions being drawn and under-funding of resources. An example has been included to illustrate the impact that incomplete data has.
ILLUSTRATED EXAMPLE: BUDGET ALLOCATED / PDE
In the example below the budget allocation per PDE is explained in detail to emphasis the impact that incorrect / incomplete data has on the planning and budgeting process.
Scenario 1: Incomplete data
In Table 1 is reflected data received from a facility when data was closed off on the 20th May 2011. Itis clearly evident that there is data missing for April, May, June, July, August and September for both Out-Patient Department (OPD) and Emergency Headcounts (EHC). No Day Patients (DP’s) are accepted at this institution.
Table 1: Data received 20th May 2011
Apr / May / Jun / Jul / Aug / Sep / Oct / Nov / Dec / Jan / Feb / Mar / TotalIPD / 2,535 / 3,269 / 2,057 / 2,071 / 1,613 / 2,098 / 2,096 / 1,948 / 2,264 / 2,555 / 2,128 / 2,272 / 26,906
DP / 0 / 0 / 0 / 0 / 0 / 0 / 0 / 0 / 0 / 0 / 0 / 0 / 0
OPD / 0 / 0 / 0 / 0 / 0 / 0 / 2,697 / 2,477 / 2,621 / 2,810 / 2,179 / 2,730 / 15,514
EHC / 0 / 0 / 0 / 0 / 0 / 0 / 205 / 230 / 279 / 227 / 154 / 133 / 1,228
IPD = In-Patient daysOPD = Out Patient Department
DP = Day PatientsEHC = Emergency Headcount
Equation 1: PDE Calculations using data from 20th May 2011
•PDE = (1/3 OPD) + (1/3 Emergency) + (1/2 DP’s) + IPD
•PDE =(15,514 / 3) + (1,228 / 3) + (0 / 2) + 26,906
•PDE = 5,171 + 409 + 0 + 26,906
•PDE = 32,486
•Cost / PDE = R1,200 x 32,486 PDE
•Budget allocated = R 38,983,200
Scenario 2: Complete data
In the scenario below, it is clearly illustrated the difference that complete data makes from both an analysis and a budget point of view.
Table 2: Data received August 2011
Apr / May / Jun / Jul / Aug / Sep / Oct / Nov / Dec / Jan / Feb / Mar / TtlIPD / 1,727 / 1,823 / 2,070 / 2,408 / 1,902 / 1,599 / 1,670 / 1,493 / 1,477 / 1,422 / 1,191 / 1,306 / 20,088
DP / 0 / 0 / 0 / 0 / 0 / 0 / 0 / 0 / 0 / 0 / 0 / 0 / 0
OPD / 7,643 / 7,221 / 10,450 / 10,188 / 7,723 / 14,341 / 2,697 / 2,477 / 2,621 / 2,810 / 2,179 / 2,730 / 73,080
EHC / 195 / 215 / 211 / 221 / 165 / 167 / 205 / 230 / 279 / 227 / 154 / 133 / 2,402
Cells highlighted indicate data change
IPD = In-Patient daysOPD = Out Patient Department
DP = Day PatientsEHC = Emergency Headcount
The entire data set for IPD has decreased from 26,906 in May 2011 to 20,088 in August 2011. The question remains why – was there a miscalculation at facility level. If the hospital originally had 26,906 in-patients where have these patients gone, have they been counted, what is the explanation?
Highlighted cells in OPD and EHC indicate data that was added after the 20th May 2011 cut-off. OPD has increased by 57,566 – is this possible? The 57,566 patients were seen in the 1st 6 months of the year, with only 15,514 seen in the last 6 months? Why?
If the same calculation is redone using the new set of data, the result is as follows.
Equation 2: PDE Calculations using data from 20th May 2011
•PDE = (1/3 OPD) + (1/3 Emergency) + (1/2 DP’s) + IPD
•PDE =(73,080 / 3) + (2,402 / 3) + (0 / 2) + 20,088
•PDE = 24,360 + 801 + 0 + 20,088
•PDE = 45,249
•Cost / PDE = R1,200 x 45,249 PDE
•Budget allocated = R 54,298,800
This is a difference of R 15,3 million per annum, that the facility will be UNDER-FUNDED due to data incompleteness.
