Performance-Based Financing: Report on Feasibility and Implementation Options Final September 2007

This study examines the feasibility of introducing a performance-related bonus scheme in the health sector. After describing the Tanzania health context, we define “Performance-Based Financing”, examine its rationale and review the evidence on its effectiveness. The following sections systematically assess the potential for applying the scheme in Tanzania. On the basis of risks and concerns identified, detailed design options and recommendations are set out. The report concludes with a (preliminary) indication of the costs of such a scheme and recommends a way forward for implementation. We prefer the name “Payment for Performance” or “P4P”. This is because what is envisaged is a bonus payment that is earned by meeting performance targets1. The dominant financing for health care delivery would remain grant-based as at present. There is a strong case for introducing P4P. Its main purpose will be to motivate front-line health workers to improve service delivery performance. In recent years, funding for council health services has increased dramatically, without a commensurate increase in health service output. The need to tighten focus on results is widely acknowledged. So too is the need to hold health providers more accountable for performance at all levels, form the local to the national. P4P is expected to encourage CHMTs and health facilities to “manage by results”; to identify and address local constraints, and to find innovative ways to raise productivity and reach under-served groups. As well as leveraging more effective use of all resources, P4P will provide a powerful incentive at all levels to make sure that HMIS information is complete, accurate and timely. It is expected to enhance accountability between health facilities and their managers / governing committees as well as between the Council Health Department and the Local Government Authority. Better performance-monitoring will enable the national level to track aggregate progress against goals and will assist in identifying under-performers requiring remedial action. We recommend a P4P scheme that provides a monetary team bonus, dependent on a whole facility reaching facility-specific service delivery targets. The bonus would be paid quarterly and shared equally among health staff. It should target all government health facilities at the council level, and should also reward the CHMT for “whole council” performance. All participating facilities/councils are therefore rewarded for improvement rather than absolute levels of performance. Performance indicators should not number more than 10, should represent a “balanced score card” of basic health service delivery, should present no risk of “perverse incentive” and should be readily measurable. The same set of indicators should be used by all. CHMTs would assist facilities in setting targets and monitoring performance. RHMTs would play a similar role with respect to CHMTs. The Council Health Administration would provide a “check and balance” to avoid target manipulation and verify bonus payments due. The major constraint on feasibility is the poor state of health information. Our study confirmed the findings of previous ones, observing substantial omission and error in reports from facilities to CHMTs. We endorse the conclusion of previous reviewers that the main problem lies not with HMIS design, but with its functioning. We advocate a particular focus on empowering and enabling the use of information for management by facilities and CHMTs. We anticipate that P4P, combined with a major effort in HMIS capacity building – at the facility and council level – will deliver dramatic improvements in data quality and completeness. We recommend that the first wave of participating councils are selected on the basis that they can first demonstrate robust and accurate data. We anticipate that P4P for facilities will not deliver the desired benefits unless they have a greater degree of control to solve their own problems. We therefore propose - as a prior and essential condition – the introduction of petty cash imprests for all health facilities. We believe that such a measure would bring major benefits even to facilities that have not yet started P4P. It should also empower Health Facility Committees to play a more meaningful role in health service governance at the local level. We recommend to Government that P4P bonuses, as described here, are implemented across Mainland Tanzania on a phased basis. The main constraint on the pace of roll-out is the time required to bring information systems up to standard. Councils that are not yet ready to institute P4P should get an equivalent amount of money – to be used as general revenue to finance their comprehensive council health plans. We also recommend that up-to-date reporting on performance against service delivery indicators is made a mandatory requirement for all councils and is also agreed as a standard requirement for the Joint Annual Health Sector Review. P4P can also be applied on the “demand-side” – for example to encourage women to present in case of obstetric emergencies. There is a strong empirical evidence base from other countries to demonstrate that such incentives can work. We recommend a separate policy decision on whether or not to introduce demand-side incentives. In our view, they are sufficiently promising to be tried out on an experimental basis. When taken to national scale (all councils, excepting higher level hospitals), the scheme would require annual budgetary provision of about 6 billion shillings for bonus payments. This is equivalent to 1% of the national health budget, or about 3% of budgetary resources for health at the council level. We anticipate that design and implementation costs would amount to about 5 billion shillings over 5 years – the majority of this being devoted to HMIS strengthening at the facility level across the whole country

From the frontline: strengthening surveillance and response capacities of the rural workforce in the Asia-Pacific region. How can grass-roots implementation research help?

Health systems in the Asia-Pacific region are poorly prepared for pandemic threats, particularly in rural/provincial areas. Yet future emerging infectious diseases are highly likely to emerge in these rural/provincial areas, due to high levels of contact between animals and humans (domestically and through agricultural activities), over-stretched and under-resourced health systems, notably within the health workforce, and a diverse array of socio-cultural determinants of health. In order to optimally implement health security measures at the frontline of health services where the people are served, it is vital to build capacity at the local district and facility level to adapt national and global guidelines to local contexts, including health systems, and community and socio-cultural realities. During 2017/18 James Cook University (JCU) facilitated an implementation research training program (funded by Australian Department of Foreign Affairs and Trade) for rural/provincial and regional health and biosecurity workers and managers from Fiji, Indonesia, Papua New Guinea (PNG), Solomon Islands and Timor-Leste. This training was designed so frontline health workers could learn research in their workplace, with no funding other than workplace resources, on topics relevant to health security in their local setting. The program, based upon the WHO-TDR Structured Operational Research and Training IniTiative (SORT-IT) consists of three blocks of teaching and a small, workplace-based research project. Over 50 projects by health workers including surveillance staff, laboratory managers, disease control officers, and border security staff included: analysis and mapping of surveillance data, infection control, IHR readiness, prevention/response and outbreak investigation. Policy briefs written by participants have informed local, provincial and national health managers, policymakers and development partners and provided on-the-ground recommendations for improved practice and training. These policy briefs reflected the socio-cultural, health system and disease-specific realities of each context. The information in the policy briefs can be used collectively to assess and strengthen health workforce capacity in rural/provincial areas. The capacity to use robust but simple research tools for formative and evaluative purposes provides sustainable capacity in the health system, particularly the rural health workforce. This capacity improves responses to infectious diseases threats and builds resilience into fragile health systems