Important data elements to review and verify on a monthly / institutional basis as it has a “knock-on” effect for the majority of indicators
- PHC headcount
- IPD
- OPD
- Emergency Headcounts
- Separations
2.2CORE DHIS DATA ELEMENTS
In relation to the DHER report, a list of five core data elements have been identified. These 5 data elements need to be reviewed from a data management perspective to ensure the completeness and validity of the data as these data elements are utilized in many indicators throughout the DHER report.
The consequence of incorrect data will have a profound knock-on effect on the analysis and interpretation of data in the DHER and will impact on recommendations made. It is therefore essential that these core data elements are correct to ensure accurate analysis.
- PHC Headcount
- Total Separations
- Total OPD Headcount
- Day Patients
- Total In-Patient Days
2.3LINKING OF INDICATORS
In the development of these guidelines the linking of indicators has been discussed in various sections of the document including the section below and also in the section titled “DHER REPORTING TEMPLATE FOR WORD DOCUMENT”. This is done for ease of referencing as the relationship between indicators is dependent on the expected outcome of the analysis. Therefore the same indicator could be repeated used in the analysis but within a different context (in relation to a different set of indicators).
An example would be the workload per PN. If this indictor is reviewed in relation to the Cost per PHC Headcount it would be from a Sustainability of Services perspective. However if this same indicator was compared between facilities, it would be from an Equity of Resources perspective. This same indicator, workload per PN, could be reviewed in conjunction with the National / Provincial norm and as such it would relate to the Efficiency (quality) of services.
The discussion on the linking of indicators has been placed within the context of the subject being discussed. In the chapter entitled “DHER REPORTING TEMPLATE FOR WORD DOCUMENT” the linking of specific indicators for a specific outcome has also been included to act as a basis for the analysis of that narrative.
A broader discussion of the relationship between indicators appears in the table below and outlines some linkages between various indicators. This is not an all-inclusive list and is designed as a guide only.
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DHER REPORT – ANALYSIS AND INTERPRETATION
Table 3: Linkages with Service Delivery (Activity) Indicators
Prime Indicator / Link with other indicators / Further Links / Rationale / ScenarioPHC Headcount / Utilisation Rate / Cost per Visit / The theory or concept of “Scale of economies”[1] states that the more patients you treat, the cheaper it should be to treat the patients.
Example: If cost per visit ↑high, utilisation should be ↓low with a comparatively ↓low PHC headcount.
Example: If cost per visit is ↓low, utilisation should be ↑high with a comparatively ↑ headcount.
Investigate: if cost per visit high, with high utilisation and high headcount or if cost per uninsured low, with low utilisation and low headcount.
Utilisation Rate / There is definite link between the PHC headcount and the utilisation rate. This can be carried forward into target setting and projections. If the utilisation rate is to be increased to 2.4, then the population data can be used to project the PHC headcount, and vice versa.
PHC Budget Allocated / If the cost per headcount is low and over-expenditure has occurred when reviewed against the budget, it could mean that clinics and CHC’s are under-funded.
If the cost per headcount is high and over-expenditure has occurred when reviewed against the budget, it could mean that there is a need for more stringent efficiency measures. The comparison should ideally take place over a 3 year period to identify if there has been a spike in expenditure and if this is due to an increased utilisation rate or poor management.
OPD Headcount / If there is a decrease in PHC headcount, look for an increase in OPD headcount as patients sometimes prefer to be seen at hospital level due to quick referrals to doctors, perceived better service etc. However it stands to reason that as services start to improve at PHC level, the PHC headcounts should increase, so the Non-referred OPD headcount should decrease.
Investigate: Big increase in PHC headcount coupled with increase in OPD.
R / PHC / If the R/PHC visit is high comparatively, it indicates that there has been high expenditure with low headcounts.
Utilisation Rate / Ambulatory Ratio / OPD Headcount / There is a correlation between the utilisation rate, the Ambulatory Ratio and OPD Headcount as if the PHC system is working, the utilisation rate will be high, the ambulatory ratio should be low, as should the OPD Headcount not referred.