Malaria mosquito resistance to agricultural insecticides: risk area mapping in Thailand

Malaria / Disease vectors / Waterborne diseases / Irrigated farming / Pest control / Insecticides / Public health / Risks / Mapping / GIS / Land use / Thailand / Chiang Mai / Mae Hong Son / Tak / Kanchanaburi

Transactions of the First International Conference on Health Information Technology Advancement vol. 1, no. 1

Full proceedings of The First International Conference on Health Information Technology Advancement held at Western Michigan University in Kalamazoo, Michigan on October 28, 2011. Conference Co-Chairs: Dr. Bernard Han, Director of the Center for HIT Advancement (CHITA) at Western Michigan University Dr. Sharie Falan, Associate Director of the Center for HIT Advancement (CHITA) at Western Michigan University Transactions Editor: Dr. Huei Lee, Professor in the Department of Computer Information Systems at Eastern Michigan Universit

Antibiotic resistance in primary care in Austria - a systematic review of scientific and grey literature

<p>Abstract</p> <p>Background</p> <p>Antibiotic resistance is an increasing challenge for health care services worldwide. While up to 90% of antibiotics are being prescribed in the outpatient sector recommendations for the treatment of community-acquired infections are usually based on resistance findings from hospitalized patients. In context of the EU-project called "APRES - the appropriateness of prescribing antibiotic in primary health care in Europe with respect to antibiotic resistance" it was our aim to gain detailed information about the resistance data from Austria in both the scientific and the grey literature.</p> <p>Methods</p> <p>A systematic review was performed including scientific and grey literature published between 2000 and 2010. Inclusion and exclusion criteria were defined and the review process followed published recommendations.</p> <p>Results</p> <p>Seventeen scientific articles and 23 grey literature documents could be found. In contrast to the grey literature, the scientific publications describe only a small part of the resistance situation in the primary health care sector in Austria. Merely half of these publications contain data from the ambulatory sector exclusively but these data are older than ten years, are very heterogeneous concerning the observed time period, the number and origin of the isolates and the kind of bacteria analysed. The grey literature yields more comprehensive and up-to-date information of the content of interest. These sources are available in German only and are not easily accessible. The resistance situation described in the grey literature can be summarized as rather stable over the last two years. For <it>Escherichia coli </it>e.g. the highest antibiotic resistance rates can be seen with fluorochiniolones (19%) and trimethoprim/sulfamethoxazole (27%).</p> <p>Conclusion</p> <p>Comprehensive and up-to-date antibiotic resistance data of different pathogens isolated from the community level in Austria are presented. They could be found mainly in the grey literature, only few are published in peer-reviewed journals. The grey literature, therefore, is a very valuable source of relevant information. It could be speculated that the situation of published literature is similar in other countries as well.</p

Extracting information from the text of electronic medical records to improve case detection: a systematic review

Background: Electronic medical records (EMRs) are revolutionizing health-related research. One key issue for study quality is the accurate identification of patients with the condition of interest. Information in EMRs can be entered as structured codes or unstructured free text. The majority of research studies have used only coded parts of EMRs for case-detection, which may bias findings, miss cases, and reduce study quality. This review examines whether incorporating information from text into case-detection algorithms can improve research quality. Methods: A systematic search returned 9659 papers, 67 of which reported on the extraction of information from free text of EMRs with the stated purpose of detecting cases of a named clinical condition. Methods for extracting information from text and the technical accuracy of case-detection algorithms were reviewed. Results: Studies mainly used US hospital-based EMRs, and extracted information from text for 41 conditions using keyword searches, rule-based algorithms, and machine learning methods. There was no clear difference in case-detection algorithm accuracy between rule-based and machine learning methods of extraction. Inclusion of information from text resulted in a significant improvement in algorithm sensitivity and area under the receiver operating characteristic in comparison to codes alone (median sensitivity 78% (codes + text) vs 62% (codes), P = .03; median area under the receiver operating characteristic 95% (codes + text) vs 88% (codes), P = .025). Conclusions: Text in EMRs is accessible, especially with open source information extraction algorithms, and significantly improves case detection when combined with codes. More harmonization of reporting within EMR studies is needed, particularly standardized reporting of algorithm accuracy metrics like positive predictive value (precision) and sensitivity (recall)

Cohort profile : early pandemic evaluation and enhanced surveillance of COVID-19 (EAVE II) database

Funding: The original EAVE project was funded by the National Institute for Health Research Health Technology Assessment Programme (project number 13/34/14). EAVE II is funded by the Medical Research Council [MR/R008345/1] and supported by the Scottish Government. This work is supported by BREATHE - The Health Data Research Hub for Respiratory Health [MC_PC_19004]. BREATHE is funded through the UK Research and Innovation Industrial Strategy Challenge Fund and delivered through Health Data Research UK.PostprintPeer reviewe