Investigate: If the utilisation rate is low, the ambulatory ratio is high (above 1.5) and the OPD headcount is high, this indicates that the PHC system is not functioning correctly.
R / Capita / - / This compares the utilisation of the services against the cost per person (capita) to render those services. Therefore if the utilisation rate is high, there will be more visits to the clinic and therefore the cost per capita should be higher than districts with lower utilisations but similar PHC expenditure.
Investigate: If the utilisation rate is comparatively high, but the cost per capita is low, the situation / data should be further investigated.
R / PHC visit / Budget Allocated budget for PHC / To determine if the budget allocated is / was sufficient. For demonstration purposes, following data utilised:-
- utilisation rate is 3.5,
- population is 50000,
- cost per PHC visit is R 100
2nd Calculation: 175000 xR 100 = R 17,500,000 budget
Cost per uninsured / Allocated budget for PHC / To determine if the budget allocated was / is sufficient. For demonstration purposes the following data has been used;-
- R 150 cost per uninsured
- Population is 50000
Ambulatory Ratio / OPD Headcount / If the ambulatory ratio is higher than 1.5, this needs to be reviewed in conjunction with the PHC utilisation rate to identify if the population is accessing health services at clinic level or at hospital level. If the population is by-passing clinics, further investigation is required to identify the root cause i.e. poor staff attitudes to patients, poor accessibility of services, opening times limited etc.
Ratio of ambulatory / If the ambulatory ratio is high (i.e. <2) it means that the PHC system is not working. It should be less than 1 but in KZN less than 1.5 is about average. If higher, it needs to be reviewed, see indicator below.
R / PHC visit / PHC Headcount / - / Link between the PHC headcount and the R/ PHC Visit. If the Headcount is high then the cost per visit should be lower due to economy of scales.
% Drug Expenditure / HIV / TB Prevalence / Drug expenditure makes up a high % of the R / PHC visit review in relation to HIV / TB prevalence.
Example: If the drug expenditure % is high for the R / PHC visit in Umzinyathi, look at the number of MDR TB cases as TB expenditure is under programme 2 and the MDR programme is facilitated at community level.
% CoE / - / The proportion of the CoE for R / PHC visit reflects the sustainability of the workforce. A high CoE is unsustainable however; the CoE cost in rural areas should be higher when compared to urban areas due to rural allowance paid.
If a there are a high number of PHC doctors also allocated to PHC this would increase the CoE % proportion of the R / PHC visit.
% CoE / Workload / Review the cost of CoE per R / PHC visit in relation to the workload. If there is a high CoE % but a low workload further investigation is required. It could be that staff are not reflected on the FTE table but are being paid at clinic level. It could also indicate that staff are being paid at the clinic, which are not working at clinic level.
ALOS / BUR / - / Correlation between the ALOS& BUR. Normally if the ALOS is high, the BUR is low and vice versa. This is because the BUR has the separations as a data element, therefore the more separations there are the shorter the ALOS, the higher BUR due to the increase in both separations and In-Patient Days.
Investigate: If both the ALOS and the BUR are high (or low) further investigations are required. It is recommended that this be done at ward level.
IPD / - / If the ALOS is high, with a high number of IP Days it means that the hospital BoD is more chronic / step-down than acute. Investigation: In this instance ward data per month should be reviewed to try and identify where the actual challenge lies.
FTE Dr’s / - / There is a correlation between the ALOS, the number of doctors available for clinical rounds, and the competency of the doctors employed by the hospital.
Scenario 1: Generally, newly qualified doctors lack the confidence of the more experienced doctors when it comes to diagnosis and therefore keep the patients longer while over prescribing in medication, and running more tests than economically viable.
Scenario 2: If there is a shortage of doctors to do clinical rounds, the patients stay longer while waiting to see a doctor. A shortage of radiographers can also have an influence on the ALOS as patients stay longer while waiting for radiography services.
C/S / FTE Dr’s / The ALOS should increase if there is a higher C/S rate. The C/S rate could be affected by the competency and skills of the attending doctors. More inexperienced doctors tend to perform more c/s and/or refer to higher levels of care. Therefore the more community service doctors there are, the chances are likely of a higher c/s rate which will influence the ALOS as it takes a longer time to recuperate than natural births.